Petroleum and Gas Engineering

Topic Information

Dear Colleagues,

Petroleum and gas engineering is an engineering technology field that uses scientific theories, methods, technologies, and equipment to efficiently drill underground oil and gas resources, maximally and economically exploit oil and gas in the formation to the ground, and safely separate, measure, and transport oil and gas. As an important part of energy in human society, oil and natural gas play an extremely important role in the development of the world economy, human social life, and civilization due to their irreplaceable and non-renewable nature. Due to the deep reservoir burial, low permeability, and ultra-low permeability in physical properties, heavy oil and super heavy oil in oil products, high pressure and high temperature, formation heterogeneity, and difficulty in wellbore formation of oil and gas, it is very difficult to drill and achieve further development.

This Topic aims to bring together relevant researchers from industry and academia to share their latest discoveries and developments in the fields of oil and gas engineering. The topics of interest include but are not limited to the following:

1. Oil and gas field development plan and production technology;
2. Oil and gas well fluid mechanics, rock mechanics, and oilfield chemistry technology;
3. Theory and method of reservoir description and development geological modeling;
4. Percolation theory and reservoir numerical simulation;
5. Theory and method of oil and gas field development;
6. Theory and technology of enhanced oil recovery;
7. Multiphase pipeline flow and oil–gas field gathering and transportation and oil-gas treatment technology.

Prof. Dr. Xiaochun Wang
Prof. Dr. Yulong Zhao
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.3	2011	17.8 Days	CHF 2400
Energies energies	3.0	6.2	2008	17.5 Days	CHF 2600
Fractal and Fractional fractalfract	3.6	4.6	2017	20.9 Days	CHF 2700
Polymers polymers	4.7	8.0	2009	14.5 Days	CHF 2700
Resources resources	3.6	7.2	2012	33.4 Days	CHF 1600

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Published Papers (32 papers)

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23 pages, 4521 KiB  

Open AccessArticle

Hybrid Machine-Learning Model for Accurate Prediction of Filtration Volume in Water-Based Drilling Fluids

by Shadfar Davoodi, Mohammed Al-Rubaii, David A. Wood, Mohammed Al-Shargabi, Mohammad Mehrad and Valeriy S. Rukavishnikov

Appl. Sci. 2024, 14(19), 9035; https://doi.org/10.3390/app14199035 (registering DOI) - 7 Oct 2024

Abstract

Accurately predicting the filtration volume (FV) in drilling fluid (DF) is crucial for avoiding drilling problems such as a stuck pipe and minimizing DF impacts on formations during drilling. Traditional FV measurement relies on human-centric experimental evaluation, which is time-consuming. Recently, machine learning [...] Read more.

Accurately predicting the filtration volume (FV) in drilling fluid (DF) is crucial for avoiding drilling problems such as a stuck pipe and minimizing DF impacts on formations during drilling. Traditional FV measurement relies on human-centric experimental evaluation, which is time-consuming. Recently, machine learning (ML) proved itself as a promising approach for FV prediction. However, existing ML methods require time-consuming input variables, hindering the semi-real-time monitoring of the FV. Therefore, employing radial basis function neural network (RBFNN) and multilayer extreme learning machine (MELM) algorithms integrated with the growth optimizer (GO), predictive hybrid ML (HML) models are developed to reliably predict the FV using only two easy-to-measure input variables: drilling fluid density (FD) and Marsh funnel viscosity (MFV). A 1260-record dataset from seventeen wells drilled in two oil and gas fields (Iran) was used to evaluate the models. Results showed the superior performance of the RBFNN-GO model, achieving a root-mean-square error (RMSE) of 0.6396 mL. Overfitting index (OFI), score, dependency, and Shapley additive explanations (SHAP) analysis confirmed the superior FV prediction performance of the RBFNN-GO model. In addition, the low RMSE (0.3227 mL) of the RBFNN-NGO model on unseen data from a different well within the studied fields confirmed the strong generalizability of this rapid and novel FV prediction method. Full article

(This article belongs to the Topic Petroleum and Gas Engineering)

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Figure 1
<p>Flowchart of the procedure applied for developing simple and hybrid ML models to predict FV in DF from two easily measured input variables, FD and MFV.</p> Full article ">Figure 2
<p>Applying the GO algorithm to determine optimal hyperparameter values for the RBFNN predictor.</p> Full article ">Figure 3
<p>Flowchart illustrating the implementation of the MELM-GO hybrid ML model designed for predicting the FV properties of DFs.</p> Full article ">Figure 4
<p>Heatmap correlation matrix displaying the relationships between independent and dependent parameters for the compiled dataset.</p> Full article ">Figure 5
<p>RMSE results for the FV dataset evaluated with the RBFNN model with three training/testing subset separation ratios.</p> Full article ">Figure 6
<p>Identification of outliers in the FV training dataset using GPR–Mahalanobis distance (MD) modeling.</p> Full article ">Figure 7
<p>The iterative reduction of RMSE within various iterations of the GO algorithm is employed to ascertain the optimal structure of the MELM algorithm in the prediction of the FV.</p> Full article ">Figure 8
<p>Comparative cross-plots evaluating accuracy of measured and predicted FV values utilizing (<b>a</b>) MELM, (<b>b</b>) MELM-GO, (<b>c</b>) RBFNN, and (<b>d</b>) RBFNN-GO models on training data.</p> Full article ">Figure 8 Cont.
<p>Comparative cross-plots evaluating accuracy of measured and predicted FV values utilizing (<b>a</b>) MELM, (<b>b</b>) MELM-GO, (<b>c</b>) RBFNN, and (<b>d</b>) RBFNN-GO models on training data.</p> Full article ">Figure 9
<p>Assessment of the error convergence of GO algorithm iteration sequences for the two HML models configured to predict the FV.</p> Full article ">Figure 10
<p>Comparative cross-plot accuracy evaluations of measured and predicted FV values achieved by the trained (<b>a</b>) MELM, (<b>b</b>) MELM-GO, (<b>c</b>) RBFNN, and (<b>d</b>) RBFNN-GO models applied to the testing data subset.</p> Full article ">Figure 11
<p>Radar chart contrasting prediction scores achieved by standalone ML and HML models.</p> Full article ">Figure 12
<p>Relationships between the percentage of added noise to the input variable distributions and R² values for FV predictions for the ML and HML models.</p> Full article ">Figure 13
<p>Visualizing the effect of the two input features on the FV predictions with SHAP values for the RBFNN-GO model applied to the training subset: (<b>a</b>) SHAP detailed feature impact plot and (<b>b</b>) SHAP summary plot of the feature importance.</p> Full article ">Figure 14
<p>The (<b>a</b>) 3D and (<b>b</b>) 2D heat map partial dependence plots showcasing the interplay between pairs of input features in the predictions of the FV as generated by the RBFNN-GO model applied to the training subset.</p> Full article ">Figure 15
<p>Comparison of the measured FV values with those predicted by the RBFNN-GO model for the unseen data.</p> Full article ">Figure 16
<p>Workflow diagram demonstrating how the configured HML models can be applied in the DF well-site laboratory to assist the drilling crew with FV semi-real-time monitoring and decision making.</p> Full article ">

7 pages, 193 KiB  

Open AccessEditorial

Recent Advancements in Petroleum and Gas Engineering

by Xiaochuan Wang, Gan Feng, Yaoqing Hu, Liuke Huang, Hongqiang Xie, Yu Zhao, Peihua Jin and Chao Liang

Energies 2024, 17(18), 4664; https://doi.org/10.3390/en17184664 - 19 Sep 2024

Viewed by 390

Abstract

Oil and natural gas resources are crucial energy sources formed during the geological and biological evolution of the Earth [...] Full article

(This article belongs to the Topic Petroleum and Gas Engineering)

14 pages, 6440 KiB  

Open AccessArticle

Feasibility of Identifying Shale Sweet Spots by Downhole Microseismic Imaging

by Congcong Yuan and Jie Zhang

Appl. Sci. 2024, 14(17), 8056; https://doi.org/10.3390/app14178056 - 9 Sep 2024

Viewed by 362

Abstract

Several studies suggest that shale sweet spots are likely associated with a low Poisson’s ratio in the shale layer. Compared with conventional geophysical techniques with active seismic data, it is straightforward and cost-effective to delineate the distribution of 3D Poisson’s ratios using microseismic [...] Read more.

Several studies suggest that shale sweet spots are likely associated with a low Poisson’s ratio in the shale layer. Compared with conventional geophysical techniques with active seismic data, it is straightforward and cost-effective to delineate the distribution of 3D Poisson’s ratios using microseismic data. In this study, an alternating method is proposed to determine microseismic event locations, 3D P-wave velocity, and Poisson’s ratio models with data recorded from downhole monitoring arrays. The method combines the improved 3D traveltime tomography, which inverts P and S arrivals for 3D P-wave velocity and Poisson’s ratio structures simultaneously, and a 3D grid search approach for event locations in an iterative fashion. The traveltime tomography directly inverts the Poisson’s ratio structure instead of calculating the Poisson’s ratios from P- and S-wave velocities (i.e., Vp and Vs) that are inverted by conventional traveltime tomography separately. The synthetic results and analysis suggest that the proposed method recovers the true Poisson’s ratio model reasonably. Additionally, we apply the method to a field dataset, which indicates that it may help delineate the reservoir structure and identify potential shale sweet spots. Full article

(This article belongs to the Topic Petroleum and Gas Engineering)

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<p>The survey geometry and event location results in the synthetic case. The black reverse triangles denote two receiver arrays installed in two wells. The red and blue dots are the true and searched event locations, respectively. (<b>a</b>) 3D view; (<b>b</b>) X-Y plan view; (<b>c</b>) X-Z plan view; and (<b>d</b>) Y-Z plan view.</p> Full article ">Figure 2
<p>True, initial, and inverted P-wave velocity models in the synthetic case. (<b>a</b>,<b>d</b>,<b>g</b>) Plan view (Y = 1.0 Km), (<b>b</b>,<b>e</b>,<b>h</b>) plan view (X = 0.86 Km), and (<b>c</b>,<b>f</b>,<b>i</b>) cross-sectional view (X = 0.86 Km, Y = 1.0 Km, and Z = 1.9 Km). (<b>a</b>–<b>c</b>) Map views of true 3D P-wave velocity model. (<b>d</b>–<b>f</b>) Map views of initial 3D P-wave velocity model. (<b>g</b>–<b>i</b>) Map views of the inverted 3D P-wave velocity result. The black rectangle denotes the border of the embedded anomaly in the true model.</p> Full article ">Figure 3
<p>True, initial, and inverted Poisson’s ratio models in the synthetic case. (<b>a</b>,<b>d</b>,<b>g</b>) Plan view (Y = 1.0 Km), (<b>b</b>,<b>e</b>,<b>h</b>) plan view (X = 0.86 Km), and (<b>c</b>,<b>f</b>,<b>i</b>) cross-sectional view (X = 0.86 Km, Y = 1.0 Km, and Z = 1.9 Km). (<b>a</b>–<b>c</b>) Map views of true 3D Poisson’s ratio model. (<b>d</b>–<b>f</b>) Map views of initial 3D Poisson’s ratio model. (<b>g</b>–<b>i</b>) Map views of the inverted 3D Poisson’s ratio result. The black rectangle denotes the border of the embedded anomaly in the true model.</p> Full article ">Figure 4
<p>The Vp (<b>a</b>) and Poisson’s ratio (<b>b</b>) images extracted along two horizontal directions at a depth of 1.88 km of tomographic models. The green and red lines represent the images of true and inverted Vp or Poisson’s ratio models with our traveltime tomographic methods, the blue lines are the images of the Vp and derived Poisson’s ratio models from Vp and Vs models inverted separately using standard traveltime tomography.</p> Full article ">Figure 5
<p>(<b>a</b>) Convergence curve of location error of 3D grid search over three loops in the synthetic case. (<b>b</b>) Misfit curves of the traveltime tomography of the last loop. The circle represents the data misfit of each loop, and the triangles and squares are the traveltime misfits over each iteration of P and S waves, respectively.</p> Full article ">Figure 6
<p>The different recordings of the same microseismic event from (<b>a</b>) eight-receiver array and (<b>b</b>) twelve-receiver array, respectively, in one field case. The P- and S-wave arrivals display a high signal-to-noise ratio and are easily picked (red and blue, respectively) on all 20 receivers.</p> Full article ">Figure 7
<p>The survey geometry and event location results of the field case. The black reverse triangles stand for two receiver arrays installed in the two monitoring wells. The seven red stars are the perforations in one stage. The blue dots are our searched location results, and the green dots are the location results provided by service company. (<b>a</b>) The 3D view; (<b>b</b>) X-Y plan view; (<b>c</b>) X-Z plan view; and (<b>d</b>) Y-Z plan view.</p> Full article ">Figure 8
<p>Initial and inverted P-wave velocity models in the field case. (<b>a</b>,<b>d</b>) Plan view (Y = 1.0 Km), (<b>b</b>,<b>e</b>) plan view (X = 0.86 Km), and (<b>c</b>,<b>f</b>) cross-sectional view (X = 0.86 Km, Y = 1.0 Km, and Z = 1.9 Km). (<b>a</b>–<b>c</b>) Map views of initial 3D P-wave velocity model. (<b>d</b>–<b>f</b>) Map views of the inverted 3D P-wave velocity result.</p> Full article ">Figure 9
<p>Initial and inverted Poisson’s ratio models in the field case. (<b>a</b>,<b>d</b>) Plan view (Y = 1.0 Km), (<b>b</b>,<b>e</b>) plan view (X = 0.86 Km), and (<b>c</b>,<b>f</b>) cross-sectional view (X = 0.86 Km, Y = 1.0 Km, and Z = 1.9 Km). (<b>a</b>–<b>c</b>) Map views of initial 3D Poisson’s ratio model. (<b>d</b>–<b>f</b>) Map views of the inverted 3D Poisson’s ratio result.</p> Full article ">Figure 10
<p>(<b>a</b>) Convergence curve of location error of 3D grid search over three loops in the field case. (<b>b</b>) Misfit curves of the traveltime tomography of the last loop. The circle represents the data misfit of each loop, and the triangle and square are the traveltime misfits over each iteration of P and S waves, respectively.</p> Full article ">

21 pages, 6616 KiB  

Open AccessArticle

Logging Lithology Discrimination with Enhanced Sampling Methods for Imbalance Sample Conditions

by Jingyue Liu, Fei Tian, Aosai Zhao, Wenhao Zheng and Wenjing Cao

Appl. Sci. 2024, 14(15), 6534; https://doi.org/10.3390/app14156534 - 26 Jul 2024

Cited by 1 | Viewed by 566

Abstract

In the process of lithology discrimination from a conventional well logging dataset, the imbalance in sample distribution restricts the accuracy of log identification, especially in the fine-scale reservoir intervals. Enhanced sampling balances the distribution of well logging samples of multiple lithologies, which is [...] Read more.

In the process of lithology discrimination from a conventional well logging dataset, the imbalance in sample distribution restricts the accuracy of log identification, especially in the fine-scale reservoir intervals. Enhanced sampling balances the distribution of well logging samples of multiple lithologies, which is of great significance to precise fine-scale reservoir characterization. This study employed data over-sampling and under-sampling algorithms represented by the synthetic minority over-sampling technique (SMOTE), adaptive synthetic sampling (ADASYN), and edited nearest neighbors (ENN) to process well logging dataset. To achieve automatic and precise lithology discrimination on enhanced sampled well logging dataset, support vector machine (SVM), random forest (RF), and gradient boosting decision tree (GBDT) models were trained using cross-validation and grid search methods. Aimed to objectively evaluate the performance of different models on different sampling results from multiple perspectives, the lithology discrimination results were evaluated and compared based on the Jaccard index and F1 score. By comparing the predictions of eighteen lithology discrimination workflows, a new discrimination process containing ADASYN, ENN, and RF has the most precise lithology discrimination result. This process improves the discrimination accuracy of fine-scale reservoir interval lithology, has great generalization ability, and is feasible in a variety of different geological environments. Full article

(This article belongs to the Topic Petroleum and Gas Engineering)

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Figure 1
<p>Schematic diagram of logging lithology discrimination with enhanced sampling methods.</p> Full article ">Figure 2
<p>The work flow of SMOTE algorithm.</p> Full article ">Figure 3
<p>The work flow of ADASYN algorithm.</p> Full article ">Figure 4
<p>The work flow of ENN algorithm.</p> Full article ">Figure 5
<p>Schematic diagram of candidate classifiers.</p> Full article ">Figure 6
<p>The number of samples before and after data balancing processing.</p> Full article ">Figure 7
<p>Principal component frequency densities.</p> Full article ">Figure 8
<p>The results of parameter tuning for SVM, RF, and GBDT models.</p> Full article ">Figure 9
<p>Model testing scores.</p> Full article ">Figure 10
<p>Box plots of the evaluation metrics for models on datasets processed with different data-balancing methods.</p> Full article ">Figure 11
<p>Visualization of lithology discrimination results of RF.</p> Full article ">

25 pages, 20201 KiB  

Open AccessArticle

Modeling and Parameter Optimization of Multi-Step Horizontal Salt Cavern Considering Heat Transfer for Energy Storage

by Jinchao Wang, Zhiming Wang, Quanshu Zeng, Jun Wang and Binwang Li

Appl. Sci. 2024, 14(15), 6433; https://doi.org/10.3390/app14156433 - 24 Jul 2024

Viewed by 656

Abstract

Horizontal salt caverns represent a prime choice for energy storage within bedded salt formations. Constructing multi-step horizontal salt caverns involves intricate fluid and chemical dynamics, including salt boundary dissolution, cavern development, brine flow, heat transfer, and species transportation. In this paper, the influence [...] Read more.

Horizontal salt caverns represent a prime choice for energy storage within bedded salt formations. Constructing multi-step horizontal salt caverns involves intricate fluid and chemical dynamics, including salt boundary dissolution, cavern development, brine flow, heat transfer, and species transportation. In this paper, the influence of heat transfer and turbulent flow is considered in developing a 3D multi-physics coupled flow model for the construction of multi-step horizontal salt caverns. The feasibility and accuracy of the model are verified by comparisons with the field data of the Volgograd horizontal salt cavern. The effects of turbulent flow and heat transfer on the dissolution process are thoroughly analyzed. By analyzing the characteristics of the flow field, the brine concentration distribution, and cavern expansion, the results indicate a steady rise in cavity brine concentrations throughout the leaching phases, with the previously formed cavities continuing to enlarge during subsequent leaching stages, albeit at a diminishing rate of expansion. Furthermore, the results reveal that a larger injection flow rate results in a larger cavern volume, whereas higher injection concentrations result in smaller cavern volumes. While the step distance has a minimal impact on cavern volume, identifying the optimal step distance remains crucial. This analysis of construction parameters aims to provide valuable insights into the design and engineering practices involved in developing multi-step horizontal salt caverns for energy storage purposes. Full article

(This article belongs to the Topic Petroleum and Gas Engineering)

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Figure 1
<p>Schematic diagram of the consumption of natural gas in China.</p> Full article ">Figure 2
<p>Schematic diagram of a multi-step horizontal salt cavern.</p> Full article ">Figure 3
<p>A diagram of thermal convection and thermal conduction during solution mining. (The yellow arrows represent thermal conduction, and the blue arrows represent thermal convection.)</p> Full article ">Figure 4
<p>Schematic diagram of geometry and meshing results.</p> Full article ">Figure 5
<p>The process of the adaptive meshing in the calculation.</p> Full article ">Figure 6
<p>Flow chart of the simulation of the coupled model.</p> Full article ">Figure 7
<p>Data comparison of outlet brine concentration evolutions with leaching time.</p> Full article ">Figure 8
<p>Data comparison of cavern volume evolutions with leaching time.</p> Full article ">Figure 9
<p>Comparison of cavern side shapes [<a href="#B46-applsci-14-06433" class="html-bibr">46</a>].</p> Full article ">Figure 10
<p>Data comparison of concentration and cavern volume evolutions with leaching time.</p> Full article ">Figure 11
<p>Diagram of the thermal convection direction and fluid flow direction of brine in the cavity. (The blue arrows represent thermal convection, and the red arrows represent fluid flow.)</p> Full article ">Figure 12
<p>Diagram of the thermal convection direction and the concentration contour of brine in the cavity. (The blue arrows represent thermal convection, and the curves represent the concentration contour line).</p> Full article ">Figure 13
<p>The cavern shape under different temperatures after 10 days of leaching.</p> Full article ">Figure 14
<p>Contours of velocity magnitude at vertical section (X = 0 m) during first stage. (The red arrow line represents the streamline of brine).</p> Full article ">Figure 15
<p>Contours of velocity magnitude at vertical section (X = 0 m) during second stage (The red arrow line represents the streamline of the brine).</p> Full article ">Figure 16
<p>Contours of velocity magnitude at vertical section (X = 0 m) of each stage (The red arrow line represents the streamline of the brine).</p> Full article ">Figure 17
<p>Contours of brine displacing concentration at vertical section (X = 0 m) during first stage (rainbow lines represent flow velocity lines).</p> Full article ">Figure 18
<p>Contours of brine displacing concentration at vertical section (X = 0 m) during second stage (rainbow lines represent flow velocity lines).</p> Full article ">Figure 19
<p>Contours of brine displacing concentration at vertical section (X = 0 m) of each stage (black lines represent brine concentration contour lines).</p> Full article ">Figure 20
<p>Cavity shapes at vertical section (X = 0 m) of each stage.</p> Full article ">Figure 21
<p>Cavity shapes at different cross sections of each stage.</p> Full article ">Figure 22
<p>Outlet brine concentration and cavern volume evolutions with leaching time.</p> Full article ">Figure 23
<p>Outlet brine concentration evolution with leaching time under different flow rates.</p> Full article ">Figure 24
<p>Cavern volume evolution with leaching time under different flow rates.</p> Full article ">Figure 25
<p>First-stage cavern shapes in vertical section (X = 0 m) under different flow rates.</p> Full article ">Figure 26
<p>Cavity’s final shapes in vertical section (X = 0 m) under different flow rates.</p> Full article ">Figure 27
<p>Outlet brine concentration evolution with leaching time under different injection concentrations.</p> Full article ">Figure 28
<p>Cavern volume evolution with leaching time under different injection concentrations.</p> Full article ">Figure 29
<p>First-stage cavern shapes in vertical section (X = 0 m) under different injection concentrations.</p> Full article ">Figure 30
<p>Cavity’s final shapes in vertical section (X = 0 m) under different injection concentrations.</p> Full article ">Figure 31
<p>Outlet brine concentration evolution with leaching time under different injection intervals.</p> Full article ">Figure 32
<p>Cavern volume evolution with leaching time under different injection intervals.</p> Full article ">Figure 33
<p>First stage cavern shapes in vertical section (X = 0 m) under different injection intervals.</p> Full article ">Figure 34
<p>Cavity’s final shapes in vertical section (X = 0 m) under different injection intervals.</p> Full article ">

11 pages, 17344 KiB  

Open AccessArticle

Mechanical Performance of Bentonite Plugs in Abandonment Operations of Petroleum Wells

by Laura Rafaela Cavalcanti de Oliveira, Mário César de Siqueira Lima, Waleska Rodrigues Pontes da Costa, Ruth Luna do Nascimento Gonçalves, Anna Carolina Amorim Costa, Karine Castro Nóbrega, Elessandre Alves de Souza and Luciana Viana Amorim

Resources 2024, 13(8), 103; https://doi.org/10.3390/resources13080103 - 23 Jul 2024

Viewed by 711

Abstract

This study aims to evaluate how the operational procedure adopted for pellet placement and the exposure to subsurface conditions influence the mechanical integrity of bentonite plugs used as barrier elements in the abandonment of petroleum wells. To this end, the plugs were formed [...] Read more.

This study aims to evaluate how the operational procedure adopted for pellet placement and the exposure to subsurface conditions influence the mechanical integrity of bentonite plugs used as barrier elements in the abandonment of petroleum wells. To this end, the plugs were formed by hydrating the pellets directly in water, simulating the onshore procedure, while the offshore plugs were obtained from pellets hydrated in deionized water after immersion in diesel or olefin, which are suggested as displacement fluids. The plugs obtained were tested by compression and adhesion tests. These mechanical tests were also carried out for specimens obtained from plugs exposed to four formulations of synthetic formation waters. The results obtained demonstrated that, in the offshore procedure, the previous contact with olefin may adversely affects the mechanical stability of bentonite plugs, while plugs formed from pellets immersed in diesel presented satisfactory mechanical properties. However, the contact with formation water evidenced that the onshore plug presents superior resistance than the offshore plug previously immersed in diesel. The highly successful performance of the onshore plug was attested by the maintenance of the compressive strength, which exhibited a maximum reduction of 13%, even after exposure to the most saline formation waters. Full article

(This article belongs to the Topic Petroleum and Gas Engineering)

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<p>Aspect of the plug formed by pellets previously immersed in diesel (<b>a</b>) and olefin (<b>b</b>), following the methodology proposed for offshore wells.</p> Full article ">Figure 2
<p>Aspect of the plug formed by pellets hydrated directly in water, following the methodology proposed for onshore wells.</p> Full article ">Figure 3
<p>Compressive strength (<b>a</b>) and adhesion (<b>b</b>) for plugs obtained from the proposed methodology for offshore and onshore wells.</p> Full article ">Figure 4
<p>Offshore plugs (diesel) after contact with synthetic formation waters.</p> Full article ">Figure 5
<p>Formation waters and base of the plugs onshore before immersion.</p> Full article ">Figure 6
<p>Formation waters and base of the plugs onshore after immersion.</p> Full article ">Figure 7
<p>Appearance of onshore plugs after immersion in each formation water.</p> Full article ">Figure 8
<p>Opening between the plates of the metal mold due to the swelling of the plug immersed in FW3.</p> Full article ">Figure 9
<p>Compressive strength (<b>a</b>) and adhesion (<b>b</b>) for the onshore plug before and after immersion in formation waters.</p> Full article ">

21 pages, 6153 KiB  

Open AccessArticle

Permeability Evolution of Shale during High-Ionic-Strength Water Sequential Imbibition

by Tianhao Bai, Sam Hashemi, Noune Melkoumian, Alexander Badalyan and Abbas Zeinijahromi

Energies 2024, 17(14), 3598; https://doi.org/10.3390/en17143598 - 22 Jul 2024

Cited by 1 | Viewed by 810

Abstract

It is widely accepted in the oil and gas industry that high-ionic-strength water (HISW) can improve oil and gas recovery in unconventional shale reservoirs by limiting shale hydration. Despite numerous supporting studies, there is a lack of a systematic analysis exploring the effect [...] Read more.

It is widely accepted in the oil and gas industry that high-ionic-strength water (HISW) can improve oil and gas recovery in unconventional shale reservoirs by limiting shale hydration. Despite numerous supporting studies, there is a lack of a systematic analysis exploring the effect of HISW on shale permeability evolution, particularly considering varying chemical compositions. In this work, we investigated the impact of different concentrations of NaCl and CaCl₂ on shale permeability through sequential HISW imbibition experiments, beginning with the highest NaCl and lowest CaCl₂ concentrations. After maintaining the highest effective stress for an extended period, significant permeability reduction and potential fracture generation were observed, as indicated by periodic fluctuations in differential pressure. These effects were further intensified by displacements with HISW solutions. Advanced post-experimental analyses using micro-CT scans and SEM-EDS analysis revealed microstructural changes within the sample. Our findings offer initial insight into how HISW-shale interactions influence shale permeability, using innovative approaches to simulate reservoir conditions. The findings indicate that discrepancies in the chemical composition between injected solutions and shale may lead to shale disintegration during hydraulic fracturing processes. Full article

(This article belongs to the Topic Petroleum and Gas Engineering)

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Figure 1
<p>Schematic demonstration of the fracture roughness measurement.</p> Full article ">Figure 2
<p>The schematic layout of the core-flooding laboratory setup used for nitrogen and HISW flooding experiments [<a href="#B35-energies-17-03598" class="html-bibr">35</a>].</p> Full article ">Figure 3
<p>Visualization of parameters Sz, Sp, and Sv [<a href="#B46-energies-17-03598" class="html-bibr">46</a>].</p> Full article ">Figure 4
<p>The permeability ratio was adjusted during the first, third, and fourth loading phases. Permeability at the effective stress of 3000 psi during the Solution #1 imbibition was measured at the 24th hour [<a href="#B35-energies-17-03598" class="html-bibr">35</a>].</p> Full article ">Figure 5
<p>Recorded upstream pressure, downstream pressure, and differential pressure during Solution #1 injection. The downstream end connected to the back pressure regulator was set at 435 psi. The confining pressure was adjusted to maintain a constant effective stress of 3000 psi.</p> Full article ">Figure 6
<p>Recorded upstream pressure, downstream pressure, and differential pressure during Solution #2 and Solution #3 injections. The downstream end connected to the back pressure regulator was set at 300 psi and 130 psi, respectively.</p> Full article ">Figure 7
<p>(<b>a</b>) Dissolved calcite was detected by SEM; (<b>b</b>) Under the compaction of overlying strata, the flaky platelets of illite were bent, contributing to shale plastic deformation; (<b>c</b>) A quartz grain detached from the artificial fracture surface; (<b>d</b>) Propagation of a bedding lamina cemented by calcite.</p> Full article ">Figure 8
<p>(<b>a</b>) Picture of the core outlet surface before experiments; (<b>b</b>) Picture of the core outlet surface after the experiments, with the induced fractures highlighted by the red circles; (<b>c</b>) Post-experimental CT scan image of the generated fractures within the core.</p> Full article ">Figure 9
<p>Artificial fracture surfaces (<b>a</b>) before and (<b>b</b>) after experiments, with the breakage highlighted by the red circle.</p> Full article ">Figure 10
<p>3D reconstruction of the shale core at different angles from post-experimental CT scan.</p> Full article ">

25 pages, 47369 KiB  

Open AccessReview

Current Status and Development Trend of Research on Polymer-Based Kinetic Inhibitors for Natural Gas Hydrates

by Shujie Liu, Sunan Wang, Jiansheng Luo, Yilong Xu, Liangliang Ren, Xiong Xiang, Tie Geng, Botao Xu and Lei Guo

Polymers 2024, 16(14), 1985; https://doi.org/10.3390/polym16141985 - 11 Jul 2024

Viewed by 848

Abstract

As the understanding of natural gas hydrates as a vast potential resource deepens, their importance as a future clean energy source becomes increasingly evident. However, natural gas hydrates trend towards secondary generation during extraction and transportation, leading to safety issues such as pipeline [...] Read more.

As the understanding of natural gas hydrates as a vast potential resource deepens, their importance as a future clean energy source becomes increasingly evident. However, natural gas hydrates trend towards secondary generation during extraction and transportation, leading to safety issues such as pipeline blockages. Consequently, developing new and efficient natural gas hydrate inhibitors has become a focal point in hydrate research. Kinetic hydrate inhibitors (KHIs) offer an effective solution by disrupting the nucleation and growth processes of hydrates without altering their thermodynamic equilibrium conditions. This paper systematically reviews the latest research progress and development trends in KHIs for natural gas hydrates, covering their development history, classification, and inhibition mechanisms. It particularly focuses on the chemical properties, inhibition effects, and mechanisms of polymer inhibitors such as polyvinylpyrrolidone (PVP) and polyvinylcaprolactam (PVCap). Studies indicate that these polymer inhibitors provide an economical and efficient solution due to their low dosage and environmental friendliness. Additionally, this paper explores the environmental impact and biodegradability of these inhibitors, offering guidance for future research, including the development, optimization, and environmental assessment of new inhibitors. Through a comprehensive analysis of existing research, this work aims to provide a theoretical foundation and technical reference for the commercial development of natural gas hydrates, promoting their safe and efficient use as a clean energy resource. Full article

(This article belongs to the Topic Petroleum and Gas Engineering)

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Figure 1
<p>Cages and three common crystal structures of natural gas hydrates (NGHs) [<a href="#B3-polymers-16-01985" class="html-bibr">3</a>,<a href="#B11-polymers-16-01985" class="html-bibr">11</a>].</p> Full article ">Figure 2
<p>Schematic diagram of ambient temperature change in deep water.</p> Full article ">Figure 3
<p>Schematic diagram of secondary hydrate generation in the wellbore during the deepwater drilling process.</p> Full article ">Figure 4
<p>History of kinetic hydrate inhibitor research.</p> Full article ">Figure 5
<p>Second-generation kinetic inhibitor structure: (<b>a</b>) VC-713, (<b>b</b>) PVCap, (<b>c</b>) Poly(VP-VC), and (<b>d</b>) VIMA-VCap.</p> Full article ">Figure 6
<p>Second-generation kinetic inhibitor structures: (<b>a</b>) polydiethylacrylamide, (<b>b</b>) polyisopropylacrylamide, (<b>c</b>) polymaleimide, and (<b>d</b>) polyisopropylmethacrylamide.</p> Full article ">Figure 7
<p>VIMA-IPMA structure.</p> Full article ">Figure 8
<p>Third-generation kinetic hydrate inhibitor structures: (<b>a</b>) polyvinyl alcohol i, (<b>b</b>) polyvinyl alcohol ii, (<b>c</b>) polyvinyl alcohol III, (<b>d</b>) PEO, (<b>e</b>) HEC, and (<b>f</b>) HECE.</p> Full article ">Figure 9
<p>Third-generation kinetic hydrate inhibitor structures: (<b>a</b>) polyalkylamine oxides and (<b>b</b>) TBAPS.</p> Full article ">Figure 10
<p>Category of kinetic inhibitors.</p> Full article ">Figure 11
<p>Schematic diagram of perturbation suppression mechanism [<a href="#B102-polymers-16-01985" class="html-bibr">102</a>].</p> Full article ">Figure 12
<p>Conceptual model for inhibitor binding and crystal growth inhibition [<a href="#B96-polymers-16-01985" class="html-bibr">96</a>].</p> Full article ">Figure 13
<p>Schematic diagram of layer mass transfer obstruction mechanism [<a href="#B96-polymers-16-01985" class="html-bibr">96</a>,<a href="#B152-polymers-16-01985" class="html-bibr">152</a>].</p> Full article ">Figure 14
<p>Schematic diagram of adsorption and spatial obstruction mechanism [<a href="#B151-polymers-16-01985" class="html-bibr">151</a>,<a href="#B152-polymers-16-01985" class="html-bibr">152</a>].</p> Full article ">Figure 15
<p>Surface contact geometry of particle–particle adhesion and magnified liquid bridge [<a href="#B179-polymers-16-01985" class="html-bibr">179</a>].</p> Full article ">

12 pages, 3022 KiB  

Open AccessArticle

Hydraulic Expansion Techniques for Fracture-Cavity Carbonate Rock with Field Applications

by Jiaxue Li, Wenjun Lu and Jie Sun

Appl. Sci. 2024, 14(13), 5851; https://doi.org/10.3390/app14135851 - 4 Jul 2024

Cited by 1 | Viewed by 766

Abstract

Fracture-cavity carbonate reservoirs provide a large area, fracture development, high productivity, long stable production time, and other characteristics. However, after long-term exploitation, the lack of energy in the formation leads to a rapid decrease in production, and the water content in crude oil [...] Read more.

Fracture-cavity carbonate reservoirs provide a large area, fracture development, high productivity, long stable production time, and other characteristics. However, after long-term exploitation, the lack of energy in the formation leads to a rapid decrease in production, and the water content in crude oil steadily increases, thereby disrupting normal production. To recover normal production, it is necessary to connect the cracks and pores that have not been affected during the original production, so as to allow the crude oil inside to enter the production cracks and replenish energy through methods such as hydraulic expansion of fracture-cavity carbonate rock. Accordingly, we propose hydraulic expansion techniques based on the following four processes for implementation: (1) applying high pressure to prevent a nearby fracture network from opening the seam, (2) connecting a distant fracture-cavity body, (3) breaking through the clay filling section of a natural fracture network, and (4) constructing an injection production well pattern for accelerating injection and producing diversion. Hydraulic fracturing involves closing or partially closing the original high-permeability channels, which usually produce a large amount of water, while opening previously unaffected areas through high pressure to increase crude oil production. We also introduce two composite techniques: (1) temporary plugging of the main deep fractures, followed by hydraulic expansion; and (2) capacity expansion and acidification/pressure processes. Hydraulic expansion allowed us to recover and supplement the formation energy and efficiently increase production. We tested various wells, achieving an effective hydraulic expansion rate of up to 85%. In addition, the productivity of conventional water injection and hydraulic expansion after on-site construction was compared for one well, clearly indicating the effectiveness of water injection and the remarkable crude oil increase after hydraulic expansion. Full article

(This article belongs to the Topic Petroleum and Gas Engineering)

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Figure 1

Figure 1
<p>Section of a fracture-cavity carbonate reservoir.</p> Full article ">Figure 2
<p>Diagram of production-increasing techniques.</p> Full article ">Figure 3
<p>Construction curve of expansion well 1 under high-pressure hydraulic expansion.</p> Full article ">Figure 4
<p>Production and pressure before and after hydraulic expansion of expansion well 2 and adjacent wells.</p> Full article ">Figure 5
<p>Construction curve of expansion well 3 under hydraulic expansion.</p> Full article ">Figure 6
<p>Production and water content before and after capacity expansion of expansion well 3.</p> Full article ">Figure 7
<p>Production and water content during acidification and hydraulic expansion of expansion well 4.</p> Full article ">Figure 8
<p>Pressure during first round of water injection in expansion well 5.</p> Full article ">

15 pages, 3492 KiB  

Open AccessArticle

Real-Time Method and Implementation of Head-Wave Extraction for Ultrasonic Imaging While Drilling

by Liangchen Zhang, Junqiang Lu, Jinping Wu, Baiyong Men, Chao Xie, Yanbo Zong, Shubo Yang and Weining Ni

Appl. Sci. 2024, 14(12), 5292; https://doi.org/10.3390/app14125292 - 19 Jun 2024

Viewed by 563

Abstract

Extracting head waves and subsequently uploading their results from the downhole to the surface system in real time could improve the real-time guidance of ultrasonic imaging logging while drilling (UILWD) for drilling operations. To realize the downhole real-time extraction of head waves in [...] Read more.

Extracting head waves and subsequently uploading their results from the downhole to the surface system in real time could improve the real-time guidance of ultrasonic imaging logging while drilling (UILWD) for drilling operations. To realize the downhole real-time extraction of head waves in this logging, three aspects were explored in this study. First, an improved energy ratio head-wave arrival extraction algorithm based on the weighting coefficients and characteristic functions, along with an amplitude detection method relying on peak-to-peak values, was proposed. Second, an echo reception pre-processing analog circuit and a digital signal processing circuit based on FPGA were designed. A pipeline algorithm was developed in FPGA to extract the arrival time and amplitude of the head wave. Finally, software simulations, laboratory tests, and field experiments related to this method were conducted. Our results showed that the real-time head-wave extraction method demonstrated a strong anti-noise ability in real time. The maximum relative error of the arrival time was less than 5%. The relative error of the amplitude was acceptable, and 90% of this value was within 5%. Through the measurement, the time of processing a single-channel waveform by a downhole algorithm was less than 15 ms, thus meeting the requirements for the real-time processing of downholes. Full article

(This article belongs to the Topic Petroleum and Gas Engineering)

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Figure 1
<p>Effect comparison between the STA/LTA and improved STA/LTA methods.</p> Full article ">Figure 2
<p>Diagram of the principal hardware used for head-wave real-time extraction.</p> Full article ">Figure 3
<p>Structure diagram of the analog band-pass filter circuit.</p> Full article ">Figure 4
<p>The calculated parameters of the band-pass filter circuit.</p> Full article ">Figure 5
<p>Composition of the programmable gain amplification module.</p> Full article ">Figure 6
<p>Structure of the front-end control program.</p> Full article ">Figure 7
<p>Calculation flow of digital filtering.</p> Full article ">Figure 8
<p>Program structure of head-wave extraction.</p> Full article ">Figure 9
<p>Simulation results of head-wave extraction: (<b>a</b>) Head wave arrival detection; (<b>b</b>) Head wave amplitude detection; (<b>c</b>) Verification of simulation.</p> Full article ">Figure 10
<p>Actual image and test result of the echo reception pre-processing module. (<b>a</b>) Actual image; (<b>b</b>) Gain at different temperatures.</p> Full article ">Figure 11
<p>Test waveforms at 10 different depth points.</p> Full article ">Figure 12
<p>Verification of the downhole arrival time extraction of the head wave. (<b>a</b>) Channel 1; (<b>b</b>) Channel 2; (<b>c</b>) Channel 3.</p> Full article ">Figure 13
<p>Verification of downhole head-wave amplitude extraction. (<b>a</b>) Channel 1; (<b>b</b>) Channel 2; (<b>c</b>) Channel 3.</p> Full article ">

35 pages, 5457 KiB  

Open AccessArticle

Assessment of the Biogenic Souring in Oil Reservoirs under Secondary and Tertiary Oil Recovery

by Hakan Alkan, Felix Kögler, Gyunay Namazova, Stephan Hatscher, Wolfgang Jelinek and Mohd Amro

Energies 2024, 17(11), 2681; https://doi.org/10.3390/en17112681 - 31 May 2024

Cited by 1 | Viewed by 644

Abstract

The formation of hydrogen sulfide (H₂S) in petroleum reservoirs by anaerobic microbial activity (through sulfate-reducing microorganisms, SRMs) is called biogenic souring of reservoirs and poses a risk in the petroleum industry as the compound is extremely toxic, flammable, and corrosive, causing [...] Read more.

The formation of hydrogen sulfide (H₂S) in petroleum reservoirs by anaerobic microbial activity (through sulfate-reducing microorganisms, SRMs) is called biogenic souring of reservoirs and poses a risk in the petroleum industry as the compound is extremely toxic, flammable, and corrosive, causing devastating damage to reservoirs and associated surface facilities. In this paper, we present a workflow and the tools to assess biogenic souring from a pragmatic engineering perspective. The retention of H₂S in the reservoir due to the reactions with iron-bearing rock minerals (e.g., siderite) is shown in a theoretical approach here and supported with literature data. Cases are provided for two fields under secondary (waterflooding) and tertiary flooding with microbial enhanced oil recovery (MEOR). The use of the Monte Carlo method as a numerical modeling tool to incorporate uncertainties in the measured physical/chemical/biochemical data is demonstrated as well. A list of studies conducted with different chemicals alone or in combination with various biocides to mitigate biogenic souring provides an overview of potential inhibitors as well as possible applications. Furthermore, the results of static and dynamic inhibition tests using molybdate are presented in more detail due to its promising mitigation ability. Finally, a three-step workflow for the risk assessment of biogenic souring and its possible mitigation is presented and discussed. Full article

(This article belongs to the Topic Petroleum and Gas Engineering)

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Figure 1
<p>Schematic of the various states of sulfur due to biogenic reactions in the presence of sulfate-reducing microorganisms (SRMs). SCI: Sulfur cycle intermediates. Red arrows: Most relevant pathways in biogenic reservoir souring.</p> Full article ">Figure 2
<p>Growth and surviving envelops of the SRB in North Sea Fields (after [<a href="#B39-energies-17-02681" class="html-bibr">39</a>]).</p> Full article ">Figure 3
<p>Solubility of H₂S in pure and saline water (6 M NaCl). Data from [<a href="#B46-energies-17-02681" class="html-bibr">46</a>].</p> Full article ">Figure 4
<p>Partitioning coefficient for oil–water of the H₂S in n-hexadecane and oil (A-) brine systems of various salinity (pH = 6.0; performed using PVTSim, [<a href="#B45-energies-17-02681" class="html-bibr">45</a>]).</p> Full article ">Figure 5
<p>Effect of the pH on the partitioning coefficients (performed using PVTSim, [<a href="#B45-energies-17-02681" class="html-bibr">45</a>]).</p> Full article ">Figure 6
<p>Schematic of the biogenic souring and transport processes in an oil reservoir.</p> Full article ">Figure 7
<p>Numerical model of the North Sea reservoir studied, aerial view. Color coding indicates oil saturation (red: highest; blue: lowest).</p> Full article ">Figure 8
<p>Predicted water and H₂S rates from the production wells of the studied North Sea field. The dashed lines refer to water rates.</p> Full article ">Figure 9
<p>Measured H₂S concentrations in batch (<b>a</b>) and sand pack (<b>b</b>) experiments. In sand pack (<b>b</b>) gaseous H₂S was not detected between 1 and 30 injected PV. In (<b>c</b>) the sand pack column used for the experiments is shown (FW: Formation water; VFA: Volatile fatty acids).</p> Full article ">Figure 10
<p>Calculated H₂S profiles in produced oil and water (at reservoir conditions) in modeling MEOR study (Case 2).</p> Full article ">Figure 11
<p>H₂S distribution (mol fraction in oil) in layer 4 of the model (Case 2: MEOR) at the end of the simulation time (8th year). (<b>a</b>) No-retention case, (<b>b</b>) with very-low retention of H₂S (0.001 mg/g), (<b>c</b>) low retention case (0.01 mg/g), and (<b>d</b>) most realistic case based on the siderite content of the reservoir mineralogy (0.1 mg/g). Legend unit: mg H₂S/g rock.</p> Full article ">Figure 12
<p>Probabilistic estimation of the total H₂S production (kg) in oil production (low retention case) from the well P-1 calculated with the Monte Carlo method in an MEOR case study.</p> Full article ">Figure 13
<p>Effect of nitrate (brown, +NO³⁻, and 100 mM) and molybdate (green, +MoO₄²⁻, and 0.5 mM) on sulfate consumption (continuous lines) and H₂S formation (dashed lines), for the field studied. No-inhibition case (blue, No) is also shown as comparison. The experiments are performed as a batch, and H₂S concentrations are measured at the head space. Reproduced with permission from [<a href="#B27-energies-17-02681" class="html-bibr">27</a>].</p> Full article ">Figure 14
<p>Workflow for the assessment of biogenic reservoir souring.</p> Full article ">

16 pages, 4875 KiB  

Open AccessArticle

Viscosity Reduction Behavior of Carbon Nanotube Viscosity Reducers with Different Molecular Structures at the Oil–Water Interface: Experimental Study and Molecular Dynamics Simulation

by Zhao Hua, Jian Zhang, Yuejun Zhu, Bo Huang, Qingyuan Chen and Wanfen Pu

Energies 2024, 17(11), 2564; https://doi.org/10.3390/en17112564 - 25 May 2024

Cited by 1 | Viewed by 730

Abstract

Effectively enhancing oil recovery can be achieved by reducing the viscosity of crude oil. Therefore, this paper investigated the viscosity reduction behavior of carbon nanotube viscosity reducers with different molecular structures at the oil–water interface, aiming to guide the synthesis of efficient viscosity [...] Read more.

Effectively enhancing oil recovery can be achieved by reducing the viscosity of crude oil. Therefore, this paper investigated the viscosity reduction behavior of carbon nanotube viscosity reducers with different molecular structures at the oil–water interface, aiming to guide the synthesis of efficient viscosity reducers based on molecular structure. This study selected carbon nanotubes with different functional groups (NH₂-CNT, OH-CNT, and COOH-CNT) for research, and carbon nanotubes with varying carbon chain lengths were synthesized. These were then combined with Tween 80 to form a nanofluid. Scanning electron microscopy analysis revealed an increased dispersibility of carbon nanotubes after introducing carbon chains. Contact angle experiments demonstrated that -COOH exhibited the best hydrophilic effect. The experiments of zeta potential, conductivity, viscosity reduction, and interfacial tension showed that, under the same carbon chain length, the conductivity and viscosity reduction rate sequence for different functional groups was -NH₂ < -OH < -COOH. The dispersing and stabilizing ability and interfacial tension reduction sequence for different functional groups was -COOH < -OH < -NH₂. With increasing carbon chain length, conductivity and interfacial tension decreased, and the viscosity reduction rate and the dispersing and stabilizing ability increased. Molecular dynamics simulations revealed that, under the same carbon chain length, the diffusion coefficient sequence for different functional groups was -NH₂ < -OH < -COOH. The diffusion coefficient gradually decreased as the carbon chain length increased, resulting in better adsorption at the oil–water interface. This study holds significant importance in guiding viscosity reduction in heavy oil to enhance oil recovery. Full article

(This article belongs to the Topic Petroleum and Gas Engineering)

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Figure 1
<p>Schematic of carbon nanotube synthesis.</p> Full article ">Figure 2
<p>Infrared spectra of carbon nanotubes with different structures.</p> Full article ">Figure 3
<p>Microscopic morphology of carbon nanotubes with different structures.</p> Full article ">Figure 4
<p>Contact angles of different carbon nanotubes.</p> Full article ">Figure 5
<p>Viscosity reduction of different nanofluids.</p> Full article ">Figure 6
<p>The water yield ratio of the emulsions.</p> Full article ">Figure 7
<p>Interfacial tension of different nanofluids.</p> Full article ">Figure 8
<p>Mechanism of nanofluids at the oil–water interface.</p> Full article ">Figure 9
<p>Initial models of carbon nanotubes with different molecular structures.</p> Full article ">Figure 10
<p>Diffusion morphology of carbon nanotubes with different molecular structures at the oil–water interface.</p> Full article ">

17 pages, 7785 KiB  

Open AccessArticle

Fast Detection of the Single Point Leakage in Branched Shale Gas Gathering and Transportation Pipeline Network with Condensate Water

by Xue Zhong, Zhixiang Dai, Wenyan Zhang, Qin Wang and Guoxi He

Energies 2024, 17(11), 2464; https://doi.org/10.3390/en17112464 - 22 May 2024

Cited by 1 | Viewed by 721

Abstract

The node pressure and flow rate along the shale gas flow process are analyzed according to the characteristics of the shale gas flow pipe network, and the non-leaking and leaking processes of the shale gas flow pipe network are modeled separately. The changes [...] Read more.

The node pressure and flow rate along the shale gas flow process are analyzed according to the characteristics of the shale gas flow pipe network, and the non-leaking and leaking processes of the shale gas flow pipe network are modeled separately. The changes in pressure over time along each pipe segment in the network provide new ideas for identifying leaking pipe sections. This paper uses the logarithmic value of pressure as the basis for judging whether the flow pipe network is leaking or not, according to the process of varying flow parameters resulting in the regularity of leakage. A graph of the change in pressure of the pipe section after the leak compared to the pressure of the non-leaking section of pipe over time can be plotted, accurately identifying the specific section of pipe with the leak. The accuracy of this novel method is verified by the leakage section and statistical data of the shale gas pipeline network in situ used in this paper. Full article

(This article belongs to the Topic Petroleum and Gas Engineering)

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Figure 1
<p>Topological structure diagram of shale gas gathering and transportation pipeline network and leakage schematic diagram of pipeline section 1.</p> Full article ">Figure 2
<p>Pressure variation amplitude curve of the pipe section with leaked point after leakage.</p> Full article ">Figure 3
<p>Single point leakage model of pipe section 4 or pipe section 7.</p> Full article ">Figure 4
<p>Law of pressure change before and after leakage of pipe section 4.</p> Full article ">Figure 5
<p>Law of pressure change before and after leakage of pipe section 7.</p> Full article ">Figure 6
<p>Temperature changes before and after leakage in pipe section 4.</p> Full article ">Figure 7
<p>Temperature changes before and after leakage in pipe section 7.</p> Full article ">Figure 8
<p>Changes in liquid holdup before and after leakage in pipe section 4.</p> Full article ">Figure 9
<p>Changes in liquid holdup before and after leakage in pipe section 7.</p> Full article ">Figure 10
<p>Changes in gas flow velocity before and after leakage in pipe section 4.</p> Full article ">Figure 11
<p>Changes in gas flow velocity before and after leakage in pipe section 7.</p> Full article ">Figure 12
<p>Changes in liquid flow velocity before and after leakage in pipe section 4.</p> Full article ">Figure 13
<p>Changes in liquid flow velocity before and after leakage in pipe section 7.</p> Full article ">Figure 14
<p>Changes in pressure in pipeline network before and after the leakage happened in pipe section 4.</p> Full article ">Figure 15
<p>Changes in pressure in pipeline network before and after the leakage happened in pipe section 7.</p> Full article ">Figure 16
<p>Change in temperature in pipeline network before and after leakage happened in pipe section 4.</p> Full article ">Figure 17
<p>Change in temperature in the pipeline network before and after the leakage happened in pipe section 7.</p> Full article ">Figure 18
<p>Changes in liquid holdup in pipeline network before and after the leakage happened in pipe section 4.</p> Full article ">Figure 19
<p>Changes in liquid holdup in pipeline network before and after the leakage happened in pipe section 7.</p> Full article ">Figure 20
<p>Changes in gas flow rate in pipeline network before and after the leakage happened in pipe section 4.</p> Full article ">Figure 21
<p>Changes in gas flow rate in pipeline network before and after the leakage happened in pipe section 7.</p> Full article ">Figure 22
<p>Change in liquid flow rate in pipeline network before and after leakage happened in pipe section 4.</p> Full article ">Figure 23
<p>Change in liquid flow rate in pipeline network before and after leakage happened in pipe section 7.</p> Full article ">Figure 24
<p>Pressure variation amplitude of each pipe segment in the pipeline network with time after leakage of pipe segment 4.</p> Full article ">Figure 25
<p>Pressure variation amplitudes of each pipe segment in the pipeline network with time after leakage of pipe segment 7.</p> Full article ">

attachment

Supplementary material:
Supplementary File 1 (ZIP, 112 KiB)

20 pages, 6042 KiB  

Open AccessArticle

Evaluation of the Influence of Water Content in Oil on the Metrological Performance of Oil Flow Measurement Systems

by Augusto Proença da Silva and Elcio Cruz de Oliveira

Energies 2024, 17(10), 2355; https://doi.org/10.3390/en17102355 - 13 May 2024

Cited by 1 | Viewed by 829

Abstract

According to current Brazilian regulations, if the volumes of oil produced used as a reference for the payment of government shares and third parties contain a water content greater than 2% v/v, these volumes must be arbitrarily increased between 1.44% [...] Read more.

According to current Brazilian regulations, if the volumes of oil produced used as a reference for the payment of government shares and third parties contain a water content greater than 2% v/v, these volumes must be arbitrarily increased between 1.44% and 10.89% due exclusively to water content, which has caused operational problems for oil companies such as differences between volumes produced and volumes sold, and additional payments from government shares and third parties. This study aimed to evaluate the metrological performance of oil measurement systems with ultrasonic, Coriolis and positive displacement flow meters when subjected to varied water content, fluid temperature and flow rate conditions using the Design of Experiments and the Response Surface Methodology. The analysis of variance showed that the models presented good fits for the ultrasonic meter (coefficient of determination R² of 97.96%, p-value of 0.001, and a standard deviation of 5.89 × 10⁻⁵); Coriolis meter (R² of 90.91%, p-value of 0.037, and a standard deviation of 5.88 × 10⁻⁵); and positive displacement meter (R² of 99.07%, p-value of 0.000, and a standard deviation of 4.85 × 10⁻⁵). The results of the experiments carried out indicate that the contribution of each parameter analyzed to the metrological performance of the measurement system varies depending on the measurement technology used by the flow meter. However, the fluid temperature proved to be a relevant parameter common to all flow measurement technologies evaluated. All measuring technologies evaluated were influenced by water content in the range of 0% to 10% v/v, with the measurement error being less than 0.2% when compared to a standard positive displacement type meter in almost all experimental conditions. The Coriolis-type flow meter was the one that presented the smallest error among the measuring technologies evaluated. Full article

(This article belongs to the Topic Petroleum and Gas Engineering)

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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Schematic diagram of the test bench.</p> Full article ">Figure 2
<p>Monitoring the fluid density using the Coriolis flow meter after changing the BSW level.</p> Full article ">Figure 3
<p>Pareto chart for the estimated standardized effects of the ultrasonic flow meter.</p> Full article ">Figure 4
<p>(<b>A</b>) Response surface and (<b>B</b>) contour plot of the error of the ultrasonic flow meter in the BSW condition equal to 0% <span class="html-italic">v</span>/<span class="html-italic">v</span>.</p> Full article ">Figure 5
<p>(<b>A</b>) Response surface and (<b>B</b>) contour plot of the error of the ultrasonic flow meter in the BSW condition equal to 5% <span class="html-italic">v</span>/<span class="html-italic">v</span>.</p> Full article ">Figure 6
<p>(<b>A</b>) Response surface and (<b>B</b>) contour plot of the error of the ultrasonic flow meter in the BSW condition equal to 10% <span class="html-italic">v</span>/<span class="html-italic">v</span>.</p> Full article ">Figure 7
<p>Pareto chart for the estimated standardized effects for meter in flow rate of Coriolis type.</p> Full article ">Figure 8
<p>(<b>A</b>) Response surface and (<b>B</b>) contour plot of the error of the Coriolis meter in the BSW condition equal to 0% <span class="html-italic">v</span>/<span class="html-italic">v</span>.</p> Full article ">Figure 9
<p>(<b>A</b>) Response surface and (<b>B</b>) contour plot of the error of the Coriolis meter in the BSW condition equal to 5% <span class="html-italic">v</span>/<span class="html-italic">v</span>.</p> Full article ">Figure 10
<p>(<b>A</b>) Response surface and (<b>B</b>) contour plot of the error of the Coriolis meter in the BSW condition equal to 10% <span class="html-italic">v</span>/<span class="html-italic">v</span>.</p> Full article ">Figure 11
<p>Pareto chart for the estimated standardized effects for positive displacement meter.</p> Full article ">Figure 12
<p>(<b>A</b>) Response surface and (<b>B</b>) contour plot of the error of the positive displacement meter in the BSW condition equal to 0% <span class="html-italic">v</span>/<span class="html-italic">v</span>.</p> Full article ">Figure 13
<p>(<b>A</b>) Response surface and (<b>B</b>) contour plot of the error of the positive displacement meter in the BSW condition equal to 5% <span class="html-italic">v</span>/<span class="html-italic">v</span>.</p> Full article ">Figure 14
<p>(<b>A</b>) Response surface and (<b>B</b>) contour plot of the error of the positive displacement meter in the BSW condition equal to 10% <span class="html-italic">v</span>/<span class="html-italic">v</span>.</p> Full article ">

18 pages, 6460 KiB  

Open AccessArticle

Research on the Autonomous Control Technology Used in the Slurry Mixing System of Cementing Units

by Xiang Gao, Guojian Hou, Huiwen Yang, Changmiao Hu, Junguo Cui and Wensheng Xiao

Appl. Sci. 2024, 14(9), 3568; https://doi.org/10.3390/app14093568 - 24 Apr 2024

Viewed by 843

Abstract

Cementing is a critical link in oil and gas exploitation, in which slurry density control is particularly important. In this study, we examined a slurry mixing control system in order to solve the problem of time delays in the mixing system. The model [...] Read more.

Cementing is a critical link in oil and gas exploitation, in which slurry density control is particularly important. In this study, we examined a slurry mixing control system in order to solve the problem of time delays in the mixing system. The model of a slurry mixing system was built in accordance with the system’s structure. A Smith fuzzy PID (proportion integration differentiation) composite control solution is proposed herein, and the simulation results show that the adjustment time and overshoot are lower than those of the conventional PID control and Smith predictive compensation control. A genetic algorithm is utilized to optimize the quantization factor and scale factor of the Smith fuzzy PID controller. Following optimization, the rise time of the controller was found to be 0.45 s, which represents a decrease of 35.9%, the overshoot was reduced by 0.4%, and the stabilization time was reduced by 36.6%. Afterward, we built a cementing slurry mixing simulation experimental platform, and experiments were used to verify the feasibility and superiority of the Smith fuzzy PID controller optimized by the genetic algorithm in comparison with the conventional controllers. The study results thus provide a scientific basis for the engineering application of the autonomous control technology of the slurry mixing system in cementing units. Full article

(This article belongs to the Topic Petroleum and Gas Engineering)

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Figure 1
<p>Control schematic of the automatic slurry mixing system.</p> Full article ">Figure 2
<p>Cementing mixing system density control process.</p> Full article ">Figure 3
<p>Fuzzy PID control schematic.</p> Full article ">Figure 4
<p>Input membership function. (<b>a</b>) <span class="html-italic">e</span> membership function plots. (<b>b</b>) <span class="html-italic">ec</span> membership function plots.</p> Full article ">Figure 5
<p>Output membership function. (<b>a</b>) <span class="html-italic">K_p</span> membership function plots. (<b>b</b>) <span class="html-italic">K_i</span> membership function plots. (<b>c</b>) <span class="html-italic">K_d</span> membership function plots.</p> Full article ">Figure 6
<p>Controller simulation model.</p> Full article ">Figure 7
<p>Internal structure diagram of the fuzzy controller.</p> Full article ">Figure 8
<p>MATLAB simulation diagram of three control algorithms.</p> Full article ">Figure 9
<p>Schematic of Smith fuzzy PID control optimized by the genetic algorithm.</p> Full article ">Figure 10
<p>The process of fuzzy control optimized by the genetic algorithm.</p> Full article ">Figure 11
<p>Simulation structure of the Smith fuzzy PID controller optimized by the genetic algorithm.</p> Full article ">Figure 12
<p>Genetic algorithm adaptation curve.</p> Full article ">Figure 13
<p>MATLAB simulation diagram of the four control algorithms.</p> Full article ">Figure 14
<p>Principle diagram of the slurry mixing experiment. 1—PLC; 2—computer; 3—water flowmeter; 4—high-energy mixer; 5—cement pump; 6—water pump; 7—proportional solenoid valve; 9—densitometer; 10—slurry mixing tank; 11—recirculating pump; 12—boost pump; 13—triplex plunger pump.</p> Full article ">Figure 15
<p>Slurry density control experimental platform. 1—PLC; 2—side-mounted densitometer; 3—agitator; 4—water flowmeter.</p> Full article ">Figure 16
<p>Control algorithm output curves. (<b>a</b>) PID control output curve. (<b>b</b>) GA Smith fuzzy PID control output curve.</p> Full article ">Figure 17
<p>Experimental curve of valve openness.</p> Full article ">

24 pages, 4627 KiB  

Open AccessArticle

Utilizing Differences in Mercury Injection Capillary Pressure and Nuclear Magnetic Resonance Pore Size Distributions for Enhanced Rock Quality Evaluation: A Winland-Style Approach with Physical Meaning

by Zheng Gu, Shuoshi Wang, Ping Guo and Wenhua Zhao

Appl. Sci. 2024, 14(5), 1881; https://doi.org/10.3390/app14051881 - 25 Feb 2024

Viewed by 1133

Abstract

Pore structure is a fundamental parameter in determining the hydrocarbon storage capacity and flow characteristics of a reservoir. Mercury injection capillary pressure (MICP) and nuclear magnetic resonance (NMR) are two commonly utilized techniques for characterizing rock pore structures. However, current studies indicate that [...] Read more.

Pore structure is a fundamental parameter in determining the hydrocarbon storage capacity and flow characteristics of a reservoir. Mercury injection capillary pressure (MICP) and nuclear magnetic resonance (NMR) are two commonly utilized techniques for characterizing rock pore structures. However, current studies indicate that disparities in testing methodologies due to distinct physical characteristics lead to a partial misalignment in pore size distributions. We conducted MICP (dynamic) and NMR (static) experiments on eight tight sandstone and eight shale samples and proposed a method to utilize information from the differences in MICP and NMR pore size distributions, aiming to enhance the accuracy of rock quality analysis. We observed that in rock cores where large pores are interconnected with smaller pore throats, MICP tends to overestimate the proportion of these smaller pores and underestimate the larger ones. Furthermore, we integrated information from both dynamic and static experimental processes based on physical significance and found that the fitting accuracy of the newly proposed method is superior to the Winland r₃₅ equation. Compared to the Winland r₃₅ equation, our new method significantly improves fitting accuracy, increasing the R-squared value from 0.46 to 0.93 in sandstones and from 0.80 to 0.87 in shales. This represents a potential high-precision, comprehensive tool for rock quality analysis, offering a new perspective for an in-depth understanding of rock properties. Full article

(This article belongs to the Topic Petroleum and Gas Engineering)

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<p>The discrepancy between MICP and NMR methods schematic diagram.</p> Full article ">Figure 2
<p>Rock samples: (<b>a</b>) sandstone S1-1; (<b>b</b>) shale H3-5.</p> Full article ">Figure 3
<p>The process of rock quality evaluation.</p> Full article ">Figure 4
<p>The T₂ relaxation time distribution spectrum of Tuha sandstone.</p> Full article ">Figure 5
<p>The T₂ relaxation time distribution spectrum of Hechuan shale.</p> Full article ">Figure 6
<p>The cumulative frequency distribution of NMR and MICP (S1-1 and H3-5).</p> Full article ">Figure 7
<p>Fitting-related parameters of conversion from NMR T₂ to pore radius (S1-1 and H3-5).</p> Full article ">Figure 8
<p>A comparison of pore throat size distributions in sandstone S1-1 as determined via NMR and MICP measurements.</p> Full article ">Figure 9
<p>MICP schematic diagram: (<b>a</b>) MICP underestimates large pore porosity, (<b>b</b>) MICP overestimates small pore porosity.</p> Full article ">Figure 10
<p>A comparison of pore-throat size distributions in sandstone as determined by NMR and MICP measurements: (<b>a</b>) S1-2, (<b>b</b>) S1-3, (<b>c</b>) S1-4, (<b>d</b>) S2-5, (<b>e</b>) S2-6, (<b>f</b>) S2-7, and (<b>g</b>) S2-8.</p> Full article ">Figure 11
<p>A comparison of pore throat size distributions in shale H3-5 as determined via NMR and MICP measurements.</p> Full article ">Figure 12
<p>A comparison of pore-throat size distributions in shale as determined by NMR and MICP methods: (<b>a</b>) H1-1, (<b>b</b>) H1-2, (<b>c</b>) H2-3, (<b>d</b>) H2-4, (<b>e</b>) H3-6, (<b>f</b>) H4-7, and (<b>g</b>) H4-8.</p> Full article ">Figure 13
<p>The cumulative frequency distribution by NMR and MICP (S1-1).</p> Full article ">Figure 14
<p>Porosity–permeability graph merged with the r35 technique of Winland.</p> Full article ">Figure 15
<p>The relationship between r₃₅, K_a, and Φ: (<b>a</b>) sandstone and (<b>b</b>) shale.</p> Full article ">Figure 16
<p>The relationship between F_rc, K_a, and Φ: (<b>a</b>) sandstone and (<b>b</b>) shale.</p> Full article ">Figure 16 Cont.
<p>The relationship between F_rc, K_a, and Φ: (<b>a</b>) sandstone and (<b>b</b>) shale.</p> Full article ">

22 pages, 5535 KiB  

Open AccessArticle

Enhancing Dynagraph Card Classification in Pumping Systems Using Transfer Learning and the Swin Transformer Model

by Guoqing Dong, Weirong Li, Zhenzhen Dong, Cai Wang, Shihao Qian, Tianyang Zhang, Xueling Ma, Lu Zou, Keze Lin and Zhaoxia Liu

Appl. Sci. 2024, 14(4), 1657; https://doi.org/10.3390/app14041657 - 19 Feb 2024

Cited by 1 | Viewed by 1062

Abstract

The dynagraph card plays a crucial role in evaluating oilfield pumping systems’ performance. Nevertheless, classifying dynagraph cards can be quite difficult because certain operating conditions may exhibit similar patterns. Conventional classification approaches mainly involve labor-intensive manual analysis of these cards, leading to subjectivity, [...] Read more.

The dynagraph card plays a crucial role in evaluating oilfield pumping systems’ performance. Nevertheless, classifying dynagraph cards can be quite difficult because certain operating conditions may exhibit similar patterns. Conventional classification approaches mainly involve labor-intensive manual analysis of these cards, leading to subjectivity, prolonged processing times, and vulnerability to human prejudices. In response to this challenge, our study introduces a novel approach that leverages transfer learning and the Swin Transformer model for classifying dynagraph cards across various operating conditions in rod pumping systems. Initially, the Swin Transformer model undergoes pre-training using the ImageNet-22k dataset. Subsequently, we fine-tune the model’s weights using actual dynagraph card datasets, facilitating direct classification analysis with dynagraph cards as input variables. The adoption of transfer learning significantly reduces the training time while enhancing the accuracy of condition diagnosis. To assess the effectiveness of our proposed method, we conducted a comparative evaluation against conventional models like ResNet50, DenseNet121, LeNet, and ViT. The findings demonstrate that our approach outperforms other methods, achieving an accuracy of 96%, thereby improving classification accuracy by 3–4%. Therefore, our approach, based on transfer learning and the Swin Transformer model, provides a better solution for practical problems involving similar dynagraph cards. It meets the requirements of oil field operations, enhancing economic benefits and work efficiency. Full article

(This article belongs to the Topic Petroleum and Gas Engineering)

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<p>Pumping unit well schematic: (a) donkey head; (b) suspension rope device; (c) sucker rod; (d) smooth rod; (e) traveling valve; (f) plunger; (g) bushing; (h) standing valve.</p> Full article ">Figure 2
<p>Theoretical indicator diagram.</p> Full article ">Figure 3
<p>Different working conditions of the dynagraph card: (<b>a</b>) Normal Operation; (<b>b</b>) Fluid Pound; (<b>c</b>) Gas Interference; (<b>d</b>) Gas Locked Pump; (<b>e</b>) Delayed Closing of Traveling Valve; (<b>f</b>) Pump Barrel Split; (<b>g</b>) Fluid Pound and Delayed Closing of Traveling Valve.</p> Full article ">Figure 3 Cont.
<p>Different working conditions of the dynagraph card: (<b>a</b>) Normal Operation; (<b>b</b>) Fluid Pound; (<b>c</b>) Gas Interference; (<b>d</b>) Gas Locked Pump; (<b>e</b>) Delayed Closing of Traveling Valve; (<b>f</b>) Pump Barrel Split; (<b>g</b>) Fluid Pound and Delayed Closing of Traveling Valve.</p> Full article ">Figure 4
<p>Architecture diagram of vision transformer.</p> Full article ">Figure 5
<p>Architecture diagram of the Swin Transformer.</p> Full article ">Figure 6
<p>Architecture diagram of the Swin Transformer block.</p> Full article ">Figure 7
<p>Divided dataset.</p> Full article ">Figure 8
<p>Experimental process.</p> Full article ">Figure 9
<p>Swin Transformer’s accuracy curve.</p> Full article ">Figure 10
<p>ROC curves for each category.</p> Full article ">Figure 11
<p>Confusion matrix of test set. ((a) Normal Operation; (b) Fluid Pound; (c) Gas Interference; (d) Gas Locked Pump; (e) Delayed Closing of Traveling Valve; (f) Pump Barrel Split; (g) Fluid Pound and Delayed Closing of Traveling Valve).</p> Full article ">Figure 12
<p>Confusion matrix for different working conditions. ((a) Normal Operation; (b) Fluid Pound; (c) Gas Interference; (e) Delayed Closing of Traveling Valve).</p> Full article ">Figure 13
<p>Predicted incorrect samples. ((a) Normal Operation; (b) Fluid Pound; (c) Gas Interference; (e) Delayed Closing of Traveling Valve).</p> Full article ">Figure 14
<p>PR curves for each category.</p> Full article ">Figure 15
<p>Accuracy of training for each model.</p> Full article ">Figure 16
<p>Confusion matrix of the validation set. ((a) Normal Operation; (b) Fluid Pound; (c) Gas Interference; (d) Gas Locked Pump; (e) Delayed Closing of Traveling Valve; (f) Pump Barrel Split; (g) Fluid Pound and Delayed Closing of Traveling Valve).</p> Full article ">

34 pages, 6063 KiB  

Open AccessArticle

An Interval-Valued Intuitionistic Fuzzy Bow-Tie Model (IVIF-BT) for the Effectiveness Evaluation of Safety Barriers in Natural Gas Storage Tank

by Jiawei Liu, Hailong Yin, Yixin Zhang, Xiufeng Li, Yongquan Li, Xueru Gong and Wei Wu

Appl. Sci. 2024, 14(4), 1586; https://doi.org/10.3390/app14041586 - 16 Feb 2024

Cited by 1 | Viewed by 884

Abstract

Safety barriers (SBs) are important means of reducing failure risks of process systems. As barriers vary in type and function, their effectiveness needs to be evaluated in order to find a more reasonable configuration strategy. However, in practice, there is often a lack [...] Read more.

Safety barriers (SBs) are important means of reducing failure risks of process systems. As barriers vary in type and function, their effectiveness needs to be evaluated in order to find a more reasonable configuration strategy. However, in practice, there is often a lack of accurate and complete data relating to SBs, which poses a significant challenge in quantitatively assessing their effectiveness. To address this issue, in this study, we propose a semi-quantitative approach for evaluating the effectiveness of both preventive and protective barriers in process systems by integrating expert elicitation, interval-valued intuitionistic fuzzy numbers (IVIFNs), and a bow-tie model. In this approach, the bow-tie model is first applied to describe the system failure scenarios and the action phases of the barriers, and then IVIFNs with expert judgment are introduced to obtain the failure probabilities of basic events and the effects of SBs. Subsequently, the effectiveness of each barrier is measured by comparing the relative change in failure risk due to the addition of the barrier. To verify the feasibility of this approach, a natural gas storage tank with some barriers was analyzed. The results show that the regular inspection of the deformation or damage of the storage tank has the highest effectiveness, followed by the installation and regular maintenance of safety electrical equipment. Furthermore, compared to a single barrier, multiple barriers can significantly reduce the system risk. Full article

(This article belongs to the Topic Petroleum and Gas Engineering)

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<p>General bow-tie model with safety barriers.</p> Full article ">Figure 2
<p>IFS explanation of real number <span class="html-italic">R</span>.</p> Full article ">Figure 3
<p>Flowchart of the methodology proposed in this study for effectiveness evaluation of safety barriers of process systems.</p> Full article ">Figure 4
<p>The corresponding relationship between failure outcomes and severity types.</p> Full article ">Figure 5
<p>Bow-tie model of the natural gas storage tank considering safety barriers.</p> Full article ">Figure 6
<p>Comparison of the probabilities of the basic events in the BT model converted from IVIFNs with those from the literature [<a href="#B52-applsci-14-01586" class="html-bibr">52</a>].</p> Full article ">Figure 7
<p>The probability of the critical event under different safety barriers.</p> Full article ">Figure 8
<p>The failure consequence severity under different safety barrier scenarios.</p> Full article ">Figure 9
<p>Risk indexes of the natural gas storage tank under different safety barrier scenarios.</p> Full article ">Figure 10
<p>Effectiveness indexes of different safety barriers.</p> Full article ">Figure 11
<p>The effectiveness index of an arbitrary combination of the eleven safety barriers of the natural gas storage tank. The combination mode is denoted as <math display="inline"><semantics> <mrow> <msubsup> <mi>C</mi> <mrow> <mn>11</mn> </mrow> <mi>n</mi> </msubsup> </mrow> </semantics></math>, where <span class="html-italic">n</span> represents the number of SBs included. (<b>j</b>) Box-plot of the effectiveness index for nine combination modes.</p> Full article ">

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17 pages, 32578 KiB  

Open AccessArticle

Preparation of Preformed Submicron Crosslinked Polymer Coils for Conformance Control in Low-Permeability Reservoirs

by Jianwei Liu and Bo Peng

Polymers 2024, 16(1), 39; https://doi.org/10.3390/polym16010039 - 21 Dec 2023

Cited by 1 | Viewed by 981

Abstract

With the increasing development of low-permeability reservoirs, the significance of conformance control treatment has risen considerably. To address the conflict between injectability and plugging performance, as well as to enhance the deep migration capacity of conformance control agents, preformed submicron crosslinked polymer coils [...] Read more.

With the increasing development of low-permeability reservoirs, the significance of conformance control treatment has risen considerably. To address the conflict between injectability and plugging performance, as well as to enhance the deep migration capacity of conformance control agents, preformed submicron crosslinked polymer coils (SCPCs) have been manufactured using aqueous solution dispersion polymerization. Fourier transform infrared and scanning electron microscopy were employed to examine the chemical structure and micromorphology, while particle size distribution, zeta potential, rheological, and filtration properties were analyzed. The effectiveness of conformance control was confirmed through the parallel core displacement. The effective particle size of SCPCs was at a submicron level (500~800 nm). SCPCs exhibit a transitional threshold concentration between gel and sol states (0.25 wt%~0.5 wt%). SCPCs can efficiently block the 1.2 μm microporous filter membrane. The filtration time is up to 67.8 min. SCPCs can improve the water absorption rate of lower permeability cores from 21.21% to 57.89% with a permeability difference of 5. Therefore, SCPCs have good injectability, plugging performance, and deep migration capacity and can be used for conformance control in low-permeability reservoirs. Full article

(This article belongs to the Topic Petroleum and Gas Engineering)

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Figure 1
<p>Schematic of the microporous membrane filtration experiment.</p> Full article ">Figure 2
<p>Schematic of the parallel core displacement.</p> Full article ">Figure 3
<p>Fourier transform infrared spectra of acrylamide and submicron crosslinked polymer coils (SCPCs).</p> Full article ">Figure 4
<p>Scanning electron microscopy photos of 0.01wt% polymer coils with different crosslinking ratios (<b>a</b>) 2%; (<b>b</b>) 1%; (<b>c</b>) 0.5%; (<b>d</b>) 0.25%; and (<b>e</b>) 0%. (<b>f</b>) Scanning electron microscopy photos of 1%wt polymer coils with a crosslinking ratio of 0.25%.</p> Full article ">Figure 5
<p>Polymer coils at different crosslinking ratios.</p> Full article ">Figure 6
<p>Differences between dispersion polymerization and microemulsion polymerization.</p> Full article ">Figure 7
<p>Effect of crosslinking ratio on coils (<b>a</b>) distribution and (<b>b</b>) effective size.</p> Full article ">Figure 8
<p>Effect of different conditions on polymer coils size (<b>a</b>) concentration, (<b>b</b>) NaCl concentration, (<b>c</b>) ionic species, and (<b>d</b>) temperature.</p> Full article ">Figure 9
<p>Zeta potential of SCPCs at different pH levels.</p> Full article ">Figure 10
<p>Steady state shear curves of polymer coils with different crosslinking ratios of (<b>a</b>) 2%, (<b>b</b>) 1%, (<b>c</b>) 0.5%, and (<b>d</b>) 0.25% for different concentrations of polymer coils.</p> Full article ">Figure 11
<p>Dynamic oscillation curves with different crosslinking ratios of (<b>a</b>) 2%, (<b>b</b>) 1%, (<b>c</b>) 0.5%, and (<b>d</b>) 0.25% for different concentrations of coils.</p> Full article ">Figure 12
<p>Effect of crosslinking reaction (crosslinking ratio 0.25%) on the filtration curve of polymer coils (0.25 wt%).</p> Full article ">Figure 13
<p>Effect of different conditions on the filtration curve of polymer coils (<b>a</b>) crosslinking ratio, (<b>b</b>) SCPC concentration, (<b>c</b>) NaCl concentration, and (<b>d</b>) temperature.</p> Full article ">Figure 14
<p>Difference in blocking mechanism of high/low crosslinked coils.</p> Full article ">Figure 15
<p>Pressure difference between front and back end and split flow rate in high- and low-permeability cores in double-pipe–parallel-drive alternation.</p> Full article ">

17 pages, 5820 KiB  

Open AccessArticle

A Semi-Analytical Model for Studying the Transient Flow Behavior of Nonuniform-Width Fractures in a Three-Dimensional Domain

by Yanzhong Liang, Xuanming Zhang, Wenzhuo Zhou, Qingquan Li, Jia Li, Yawen Du, Hanxin Cai and Bailu Teng

Energies 2023, 16(24), 7920; https://doi.org/10.3390/en16247920 - 5 Dec 2023

Viewed by 850

Abstract

In the fracture propagation model, the assumption that hydraulic fractures with non-uniform widths have been successfully utilized to predict fracture propagation for decades. However, when one conducts post-fracture analysis, the hydraulic fracture is commonly simplified with a uniform width, which is contradictory to [...] Read more.

In the fracture propagation model, the assumption that hydraulic fractures with non-uniform widths have been successfully utilized to predict fracture propagation for decades. However, when one conducts post-fracture analysis, the hydraulic fracture is commonly simplified with a uniform width, which is contradictory to the real fracture models. One of the reasons for over-simplifying the fracture geometry in the post-fracture analysis can be ascribed to the fact that we are still lacking a model to characterize the pressure transient behavior of the nonuniform-width fractures which can induce three-dimensional flow around the fractures. In this work, on the basis of the Green function and Newman product method, the authors derived a semi-analytical model to account for the effect of non-uniform width distribution of the hydraulic fractures in a three-dimensional domain. In addition, the effect of the fracturing strategies on the well performance is investigated based on the developed semi-analytical model. The calculated results from the developed model show that the vertical flow in the vicinity of the fracture cannot be neglected if the fracture height is sufficiently small (e.g., h_f = 10 m), and one can observe vertical elliptical flow and vertical pseudo-radial flow during the production. A nonuniform-width fracture can penetrate further into the reservoir with a lower injection rate (e.g., q_i = 1.44 × 10³ md). For the scenarios of high fracture permeability (i.e., k_f = 1 × 10⁵ md), a smaller fracture height, lower injection rate, and larger Young’s modulus can be more favorable for enhancing the well productivity. Compared to the influence of fracture height, the influences of injection rate and Young’s modulus on the well performance are less pronounced. Full article

(This article belongs to the Topic Petroleum and Gas Engineering)

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<p>Schematic of a partially penetrating fracture with nonuniform width.</p> Full article ">Figure 2
<p>Discretization of the fracture segments.</p> Full article ">Figure 3
<p>The model used in this work: (<b>a</b>) division of the fractured reservoir model; and (<b>b</b>) discretion of one-quarter of the fracture.</p> Full article ">Figure 4
<p>Permeability distribution of the nonuniform-width fractures used in Eclipse (md).</p> Full article ">Figure 5
<p>Comparison between the results from the proposed model and those from Eclipse.</p> Full article ">Figure 6
<p>Pressure transient plot of the benchmark PKN-type fracture.</p> Full article ">Figure 7
<p>Top view of the schematics of (<b>a</b>) bilinear flow, (<b>b</b>) formation linear flow, and (<b>c</b>) horizontal pseudo-radial flow.</p> Full article ">Figure 8
<p>Pressure transient plot of the PKN-type fracture with fracture height of 10 m.</p> Full article ">Figure 9
<p>Side view of the schematics of (<b>a</b>) elliptical flow and (<b>b</b>) vertical pseudo-radial flow.</p> Full article ">Figure 10
<p>Fracture length of the PKN-type fractures as a function of fracture height.</p> Full article ">Figure 11
<p>Fracture width distribution of the PKN-type fractures with different fracture heights: (<b>a</b>) <span class="html-italic">h_f</span> = 10 m; (<b>b</b>) <span class="html-italic">h_f</span> = 20 m; (<b>c</b>) <span class="html-italic">h_f</span> = 30 m; (<b>d</b>) <span class="html-italic">h_f</span> = 40 m; and (<b>e</b>) <span class="html-italic">h_f</span> = 50 m.</p> Full article ">Figure 12
<p>Production rate of the PKN-type fracture with different fracture heights of (<b>a</b>) <span class="html-italic">k_f</span> = 1 × 10⁵ md and (<b>b</b>) <span class="html-italic">k_f</span> = 1 × 10³ md.</p> Full article ">Figure 13
<p>Fracture length of the PKN-type fractures as a function of injection rate.</p> Full article ">Figure 14
<p>Fracture width distribution of the PKN-type fractures with different injection rates: (<b>a</b>) <span class="html-italic">q_i</span> = 1.44 × 10³ m³/day; (<b>b</b>) <span class="html-italic">q_i</span> = 2.88 × 10³ m³/day; (<b>c</b>) <span class="html-italic">q_i</span> = 4.32 × 10³ m³/day; (<b>d</b>) <span class="html-italic">q_i</span> = 5.76 × 10³ m³/day; and (<b>e</b>) <span class="html-italic">q_i</span> = 7.20 × 10³ m³/day.</p> Full article ">Figure 15
<p>Production rate of the PKN-type fracture with different injection rates of (<b>a</b>) <span class="html-italic">k_f</span> = 1 × 10⁵ md and (<b>b</b>) <span class="html-italic">k_f</span> = 1 × 10³ md.</p> Full article ">Figure 16
<p>Fracture length of the PKN-type fractures as a function of Young’s modulus.</p> Full article ">Figure 17
<p>Fracture width distribution of the PKN-type fractures with different Young’s moduli: (<b>a</b>) <span class="html-italic">E</span> = 2.5 × 10⁴ MPa; (<b>b</b>) <span class="html-italic">E</span> = 3.5 × 10⁴ MPa; (<b>c</b>) <span class="html-italic">E</span> = 4.5 × 10⁴ MPa; (<b>d</b>) <span class="html-italic">E</span> = 5.5 × 10⁴ MPa; (<b>e</b>) <span class="html-italic">E</span> = 6.5 × 10⁴ MPa.</p> Full article ">Figure 18
<p>Production rate of the PKN-type fracture with different Young’s moduli of (<b>a</b>) <span class="html-italic">k_f</span> = 1 × 10⁵ md and (<b>b</b>) <span class="html-italic">k_f</span> = 1 × 10³ md.</p> Full article ">

24 pages, 6417 KiB  

Open AccessArticle

A New Methodology for Determination of Layered Injection Allocation in Highly Deviated Wells Drilled in Low-Permeability Reservoirs

by Mao Li, Zhan Qu, Songfeng Ji, Lei Bai and Shasha Yang

Energies 2023, 16(23), 7764; https://doi.org/10.3390/en16237764 - 24 Nov 2023

Cited by 2 | Viewed by 896

Abstract

During the water injection development process of highly deviated wells in low-permeability reservoirs, the water flooding distance between different layers of the same oil and water well is different due to the deviation of the well. In addition, the heterogeneity of low-permeability reservoirs [...] Read more.

During the water injection development process of highly deviated wells in low-permeability reservoirs, the water flooding distance between different layers of the same oil and water well is different due to the deviation of the well. In addition, the heterogeneity of low-permeability reservoirs is strong, and the water absorption capacity between layers is very different. This results in poor effectiveness of commonly used layered injection methods. Some highly deviated wells have premature water breakthroughs after layered water injection, which affects the development effect of the water flooding reservoirs. Therefore, based on the analysis and research of the existing layered injection allocation method and sand body connectivity evaluation method, considering the influence of sand body connectivity, the real displacement distance of highly deviated wells, reservoir physical properties, and other factors, a new methodology for determination of layered injection allocation in highly deviated wells drilled in low-permeability reservoirs is proposed. In this method, the vertical superposition and lateral contact relationship of a single sand body are determined using three methods: sand body configuration identification, seepage unit identification, and single sand body boundary identification. The connectivity coefficient, transition coefficient, and connectivity degree coefficient are introduced to quantitatively evaluate the connectivity of sand bodies and judge the connectivity relationship between single sand bodies. The correlation formula is obtained using the linear regression of the fracture length and ground fluid volume, and the real displacement distance of each layer in highly deviated wells is obtained. The calculation formula of the layered injection allocation is established by analyzing the important factors affecting the layered injection allocation, and a reasonable layered injection allocation is obtained. The calculation parameters of this method are fully considered, the required parameters are easy to obtain, and the practicability is strong. It can provide a method reference for the policy adjustment of layered water injection technology in similar water injection development reservoirs. Full article

(This article belongs to the Topic Petroleum and Gas Engineering)

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<p>Displacement distance of water injection development in vertical wells (<b>a</b>) and highly deviated wells (<b>b</b>).</p> Full article ">Figure 2
<p>Ordos Basin tectonic unit and Yanchang Formation comprehensive histogram. (<b>a</b>) Ordos Basin structure and the location of the study area. (<b>b</b>) Comprehensive histogram of the Chang 6₃ oil-bearing formation in the Huaqing area, Ordos Basin. In the figure, SP, GR, RT, and AC represent natural potential, natural gamma, resistivity and acoustic time difference, respectively.</p> Full article ">Figure 3
<p>Vertical stacking pattern and logging curve identification of the Chang 6₃ reservoir group in the Huaqing area. In the figure, SH and PERM represent argillaceous content and permeability. (<b>a</b>) Isolated vertical interface characteristics of the L419-9 well, (<b>b</b>) separated vertical interface characteristics of the B185-117 well, (<b>c</b>) superimposed vertical interface characteristics of the B179-113 well, (<b>d</b>) cut and stacked vertical interface characteristics of the B185-109 well.</p> Full article ">Figure 4
<p>Lateral contact pattern and logging curve identification of the Chang 6₃ reservoir group in the Huaqing area. (<b>a</b>) Inter-bay contact, (<b>b</b>) levee contact, (<b>c</b>) side-cut contact, (<b>d</b>) substitutive contact, (<b>e</b>) class I docking contact, (<b>f</b>) class II docking contact.</p> Full article ">Figure 4 Cont.
<p>Lateral contact pattern and logging curve identification of the Chang 6₃ reservoir group in the Huaqing area. (<b>a</b>) Inter-bay contact, (<b>b</b>) levee contact, (<b>c</b>) side-cut contact, (<b>d</b>) substitutive contact, (<b>e</b>) class I docking contact, (<b>f</b>) class II docking contact.</p> Full article ">Figure 5
<p>Flow unit division of the Chang 6₃ reservoir in the Huaqing area.</p> Full article ">Figure 6
<p>Regression curve of the width–thickness ratio of a turbidite channel and a single sand body on the side-turbidite channel of the Chang 6₃ oil layer in the Huaqing area.</p> Full article ">Figure 7
<p>The flow unit corrects the contact relationship of a single sand body identified with sand body configuration. (<b>a</b>) Sand body configuration identification of the single sand body connectivity relationship. (<b>b</b>) Flow unit correction and single sand body connectivity relationship. In the figure, the circled numbers 1, 2, and 3 represent different sand body numbers.</p> Full article ">Figure 8
<p>Plane position of the B170-109X well and the B171-109 well.</p> Full article ">Figure 9
<p>Microseismic monitoring results of the B170-109X well.</p> Full article ">Figure 10
<p>The B170-109X and B171-109 well stratigraphic profile (marked as the actual displacement distance).</p> Full article ">Figure 11
<p>Relationship curve between the single-stage fracturing fracture length and the single-stage ground fluid volume of the Chang 6₃ oil-bearing formation in the Huaqing area.</p> Full article ">Figure 12
<p>Fracturing network of the well group B193-101.</p> Full article ">Figure 13
<p>B192-101X well—B193-101 well—B194-101X well sand body connected profile.</p> Full article ">Figure 14
<p>B193-100X well—B193-101 well—B193-102X well sand body connected profile.</p> Full article ">Figure 15
<p>Dynamic verification of layered water injection production. (<b>a</b>) Production curve of the B193-101 well group. (<b>b</b>) Comparison of the water injection profile before and after the injection allocation adjustment in well B193-101.</p> Full article ">

22 pages, 10233 KiB  

Open AccessArticle

Investigation of the Water-Invasion Gas Efficiency in the Kela-2 Gas Field Using Multiple Experiments

by Donghuan Han, Wei Xiong, Tongwen Jiang, Shusheng Gao, Huaxun Liu, Liyou Ye, Wenqing Zhu and Weiguo An

Energies 2023, 16(20), 7216; https://doi.org/10.3390/en16207216 - 23 Oct 2023

Cited by 2 | Viewed by 1031

Abstract

Although improving the recovery of water-invaded gas reservoirs has been extensively studied in the natural gas industry, the nature of the efficiency of water-invaded gas recovery remains uncertain. Low-field nuclear magnetic resonance (NMR) can be used to clearly identify changes in water saturation [...] Read more.

Although improving the recovery of water-invaded gas reservoirs has been extensively studied in the natural gas industry, the nature of the efficiency of water-invaded gas recovery remains uncertain. Low-field nuclear magnetic resonance (NMR) can be used to clearly identify changes in water saturation in the core during high-pressure water-invasion gas. Here, we provide four types of water-invasion gas experiments (spontaneous imbibition, atmospheric pressure, high-pressure approximate equilibrium, and depletion development water-invasion gas) to reveal the impact of the water-invasion gas efficiency on the recovery of water-invasion gas reservoirs. NMR suggested that imbibition mainly occurs in medium to large pores and that residual gas remains mainly in large pores. The amount of gas driven out from the large pores by imbibition was much greater than that driven out from the small pores. Our findings indicate that the initial gas saturation, contact surface, and permeability are the main factors controlling the residual gas saturation, suggesting that a reasonable initial water saturation should be established before the water-invasion gas experiments. Additionally, the water-invasion gas efficiency at high pressures can be more reliably obtained than that at normal pressures. After the high-pressure approximate equilibrium water invasion for gas displacement, a large amount of residual gas remains in the relatively larger pores of the core, with a residual gas saturation of 42%. In contrast to conventional experiments, the residual gas saturation and water displacement efficiency of the high-pressure approximate equilibrium water invasion for gas displacement did not exhibit a favorable linear relationship with the permeability. The residual gas saturation ranged from 34 to 43% (avg. 38%), while the water displacement efficiency ranged from 32 to 45% (avg. 40%) in the high-pressure approximate equilibrium water invasion for gas displacement. The residual gas saturation in the depletion development water-invasion gas experiment was 26–40% (average: 33%), with an efficiency ranging from 45 to 50% (average: 48%), indicating that the depletion development experiment is closer to the actual development process of gas reservoirs. Our findings provide novel insights into water-invasion gas efficiency, providing robust estimates of the recovery of water-invasion gas reservoirs. Full article

(This article belongs to the Topic Petroleum and Gas Engineering)

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Figure 1
<p>Flow chart of the spontaneous imbibition experiment.</p> Full article ">Figure 2
<p>Flow chart of water-invasion gas in the atmospheric pressure experiment.</p> Full article ">Figure 3
<p>Flow chart of the high-pressure approximate equilibrium water-invasion gas experiment.</p> Full article ">Figure 4
<p>Flow chart of water invasion for gas displacement in a depleted reservoir.</p> Full article ">Figure 5
<p>Different factors affecting spontaneous imbibition for well KL2-J3 4-21/29 ①: (<b>a</b>) the impact of different contact surface sizes on the water-invasion gas efficiency; (<b>b</b>) the unidirectional and multidirectional imbibition T₂ spectra; (<b>c</b>) the relationship between spontaneous imbibition gas saturation and permeability; and (<b>d</b>) the variation in water-invasion gas efficiency with permeability.</p> Full article ">Figure 6
<p>The effect of initial water saturation on imbibition for well KL2-J3 4-21/29 ①: (<b>a</b>) the water-invasion gas efficiency at different initial water saturations; (<b>b</b>) the NMR spectra during spontaneous imbibition at different initial water saturations; (<b>c</b>) the relationship between initial gas saturation and residual gas saturation, the red curve is a binary equation fitted to the data in horizontal and vertical coordinates, the blue squares are the data of initial gas saturation and residual gas saturation; and (<b>d</b>) the fitting of the normalized model for the degree of spontaneous imbibition.</p> Full article ">Figure 7
<p>The fitting of the normalized model rate of spontaneous imbibition.</p> Full article ">Figure 8
<p>Water-invasion gas under atmospheric pressure: (<b>a</b>) variation in water saturation and injected water in pore volume; (<b>b</b>) variation in water-invasion gas displacement efficiency and injected water in pore volume; (<b>c</b>) NMR spectrum of water-invasion gas under atmospheric pressure; and (<b>d</b>) r elationship between original gas saturation and residual gas saturation.</p> Full article ">Figure 9
<p>Relationship between the efficiency of water-invasion gas and permeability. The straight line is a linear equation fitted to the data in horizontal and vertical coordinates. The dot is the data of permeability and efficiency of water invasion gas.</p> Full article ">Figure 10
<p>Results of high-pressure approximate equilibrium water-invasion gas: (<b>a</b>) gas volume compressibility factor curve of the Kela-2 gas field; (<b>b</b>) NMR spectra of high-pressure approximate equilibrium water-invasion gas in the KL2-J3 well 4-25/29 ④; (<b>c</b>) comparison of NMR results for atmospheric pressure and high-pressure water-invasion gas in the 4-21/29 ①; and (<b>d</b>) experimental results of efficiency and residual gas saturation.</p> Full article ">Figure 11
<p>Results of water invasion for gas displacement in depleted reservoir development: (<b>a</b>) the relationship between the average simulated pressure and the cumulative gas production in nonwater and infinite water reservoirs; (<b>b</b>) relationship between the pressure at the inlet and outlet and the cumulative gas production; (<b>c</b>) gas production per unit apparent pressure in the depletion development; and (<b>d</b>) variation trend of recovery rate in the depletion development of the infinite water reservoir.</p> Full article ">Figure 12
<p>Depletion development water-invasion gas recovery efficiency: (<b>a</b>) variation trend of recovery rate in the depletion development of the infinite water reservoir; and (<b>b</b>) the results of the water-invasion gas efficiency and residual gas saturation for high-pressure quasi-equilibrium water-invasion gas.</p> Full article ">

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17 pages, 3076 KiB  

Open AccessArticle

Thermogravimetric Pyrolysis Behavior and Kinetic Study of Two Different Organic-Rich Mudstones via Multiple Kinetic Methods

by Yaoyu Li, Shixin Zhou, Jing Li, Zexiang Sun and Wenjun Pang

Energies 2023, 16(17), 6372; https://doi.org/10.3390/en16176372 - 2 Sep 2023

Viewed by 1054

Abstract

Two representative organic-rich mudstones from the Middle Permian (MP) and the Upper Carboniferous (UC) around the Fukang Depression in the Junggar Basin were selected to study and compare the pyrolysis behavior and kinetics. The MP and UC were described as type I and [...] Read more.

Two representative organic-rich mudstones from the Middle Permian (MP) and the Upper Carboniferous (UC) around the Fukang Depression in the Junggar Basin were selected to study and compare the pyrolysis behavior and kinetics. The MP and UC were described as type I and type II kerogen, respectively. The FTIR and XRD results revealed that the MP contains carbonates and different clay minerals compared to the UC. Peak deconvolution was used for the UC to delineate the pyrolysis process to better understand and compare the similarities and differences in the pyrolysis kinetics of the two mudstones. In addition, the Coats-Redfern method was employed to further differentiate the reaction stages based on the differences in the reaction models during pyrolysis. The kinetic results revealed that the activation energy, pre-exponential factors, and reaction models of the two mudstones have some similarities and differences. Combined with the analysis of the pyrolysis volatiles, the UC sample can release more CH₄, CO, CO₂, and aromatic hydrocarbon compounds at high temperatures, indicating that the UC has more oxygen-containing functional groups and aromatics, while the MP has more aliphatics. Through the above studies, the pyrolysis kinetics and mechanism of two organic-rich rocks could be clarified, guiding their development and efficient utilization. Full article

(This article belongs to the Topic Petroleum and Gas Engineering)

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Figure 1
<p>(<b>a</b>) XRD patterns and (<b>b</b>) FTIR spectra of MP and UC.</p> Full article ">Figure 2
<p>The conversion rates α and reaction rates dα/dt curves of (<b>a</b>,<b>b</b>) MP; (<b>c</b>,<b>d</b>) UC.</p> Full article ">Figure 3
<p>Asym2sig peak fitting for UC at 5, 10, 20, and 40 K/min.</p> Full article ">Figure 4
<p>The activation energies E and the corresponding correlation coefficient R² of the organic matter pyrolysis process by Starink method for (<b>a</b>) MP; (<b>b</b>) UC.</p> Full article ">Figure 5
<p>Comparison of the reaction rate dα/dt between the experimental and simulated data for (<b>a</b>) MP; (<b>b</b>) UC-Peak I; (<b>c</b>) UC-Peak II.</p> Full article ">Figure 6
<p>(<b>a</b>,<b>b</b>) Three-dimensional infrared spectra of volatile compounds released during pyrolysis at 20 K min⁻¹; (<b>c</b>,<b>d</b>) the evolution of the major pyrolysis volatiles with temperature increasing ((<b>a</b>,<b>c</b>) for MP and (<b>b</b>,<b>d</b>) for UC).</p> Full article ">Figure 6 Cont.
<p>(<b>a</b>,<b>b</b>) Three-dimensional infrared spectra of volatile compounds released during pyrolysis at 20 K min⁻¹; (<b>c</b>,<b>d</b>) the evolution of the major pyrolysis volatiles with temperature increasing ((<b>a</b>,<b>c</b>) for MP and (<b>b</b>,<b>d</b>) for UC).</p> Full article ">

21 pages, 15796 KiB  

Open AccessArticle

Quantitative Study of the Lateral Sealing Ability of Faults Considering the Diagenesis Degree of the Fault Rock: An Example from the Nantun Formation in the Wuerxun-Beier Sag in the Hailar Basin, China

by Xinlei Hu, Yanfang Lv, Yang Liu and Junqiao Liu

Resources 2023, 12(9), 98; https://doi.org/10.3390/resources12090098 - 23 Aug 2023

Cited by 1 | Viewed by 1270

Abstract

The goal of this study was to accurately evaluate the lateral sealing ability of a fault in siliciclastic stratum based on previous analysis of the lateral sealing of faults by a large number of scholars in the published literature and physical simulation experiments. [...] Read more.

The goal of this study was to accurately evaluate the lateral sealing ability of a fault in siliciclastic stratum based on previous analysis of the lateral sealing of faults by a large number of scholars in the published literature and physical simulation experiments. Content of the clay mineral phase and the diagenetic degree of fault rock were investigated as the main factors to evaluate the lateral sealing of faults. Based on this theory, the configuration relationship between the clay content and burial depth of fault rock (SGR&H) threshold evaluation method for the lateral sealing of faults was established. Then, we applied these results to evaluate the lateral sealing ability of faults in the Beixi, Beier, Wuerxun, and Surennuoer areas in the Hailar Basin, China. The variation in SGR boundary values with burial depth between the lateral opening and moderate sealing area, as well as between the moderate and strong sealing area of the faults, are obtained. Compared with the previous methods, the SGR&H threshold method transforms the static SGR value of a formation or even a region into a dynamic SGR value that changes with the burial depth, which can fully characterize the differences in the conditions required for sealing faults with different internal structures at different depths. In determining the lateral sealing ability of faults by comparing the evaluation results, we discovered the following. (1) In the same area, the sealing thresholds of faults within different layers are different because the deep strata are subjected to greater pressures and longer loading times, so these faults are more likely to seal laterally. (2) In the same layer, the sealing thresholds of faults in different areas are also different. The higher the thickness ratio between the sandstone and the formation (RSF), the smaller the entry pressure of the fault rock when it has reached a critical seal state, so the SGR&H thresholds are relatively small. Compared to the previous methods, the SGR&H threshold method in this article reduces the exploration risk of faults with relatively low diagenetic degree in shallow strata, and also increases the exploration potential of faults with relatively high diagenetic degree in deep strata. The evaluation results are more consistent with the actual underground situation. Full article

(This article belongs to the Topic Petroleum and Gas Engineering)

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Figure 1
<p>Regional geological map of the Hailar Basin. (Revised by [<a href="#B47-resources-12-00098" class="html-bibr">47</a>]).</p> Full article ">Figure 2
<p>Comprehensive stratigraphic map of the Hailar Basin. (Revised by [<a href="#B50-resources-12-00098" class="html-bibr">50</a>]).</p> Full article ">Figure 3
<p>Relationship between internal structure of fault zone (<b>a</b>), permeability (<b>b</b>), diagenetic degree (<b>c</b>) and sealing threshold of fault rock (<b>d</b>) with burial depth, where points <span class="html-italic">A</span>, <span class="html-italic">B</span> and <span class="html-italic">C</span> are typical points of faults, <span class="html-italic">K_A</span>, <span class="html-italic">K_B</span> and <span class="html-italic">K_C</span> are the permeability of each points, <span class="html-italic">Q_A</span>, <span class="html-italic">Q_B</span> and <span class="html-italic">Q_C</span> are the diagenetic degree of each points, <span class="html-italic">h_A</span>, <span class="html-italic">h_B</span> and <span class="html-italic">h_C</span> are the burial depth of points <span class="html-italic">A</span>, <span class="html-italic">B</span> and <span class="html-italic">C</span>, <span class="html-italic">h_ddp</span> is the burial depth of depth demarcation point.</p> Full article ">Figure 4
<p>Flow diagram of the determination of the SGR&amp;H threshold. (<b>a</b>) SGR&amp;H attributes of the fault rock. (<b>b</b>) Evaluation template and SGR&amp;H threshold for fault sealing. (<b>c</b>) Determination of faults’ lateral sealing properties.</p> Full article ">Figure 5
<p>(<b>a</b>) Typical geological section of faults in J₂n Formation near Miandu-Zhadun River of the Hailar Basin. (<b>b</b>) Plot of fault rock thickness versus displacement for the different modes of faulting [<a href="#B7-resources-12-00098" class="html-bibr">7</a>]. (<b>c</b>) Fault displacement of typical wells in K₁n Formation of different areas of Hailar Basin.</p> Full article ">Figure 6
<p>Determination of the SGR&amp;H threshold of the faults in different areas and layers of the Hailar Basin. (<b>a</b>) Beixi area, which includes the Huhenuoren, Suderte, and Huoduomoer blocks. (<b>b</b>) Beier area. (<b>c</b>) Wuerxun area. (<b>d</b>) Surennuoer area.</p> Full article ">Figure 7
<p>Map of the lateral sealing ability of the B3 Fault in the Hailar Basin. (<b>a</b>) SGR value of the B3 Fault. (<b>b</b>) Composite columnar section of well B301. (<b>c</b>) Reservoir profile across well B301.</p> Full article ">Figure 8
<p>Map of the lateral sealing ability of the B70 Fault in the Hailar Basin. (<b>a</b>) SGR attributes of the B70 Fault and the relationship between the hydrocarbon discovery with the effective/original trap scope. (<b>b</b>) Seismic profile AA’ shown in <a href="#resources-12-00098-f007" class="html-fig">Figure 7</a>a.</p> Full article ">Figure 9
<p>Analysis of the target areas for Blocks SX1011-S1012 in the Hailar Basin. (<b>a</b>) The location of the target block. (<b>b</b>) Inversion profile BB’ in <a href="#resources-12-00098-f010" class="html-fig">Figure 10</a>a. (<b>c</b>) SGR attributes of the F4 Fault.</p> Full article ">Figure 10
<p>Statistical map of the RSF in the K₁n Formation in different areas of the Hailar Basin.</p> Full article ">

20 pages, 4374 KiB  

Open AccessArticle

Geological Characteristics and Challenges of Marine Shale Gas in the Southern Sichuan Basin

by Shasha Sun, Shiwei Huang, Feng Cheng, Wenhua Bai and Zhaoyuan Shao

Energies 2023, 16(15), 5796; https://doi.org/10.3390/en16155796 - 4 Aug 2023

Cited by 2 | Viewed by 1187

Abstract

After more than 10 years of exploration, development, research, and practical efforts, China has opened up new perspectives for the commercial exploitation of marine shale gas. While high shale gas production is a main driver for energy security and economic development in China, [...] Read more.

After more than 10 years of exploration, development, research, and practical efforts, China has opened up new perspectives for the commercial exploitation of marine shale gas. While high shale gas production is a main driver for energy security and economic development in China, there have been few attempts to systemically scientific analysis the challenges, prospect, development strategies, and goals for shale gas. Here, we present a detailed comparison of the differences in shale gas between the Sichuan Basin and North America from multiple dimensions, explain how and to what extent recent advances have been made, discuss the current challenges, and provide strategies to deal with these challenges. We demonstrate that a total of 13 graptolite zones developed in the Wufeng–Longmaxi Formations, achieved by representative cores from 32 coring wells and 7 outcrop profiles, can establish the chronostratigraphic framework in the Sichuan Basin, which leads to the potential impact of high-quality reservoir distribution and shale gas production. Shale gas is still faced with the challenges of complex underground and surface conditions, low single-well EUR, and immature deep development engineering technology. To circumvent these issues, here, we propose several strategies, including sweet-spot optimization, low-cost drilling techniques, and efficient fracturing technologies. Our results strengthen the importance of adopting fundamental theoretical research and practical and feasible development goals to realize more commercial discoveries of shale gas of diverse types and higher growth of shale gas reserves and production. Full article

(This article belongs to the Topic Petroleum and Gas Engineering)

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Figure 1
<p>Cumulative histogram of China’s shale gas production in recent years.</p> Full article ">Figure 2
<p>Geological settings of shale gas play in Sichuan basin revised by reference [<a href="#B15-energies-16-05796" class="html-bibr">15</a>]. Reprint with permission from Ref. [<a href="#B15-energies-16-05796" class="html-bibr">15</a>]. 2021, Guangzhao Zhou.</p> Full article ">Figure 3
<p>Chronostratigraphic division of the Wufeng–Longmaxi black shales in South China revised by reference [<a href="#B17-energies-16-05796" class="html-bibr">17</a>]. (<b>a</b>). Strata of Paleozoic and Mesozoic; (<b>b</b>). Strata of Wufeng-Longmaxi Formation. Reprint with permission from Ref. [<a href="#B17-energies-16-05796" class="html-bibr">17</a>]. 2017, Caineng Zou.</p> Full article ">Figure 4
<p>Graptolite zones of the Wufeng–Longmaxi black shales in South China [<a href="#B12-energies-16-05796" class="html-bibr">12</a>,<a href="#B13-energies-16-05796" class="html-bibr">13</a>]. Reprint with permission from Refs. [<a href="#B12-energies-16-05796" class="html-bibr">12</a>,<a href="#B13-energies-16-05796" class="html-bibr">13</a>]. 2017, Xu Chen, 2020, Hongyan Wang.</p> Full article ">Figure 5
<p>The parameters of shale gas in Longmaxi Formation. Reprint with permission from Ref. [<a href="#B14-energies-16-05796" class="html-bibr">14</a>]. 2022, Zhensheng Shi.</p> Full article ">Figure 6
<p>Black shale thickness map of the Longmaxi Formation in South China. Reprint with permission from Ref. [<a href="#B19-energies-16-05796" class="html-bibr">19</a>]. 2021, Zhensheng Shi.</p> Full article ">Figure 7
<p>Sedimentary facies model of the Wufeng–Longmaxi Formation in Sichuan Basin]. Reprint with permission from Ref. [<a href="#B22-energies-16-05796" class="html-bibr">22</a>]. 2023, Hongyan Wang.</p> Full article ">

28 pages, 6766 KiB  

Open AccessArticle

A Novel Efficient Borehole Cleaning Model for Optimizing Drilling Performance in Real Time

by Mohammed Al-Rubaii, Mohammed Al-Shargabi, Dhafer Al-Shehri, Abdullah Alyami and Konstantin M. Minaev

Appl. Sci. 2023, 13(13), 7751; https://doi.org/10.3390/app13137751 - 30 Jun 2023

Cited by 8 | Viewed by 2163

Abstract

The drilling industry has evolved significantly over the years, with new technologies making the process more efficient and effective. One of the most crucial issues of drilling is borehole cleaning, which entails removing drill cuttings and keeping the borehole clean. Inadequate borehole cleaning [...] Read more.

The drilling industry has evolved significantly over the years, with new technologies making the process more efficient and effective. One of the most crucial issues of drilling is borehole cleaning, which entails removing drill cuttings and keeping the borehole clean. Inadequate borehole cleaning can lead to drilling problems such as stuck pipes, poor cementing, and formation damage. Real-time drilling evaluation has seen significant improvements, allowing drilling engineers to monitor the drilling process and make adjustments accordingly. This paper introduces a novel real-time borehole cleaning performance evaluation model based on the transport index (TI_m). The novel TI_m model offers a real-time indication of borehole cleaning efficiency. The novel model was field-tested and validated for three wells, demonstrating its ability to determine borehole cleaning efficiency in typical drilling operations. Using TI_m in Well-A led to a 56% increase in the rate of penetration (ROP) and a 44% reduction in torque. Moreover, the efficient borehole cleaning obtained through the use of TI_m played a significant role in improving drilling efficiency and preventing stuck pipes incidents. The TI_m model was also able to identify borehole cleaning efficiency during a stuck pipe issue, highlighting its potential use as a tool for optimizing drilling performance. Full article

(This article belongs to the Topic Petroleum and Gas Engineering)

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Figure 1

Figure 1
<p>The flowchart outlining the various topics discussed and the systematic order in which they are presented.</p> Full article ">Figure 2
<p>Parameters and their effect on borehole cleaning.</p> Full article ">Figure 3
<p>Influence of rotation on cuttings bed; (<b>a</b>) low RPM; (<b>b</b>) medium RPM; (<b>c</b>) at 120 RPM.</p> Full article ">Figure 4
<p>Read of maximum ROP based on the TI values and the borehole angle. Chart is based on Luo’s chart [<a href="#B37-applsci-13-07751" class="html-bibr">37</a>].</p> Full article ">Figure 5
<p>Graphical chart for the <span class="html-italic">RF</span> in borehole size 17–12″. Chart is based on Luo’s chart [<a href="#B38-applsci-13-07751" class="html-bibr">38</a>].</p> Full article ">Figure 6
<p>The interpolation of AF based on the borehole angle.</p> Full article ">Figure 7
<p>The measured, calculated, and output of the novel model <span class="html-italic">TI_m</span>, which is a real-time automated assessment for evaluating borehole cleaning conditions.</p> Full article ">Figure 8
<p>The performance of TI_m in relation to three key parameters: (<b>a</b>) MW_eff, (<b>b</b>) ECD, and (<b>c</b>) modified LC_m.</p> Full article ">Figure 9
<p>The performance of TI_m in relation to three key parameters: (<b>a</b>) LSYP, (<b>b</b>) LSYP/YP, (<b>c</b>) modified n_em, and (<b>d</b>) modified k_em.</p> Full article ">Figure 10
<p>Flowchart to estimate the novel TI_m model in real time.</p> Full article ">Figure 11
<p>Application of TI_m in offshore Well-A with proper borehole cleaning (<b>a</b>) and Well-B with poor borehole cleaning (<b>b</b>).</p> Full article ">Figure 12
<p>The changes in drilling parameters for Well-A with proper borehole cleaning and Well-B with poor borehole cleaning: (<b>a</b>) WOB, (<b>b</b>) SPP, (<b>c</b>) TRQ, and (<b>d</b>) ROP.</p> Full article ">Figure 13
<p>Application of TI_m in Well C in the case of a stuck pipe: (<b>a</b>) Well-C with poor borehole cleaning, (<b>b</b>) ROP, and (<b>c</b>) TRQ.</p> Full article ">Figure 14
<p>The automated process of using field data to evaluate the status of hole cleaning using TI_m for optimizing the drilling performance efficiency.</p> Full article ">

19 pages, 3680 KiB  

Open AccessArticle

A Novel Automated Model for Evaluation of the Efficiency of Hole Cleaning Conditions during Drilling Operations

by Mohammed Al-Rubaii, Mohammed Al-Shargabi and Dhafer Al-Shehri

Appl. Sci. 2023, 13(11), 6464; https://doi.org/10.3390/app13116464 - 25 May 2023

Cited by 10 | Viewed by 3021

Abstract

Hole cleaning for the majority of vertical and directional drilling wells continues to be a substantial difficulty despite improvements in drilling fluids, equipment, field techniques, and academic and industrial research. Poor hole cleaning might cause issues such as stuck pipe incidents, drilling cuttings [...] Read more.

Hole cleaning for the majority of vertical and directional drilling wells continues to be a substantial difficulty despite improvements in drilling fluids, equipment, field techniques, and academic and industrial research. Poor hole cleaning might cause issues such as stuck pipe incidents, drilling cuttings accumulation, torque and drag, the erratic equivalent circulating density in the annulus, wellbore instability, tight spots, and hole condition issues. In order to enable the real-time and automated evaluation of hole cleaning efficiency for vertical and directional drilling, the article’s objective is to develop a novel model for the cutting transport ratio (${\mathrm{C}\mathrm{T}\mathrm{R}}_{\mathrm{m}}$) that can be incorporated into drilling operations on a real-time basis. The novel ${\mathrm{C}\mathrm{T}\mathrm{R}}_{\mathrm{m}}$ model provides a robust indicator for hole cleaning, which can assess complications and enhance drilling efficiency. Moreover, the novel ${\mathrm{C}\mathrm{T}\mathrm{R}}_{\mathrm{m}}$ model was successfully tested and validated in the field for four wells. The results of the real-time evaluation showed that the novel model was capable of identifying the hole cleaning efficiency in a normal drilling performance for Well-C and a stuck pipe issue in Well-D. In addition, the novel ${\mathrm{C}\mathrm{T}\mathrm{R}}_{\mathrm{m}}$ improved the rate of penetration by 52% in Well-A in comparison to Well-B. Full article

(This article belongs to the Topic Petroleum and Gas Engineering)

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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Schematic of transport cutting: (<b>a</b>) shows the transport of cuttings in a horizontal well, and (<b>b</b>) shows the difficulty of hole cleaning in relation to inclination.</p> Full article ">Figure 2
<p>Cutting transport mechanism for different ranges of zenith angle at different speeds in the annulus.</p> Full article ">Figure 3
<p>The use of iterative steady-state modelling with real-time parameters as inputs while drilling in real-time.</p> Full article ">Figure 4
<p>The input and output of the novel model CTR_m as a real-time evaluation for indicating the hole cleaning condition.</p> Full article ">Figure 5
<p>Application of CTR_m in the offshore proper hole cleaning of Well-A (<b>a</b>) and the poor hole cleaning of Well-B (<b>b</b>).</p> Full article ">Figure 6
<p>Application of CTR_m in the offshore gas vertical Well-C (<b>a</b>) and the horizontal Well-B (<b>b</b>).</p> Full article ">Figure A1
<p>The Methodology of the Novel Model CTR_m.</p> Full article ">

14 pages, 4350 KiB  

Open AccessArticle

Study on the Compressive and Tensile Properties of Gneiss Outcrop of Bozhong 196 Gas Field in China

by Lianzhi Yang, Tong Niu, Fanmin He and Zhiyong Song

Energies 2023, 16(9), 3919; https://doi.org/10.3390/en16093919 - 6 May 2023

Cited by 1 | Viewed by 1603

Abstract

In this paper, based on the gneiss outcrop of Bozhong 196 gas field in China, uniaxial compression and Brazil splitting tests were conducted by using cores of different orientations. The following compression properties were studied: the elastic compression modulus, Poisson’s ratio and compressive [...] Read more.

In this paper, based on the gneiss outcrop of Bozhong 196 gas field in China, uniaxial compression and Brazil splitting tests were conducted by using cores of different orientations. The following compression properties were studied: the elastic compression modulus, Poisson’s ratio and compressive strength of the gneiss outcrop. The following tensile properties were studied: the tensile modulus, the tensile strength and peak energy rate of gneiss outcrop. The results demonstrate the following: (1) The elastic compression modulus, compressive strength, tensile strength and peak energy rate of gneiss specimens with horizontal core-taking are greater than those with vertical core-taking. (2) The elastic compression modulus, Poisson’s ratio and compressive strength of horizontally cored gneiss specimens are 29.688–45.760 GPa, 0.186–0.386, and 174.94–147.80 MPa, respectively; the elastic compression modulus, Poisson’s ratio and compressive strength of the vertical gneiss specimens are 26.541–32.602 GPa, 0.429–0.476 and 169.37–134.46 MPa. (3) The tensile modulus of the horizontal gneiss specimens is 4.93–5.98 GPa. The tensile modulus of the vertical gneiss specimens is 0.96–2.11 GPa. The tensile modulus of the horizontal gneiss specimens is five times that of the vertical gneiss specimens. The elastic compression modulus of gneiss is 5–20 times that of the tensile modulus. (4) The tensile strength and peak energy rate of horizontally cored gneiss specimens are 14.33–17.55 MPa and 2598.67–4049.53 J/m², respectively. The tensile strength of the vertical gneiss specimens is 6.12–9.65 MPa, and the peak energy rate is 715.74–1515.30 J/m². (5) There is a good linear relationship between the peak energy rate and tensile strength of gneiss. The research results can provide a scientific and reasonable reference for in situ fracturing of Bozhong 196 gas field. Full article

(This article belongs to the Topic Petroleum and Gas Engineering)

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Figure 1
<p>Uniaxial compression test by TAR-1500 Rock mechanics test system.</p> Full article ">Figure 2
<p>Brazilian splitting test by TAR-1500 Rock mechanics test system.</p> Full article ">Figure 3
<p>Baijiafen gneiss sampling site and processing of specimens. (<b>a</b>) Baijiafen gneiss sampling site (<b>b</b>) Gneiss outcrop of Bozhong 196 gas field (<b>c</b>) Processing of specimens.</p> Full article ">Figure 4
<p>Stress–strain curves of horizontal gneiss specimens.</p> Full article ">Figure 5
<p>Stress–strain curves of vertical gneiss specimens.</p> Full article ">Figure 6
<p>The gneiss specimens after compression. (The red lines are cracks).</p> Full article ">Figure 7
<p>The gneiss specimens after splitting.</p> Full article ">Figure 8
<p>Load–vertical displacement curves of horizontal gneiss specimens by Brazil splitting tests.</p> Full article ">Figure 9
<p>Load–vertical displacement curves of vertical gneiss specimens by Brazil splitting tests.</p> Full article ">Figure 10
<p>Relationship between tensile strength and peak energy rate of horizontal gneiss specimens.</p> Full article ">Figure 11
<p>Relationship between tensile strength and peak energy rate of vertical gneiss specimens.</p> Full article ">

17 pages, 867 KiB  

Open AccessReview

State of the Art of Oil and Gas Pipeline Vulnerability Assessments

by Han Zhang, Qingshan Feng, Bingchuan Yan, Xianbin Zheng, Yue Yang, Jian Chen, Hong Zhang and Xiaoben Liu

Energies 2023, 16(8), 3439; https://doi.org/10.3390/en16083439 - 13 Apr 2023

Cited by 4 | Viewed by 2482

Abstract

In recent years, the safety of oil and gas pipelines has become a primary concern for the pipeline industry. This paper presents a comprehensive study of the vulnerability concepts that may be used to measure the safety status of pipeline systems. The origins [...] Read more.

In recent years, the safety of oil and gas pipelines has become a primary concern for the pipeline industry. This paper presents a comprehensive study of the vulnerability concepts that may be used to measure the safety status of pipeline systems. The origins of the vulnerability concepts are identified, the development and evolution of the vulnerability concepts are described, and the main connotations of the four levels of vulnerability concepts applied in different fields at this stage are summarized. Qualitative and quantitative methods of vulnerability assessment are comprehensively investigated, and the advantages and disadvantages, scope of application and key issues faced are compared and summarized. The research and analysis show that the vulnerability assessment of oil and gas pipelines is at a preliminary stage, and there is an urgent demand to establish a unified vulnerability concept and assessment system for oil and gas pipeline systems. The current qualitative or semi-quantitative assessment of pipeline vulnerability research lacks reasonable and scientific standards and bases for the classification of indicators and the determination of indicator scores, and it needs to focus on the establishment and improvement of quantitative assessment models. Full article

(This article belongs to the Topic Petroleum and Gas Engineering)

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Figure 1
<p>Diagram of the three elements of vulnerability.</p> Full article ">Figure 2
<p>Wetland vulnerability assessment indicator system [<a href="#B46-energies-16-03439" class="html-bibr">46</a>].</p> Full article ">Figure 3
<p>Distribution of vulnerability to the dual exposure to climate change and globalization in India [<a href="#B74-energies-16-03439" class="html-bibr">74</a>].</p> Full article ">

23 pages, 11005 KiB  

Open AccessArticle

Predicting Reservoir Petrophysical Geobodies from Seismic Data Using Enhanced Extended Elastic Impedance Inversion

by Eko Widi Purnomo, Abdul Halim Abdul Latiff and Mohamed M. Abdo Aly Elsaadany

Appl. Sci. 2023, 13(8), 4755; https://doi.org/10.3390/app13084755 - 10 Apr 2023

Cited by 4 | Viewed by 2985

Abstract

The study aims to implement a high-resolution Extended Elastic Impedance (EEI) inversion to estimate the petrophysical properties (e.g., porosity, saturation and volume of shale) from seismic and well log data. The inversion resolves the pitfall of basic EEI inversion in inverting below-tuning seismic [...] Read more.

The study aims to implement a high-resolution Extended Elastic Impedance (EEI) inversion to estimate the petrophysical properties (e.g., porosity, saturation and volume of shale) from seismic and well log data. The inversion resolves the pitfall of basic EEI inversion in inverting below-tuning seismic data. The resolution, dimensionality and absolute value of basic EEI inversion are improved by employing stochastic perturbation constrained by integrated energy spectra attribute in a Bayesian Markov Chain Monte Carlo framework. A general regression neural network (GRNN) is trained to learn and memorize the relationship between the stochastically perturbed EEI and the associated well petrophysical log data. The trained GRNN is then used to predict the petrophysical properties of any given stochastic processed EEI. The proposed inversion was successfully conducted to invert the volume of shale, porosity and water saturation of a 4.0 m thick gas sand reservoir in Sarawak Basin, Malaysia. The three petrophysical geobodies were successfully built using the discovery wells cut-off values, showing that the inverted petrophysical properties satisfactorily reconstruct the well petrophysical logs with sufficient resolution and an accurate absolute value at the well site and are laterally conformable with seismic data. Inversion provides reliable petrophysical properties prediction that potentially helps further reservoir development for the study field. Full article

(This article belongs to the Topic Petroleum and Gas Engineering)

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Figure 1
<p>Study Field, (<b>a</b>) Field location, (<b>b</b>) Schematic diagram showing the major SC3 reservoir depositional model in study area, (<b>c</b>) 3D seismic survey grid showing three well locations and seismic time slice at 1000 ms two-way time depth, (<b>d</b>) Seismic survey map view showing the position of three available wells.</p> Full article ">Figure 2
<p>Petroleum system and seismic data interpretation. (<b>a</b>) Generalized stratigraphy of the studied province showing the major hydrocarbon occurrences (Madon and Abolins 1999), (<b>b</b>) Regional E–W seismic section showing gas generation occurred from source rock at depth (Cycle 1 and 2) and migrated to the Field’s sands (SC3) by deep rooted faults. (<b>c</b>) NW–SE seismic section and seismic horizons interpretation of studied zone. Overlaid log is well site seismic trace (Courtesy Petronas Carigali).</p> Full article ">Figure 3
<p>Seismic data input and seismic-to-well tie. Near (<b>a</b>) and Far (<b>b</b>) Seismic section of the study field showing strong AVO anomaly. Black curves are Gamma Ray logs from (left to right) Well three, Well one and Well two. The markers show thick upper reservoir, SC3, and thin lower reservoir, SC3A at each wells site. (<b>c</b>) Well one seismic to well tie. Red curve is extracted wavelet; blue is a 45 Hz Ricker wavelet (Courtesy of Petronas Carigali).</p> Full article ">Figure 4
<p>Petrophysical (porosity PHIE, water saturation SWE and volume of shale VSH) logs data from Well 1, Well 2 and Well 3 are used for inversion. Two yellow dashed-line rectangles locate two interested gas reservoirs.</p> Full article ">Figure 5
<p>Workflow of inversion.</p> Full article ">Figure 6
<p>The EEI inversion correlation coefficient versus χ angle cross-plot.</p> Full article ">Figure 7
<p>Stochastic process of EEI porosity enhancement. (<b>a</b>) porosity log reflectivity; (<b>b</b>) EEI porosity signature at Well-1 location; (<b>c</b>) initial prior EEI reflectivity extracted from EEI signature; (<b>d</b>) applied wavelet statistically extracted from EEI signature; (<b>e</b>) 50 prior perturbed EEI reflectivity models (thin colorful curves) overlaid by its associated posterior (thick blue curve) and log porosity reflectivity (thick red curve); (<b>f</b>) 50 prior EEI signature models, associated with the 50 EEI prior reflectivity (<b>e</b>), overlaid by its associated posterior EEI signature (thick blue curve) and the observed EEI signature (thick red curve); (<b>g</b>) 50 EEI reflectivity posterior models collected from 50 independent Bayesian-MCMC simulations; (<b>h</b>) 50 EEI signature posterior models associated with the 50 EEI reflectivity prior models (<b>f</b>).</p> Full article ">Figure 8
<p>The GRNN training diagram built using MATLAB built-in <span class="html-italic">newgrnn</span> function; (<b>a</b>) 1 neuron input layer; (<b>b</b>) 188 neurons radial basis layer; (<b>c</b>) 1 neuron summation layer; and (<b>d</b>) 1 neuron output layer.</p> Full article ">Figure 9
<p>Effect of smoothing factor (σ) to the petrophysical prediction accuracy (R²), Porosity prediction (1st row), water saturation prediction (2nd row), and volume of shale (3rd row).</p> Full article ">Figure 10
<p>Cross-plot between accuracy R² (R-squared) and smoothing factor (sigma) of porosity, water saturation and volume of shale prediction for <span class="html-italic">σ</span> (sigma) value scanned between 0 and 2.</p> Full article ">Figure 11
<p>Porosity inversions at Well 1 location. (<b>a</b>) porosity log, (<b>b</b>) near seismic trace, (<b>c</b>) Far seismic trace, (<b>d</b>) traditional inverted EEI porosity, (<b>e</b>) porosity log (red) overlaid by proposed inverted EEI porosity (blue). “SC3”, “SC3A”, consecutively, locate the anomalous thick and thin gas reservoir sand and “Water sand” locates the deeper water sand.</p> Full article ">Figure 12
<p>Porosity estimation at well location, (<b>a</b>) Well 3 (blind), (<b>b</b>) Well 1 (control) and (<b>c</b>) Well 2 (blind).</p> Full article ">Figure 13
<p>Cross-plots of estimate and real porosity log for (<b>a</b>) Well-3 (blind), (<b>b</b>) Well-1 (control), and (<b>c</b>) Well-2 (blind). Red lines are the best fit line between the real and estimated porosity.</p> Full article ">Figure 14
<p>Overlay (upper row) and cross plots (lower row) of estimate and real water saturation at well locations. From left to right: (first column) Well 3 (blind), (second column) Well 1 (control), (third column) Well 2 (blind). Both the estimated and real data are smoothed. Red lines in the cross plot are the best line between the real and estimated water saturation.</p> Full article ">Figure 15
<p>Overlay (upper row) and cross plots (lower row) of the estimated and real volume of shale at well locations. From left to right: (first column) Well 3 (blind), (second column) Well 1 (control), (third column) Well 2 (blind). Both estimated and real data are smoothed. Red lines in the cross plot are the best line between the real and estimated volume of shale.</p> Full article ">Figure 16
<p>Inverted porosity inline cross section passing near Well 1, Well 2 and Well 3 location. Well 2 and Well 3 are blind wells.</p> Full article ">Figure 17
<p>Petrophysical geobody constructed using discovery well cutoff value, Volume of shale &lt;70% (top), porosity &gt;12% (middle), water saturation &lt;70% (bottom). SC3 and SC3A are, consecutively, thick and thin reservoir gas sand.</p> Full article ">Figure 18
<p>Individual geobody of estimate water saturation of thin gas sand of SC3A pay showing structural confirmation with seismic data.</p> Full article ">

29 pages, 4459 KiB  

Open AccessReview

Extended-Reach Drilling (ERD)—The Main Problems and Current Achievements

by Karim El Sabeh, Nediljka Gaurina-Međimurec, Petar Mijić, Igor Medved and Borivoje Pašić

Appl. Sci. 2023, 13(7), 4112; https://doi.org/10.3390/app13074112 - 23 Mar 2023

Cited by 10 | Viewed by 9152

Abstract

With the development of different segments within the drilling technology in the last three decades, well drilling has become possible in harsh downhole conditions. The vertical well provides access to oil and gas reserves located at a certain depth directly below the wellsite, [...] Read more.

With the development of different segments within the drilling technology in the last three decades, well drilling has become possible in harsh downhole conditions. The vertical well provides access to oil and gas reserves located at a certain depth directly below the wellsite, and a large number of vertical wells are required for the exploitation of hydrocarbons from spatially expanded deposits. However, the borehole can deviate from the vertical well, which means that the target zone can be reached by a horizontal directional well. With this type of well, especially in the case of drilling an extended-reach well (ERW), the length of the wellbore in contact with the reservoir and/or several separate reservoirs is significantly increased, therefore, it is a much better option for the later production phase. Unfortunately, the application of extended-reach drilling (ERD technology), with all of its advantages, can cause different drilling problems mostly related to the increased torque, drag, hole cleaning and equivalent circulation density (ECD), as well as to an increase in the well price. Overcoming these problems requires continuous operational change to enable operators to address downhole challenges. Today, the longest well reaches 15,240 m (50,000 ft), which raises the question of the technological and economic feasibility of this type of drilling project, especially with the lower oil price on the energy market. This paper provides a comprehensive overview of extended-reach drilling technology, discusses the main problems and analyzes current achievements. Full article

(This article belongs to the Topic Petroleum and Gas Engineering)

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Figure 1
<p>Different well types (Reprinted with permission from Ref. [<a href="#B2-applsci-13-04112" class="html-bibr">2</a>]. 2023, Croatian Academy of Engineering).</p> Full article ">Figure 2
<p>The ratio of measured depth vs. the true vertical depth and reach for wells such as BD-04, M-16, OP-11 and Z-44 and other wells presented in Table 2.</p> Full article ">Figure 3
<p>Hole cleaning in extended-reach wells (cleaning zones (<b>a</b>), influence of gravity and hole inclination (<b>b</b>) and recommendations for improving (<b>c</b>)).</p> Full article ">Figure 4
<p>Hydroclean tools: (<b>a</b>) first-generation tool (G1) and (<b>b</b>) second-generation tool (G2) (Reprinted with permission from Ref. [<a href="#B66-applsci-13-04112" class="html-bibr">66</a>]. 2023, Society of Petroleum Engineers”).</p> Full article ">Figure 5
<p>The ratio between measured depth (MD) and true vertical depth (TVD) of 26 analyzed wells.</p> Full article ">Figure 6
<p>True vertical depth of the analyzed wells.</p> Full article ">Figure 7
<p>Measured depth of 30 analyzed wells.</p> Full article ">Figure 8
<p>Outside diameter of conductor string installed in 10 analyzed wells.</p> Full article ">Figure 9
<p>Setting depth of conductor strings installed in 10 analyzed wells.</p> Full article ">Figure 10
<p>Diameter of intermediate casing I in the 24 analyzed wells.</p> Full article ">Figure 11
<p>Diameter of intermediate casing II in 8 analyzed wells.</p> Full article ">Figure 12
<p>Diameter of production casing/production liner in 21 analyzed wells.</p> Full article ">

18 pages, 5466 KiB  

Open AccessArticle

Fractal-Based Approaches to Pore Structure Investigation and Water Saturation Prediction from NMR Measurements: A Case Study of the Gas-Bearing Tight Sandstone Reservoir in Nanpu Sag

by Weibiao Xie, Qiuli Yin, Jingbo Zeng, Guiwen Wang, Cheng Feng and Pan Zhang

Fractal Fract. 2023, 7(3), 273; https://doi.org/10.3390/fractalfract7030273 - 21 Mar 2023

Cited by 9 | Viewed by 1746

Abstract

Pore space of tight sandstone samples exhibits fractal characteristics. Nuclear magnetic resonance is an effective method for pore size characterization. This paper focuses on fractal characteristics of pore size from nuclear magnetic resonance (NMR) of tight sandstone samples. The relationship between the fractal [...] Read more.

Pore space of tight sandstone samples exhibits fractal characteristics. Nuclear magnetic resonance is an effective method for pore size characterization. This paper focuses on fractal characteristics of pore size from nuclear magnetic resonance (NMR) of tight sandstone samples. The relationship between the fractal dimension from NMR with pore structure and water saturation is parameterized by analyzing experimental data. Based on it, a pore structure characterization and classification method for water-saturated tight sandstone and a water saturation prediction method in a gas-bearing sandstone reservoir have been proposed. To verify the models, the fractal dimension from NMR of 19 tight sandstone samples selected from the gas-bearing tight sandstone reservoir of Shahejie Formation in Nanpu Sag and that of 16 of them under different water saturation states are analyzed. The application result of new methods in the gas-bearing tight sandstone reservoir of Shahejie Formation in Nanpu Sag shows consistency with experimental data. This paper has facilitated the development of the NMR application by providing a non-electrical logging idea in reservoir quality evaluation and water saturation prediction. It provides a valuable scientific resource for reservoir engineering and petrophysics of unconventional reservoir types, such as tight sandstone, low porosity, and low permeability sandstone, shale, and carbonate rock reservoirs. Full article

(This article belongs to the Topic Petroleum and Gas Engineering)

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<p>Influence of gas filling on pore structure from NMR. (<b>A</b>) Water-saturated rock; (<b>D</b>) Equivalent pore structure model of (<b>A</b>) from NMR; (<b>B</b>) The maximum pore in (<b>A</b>) is filling with gas; (<b>E</b>) Equivalent pore structure model of (<b>B</b>) from NMR; (<b>C</b>) Gas filling in the pores bigger than the (<span class="html-italic">i</span> − 1)-th largest pore; (<b>F</b>) Equivalent pore structure model of (<b>C</b>) from NMR.</p> Full article ">Figure 2
<p>T₂ curves of 19 rock samples and pore structure classification.</p> Full article ">Figure 3
<p><math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mrow> <mi>v</mi> <mi>a</mi> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mrow> <mi>v</mi> <mi>b</mi> </mrow> </msub> </mrow> </semantics></math> distribution of Type A–E.</p> Full article ">Figure 4
<p>Pore structure classification based on <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mrow> <mi>v</mi> <mi>a</mi> </mrow> </msub> </mrow> </semantics></math>–<math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mrow> <mi>v</mi> <mi>b</mi> </mrow> </msub> </mrow> </semantics></math> cross plot.</p> Full article ">Figure 5
<p>T₂ spectra under different water saturation states of a representative rock sample of each pore structure type.</p> Full article ">Figure 5 Cont.
<p>T₂ spectra under different water saturation states of a representative rock sample of each pore structure type.</p> Full article ">Figure 6
<p>Relationship between <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <msub> <mi>D</mi> <mrow> <mi>v</mi> <mi>a</mi> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>S</mi> <mi>w</mi> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 7
<p>Evaluation result of new methods in Well ×5.</p> Full article ">Figure 8
<p>Corresponding relationship between T₂ spectra and pore space.</p> Full article ">Figure 9
<p>Comparison between the T₂ curve derived fractal dimension and the calculated fractal dimension by Equations (15) and (16).</p> Full article ">Figure 10
<p><math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mi>v</mi> </msub> </mrow> </semantics></math>—Swir relationship (<math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mrow> <mi>v</mi> <mi>a</mi> </mrow> </msub> </mrow> </semantics></math>, Swir) and (<math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mrow> <mi>v</mi> <mi>b</mi> </mrow> </msub> </mrow> </semantics></math>, Swir) are marked by red spot and black square, respectively.</p> Full article ">Figure 11
<p><math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mi>v</mi> </msub> </mrow> </semantics></math>—T_2lm relationship (<math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mrow> <mi>v</mi> <mi>a</mi> </mrow> </msub> </mrow> </semantics></math>, T_2lm) and (<math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mrow> <mi>v</mi> <mi>b</mi> </mrow> </msub> </mrow> </semantics></math>, T_2lm) are marked by red spot and black square, respectively.</p> Full article ">

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