Nothing Special   »   [go: up one dir, main page]

Huang et al., 2021 - Google Patents

Quantitative analysis of the main controlling factors of oil saturation variation

Huang et al., 2021

View PDF @Full View
Document ID
5938375689385832295
Author
Huang R
Wei C
Yang J
Xu X
Li B
Wu S
Xiong L
Publication year
Publication venue
Geofluids

External Links

Snippet

With the high‐speed development of artificial intelligence, machine learning methods have become key technologies for intelligent exploration, development, and production in oil and gas fields. This article presents a workflow analysing the main controlling factors of oil …
Continue reading at onlinelibrary.wiley.com (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V99/00—Subject matter not provided for in other groups of this subclass
    • G01V99/005—Geomodels or geomodelling, not related to particular measurements
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
    • G01N33/26—Investigating or analysing materials by specific methods not covered by the preceding groups oils; viscous liquids; paints; inks
    • G01N33/28—Oils, i.e. hydrocarbon liquids
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50—Computer-aided design
    • G06F17/5009—Computer-aided design using simulation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/60—Analysis
    • G01V2210/66—Subsurface modeling
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V11/00—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS prospecting or detecting by methods combining techniques covered by two or more of main groups G01V1/00 - G01V9/00

Similar Documents

Publication Publication Date Title
Thanh et al. Knowledge-based machine learning techniques for accurate prediction of CO2 storage performance in underground saline aquifers
Liu et al. Permeability predictions for tight sandstone reservoir using explainable machine learning and particle swarm optimization
Tadeusiewicz Neural networks in mining sciences–general overview and some representative examples
Zhou et al. Fast prediction of reservoir permeability based on embedded feature selection and LightGBM using direct logging data
Liu et al. NMR log response prediction from conventional petrophysical logs with XGBoost-PSO framework
Liu et al. Artificial intelligence method for shear wave travel time prediction considering reservoir geological continuity
Cao et al. Extreme learning machine for reservoir parameter estimation in heterogeneous sandstone reservoir
Aliyuda et al. Machine-learning algorithm for estimating oil-recovery factor using a combination of engineering and stratigraphic dependent parameters
Aifa Neural network applications to reservoirs: Physics-based models and data models
Niu et al. Development of visual prediction model for shale gas wells production based on screening main controlling factors
Zeng et al. Selective methods of TOC content estimation for organic-rich interbedded mudstone source rocks
Huang et al. Prediction and optimization of WAG flooding by using LSTM neural network model in Middle East carbonate reservoir
Wei et al. Characterization of saturation and pressure distribution based on deep learning for a typical carbonate reservoir in the Middle East
Gao et al. Prediction of fracture density in a gas reservoir using robust computational approaches
Wang et al. Quantitative evaluation of unconsolidated sandstone heavy oil reservoirs based on machine learning
Huang et al. Quantitative analysis of the main controlling factors of oil saturation variation
Lu et al. Lithology Logging Recognition Technology Based on GWO‐SVM Algorithm
Safaei-Farouji et al. An advanced computational intelligent framework to predict shear sonic velocity with application to mechanical rock classification
CN114741944A (en) Method for predicting logging curve by using machine learning and deep learning algorithm
Guo et al. A new method for fracturing wells reservoir evaluation in fractured gas reservoir
Ashraf et al. Identifying payable cluster distributions for improved reservoir characterization: a robust unsupervised ML strategy for rock typing of depositional facies in heterogeneous rocks
Ji et al. Fracture identification of carbonate reservoirs by deep forest model: An example from the D oilfield in Zagros Basin
Guo et al. Evaluation of Coalbed Methane Content by Using Kernel Extreme Learning Machine and Geophysical Logging Data
Cheng et al. Petroleum lithology discrimination based on PSO-LSSVM classification model
Dong et al. Fracture identification in shale reservoir using a deep learning method: Chang 7 reservoirs, Triassic Yanchang formation