CN111983671A

CN111983671A - Shallow water lake basin reservoir prediction method and device based on micro-ancient landform restoration

Info

Publication number: CN111983671A
Application number: CN201910432619.2A
Authority: CN
Inventors: 黄军平; 向倪娇; 杨占龙; 薛云军
Original assignee: Petrochina Co Ltd
Current assignee: Petrochina Co Ltd
Priority date: 2019-05-23
Filing date: 2019-05-23
Publication date: 2020-11-24
Anticipated expiration: 2039-05-23
Also published as: CN111983671B

Abstract

The application provides a shallow water lake basin reservoir prediction method and device based on micro ancient landform restoration, wherein the method comprises the following steps: determining the position of a diversion river channel through micro-ancient landform restoration and seismic facies research of a shallow water lake basin of a target layer system; according to the deposition compensation principle, recovering the micro-ancient landform, and determining the distribution mode of the diversion river; determining the reservoir distribution characteristics in the shallow water lake basin of the target layer system according to the distribution mode of the diversion river and the restoration of the micro-ancient landform; determining a wave impedance attribute capable of quantitatively characterizing reservoir distribution characteristics; according to the determined wave impedance attribute, performing wave impedance seismic waveform indication inversion; and slicing the inversion data body to obtain a plurality of stratigraphic slices so as to determine the deposition system and reservoir variation of the shallow water lake basin of the target layer system. By the scheme, the problem that the prediction result is inaccurate when the shallow water lake basin is predicted only by using seismic data is solved, and the technical effect of accurately determining the reservoir distribution and the development degree of the shallow water lake basin is achieved.

Description

Shallow water lake basin reservoir prediction method and device based on micro-ancient landform restoration

Technical Field

The application belongs to the technical field of geological exploration, and particularly relates to a shallow water lake basin reservoir prediction method and device based on micro-ancient landform restoration prediction.

Background

The shallow water delta in the shallow water lake basin is a main sedimentation system, and the exploration proves that the shallow water delta of the type is deposited in the stratums of the middle and new generations to be widely developed, so that a large amount of oil and gas resources exist, and the shallow water delta has a wide exploration prospect. And as the exploration finding rate of the constructed oil and gas reservoir is lower and lower, the exploration of the lithologic oil and gas reservoir or the low-permeability-ultra-low-permeability oil and gas reservoir is more and more important. However, reservoirs formed in shallow water sedimentary environments are difficult to identify by using seismic data, and particularly in extrusion basins, the reservoirs are influenced by coal-series stratums, and seismic data prediction is more difficult.

A large number of researches show that the shallow water delta is usually formed under the conditions of shallow water body, gentle terrain and overall slow settlement, and the delta mainly comprises a diversion river channel sand body. Shallow water delta mostly develops in a wide and slow area without obvious fracture, so that a front accumulation structure does not develop too much, and 3-layer structures of a top accumulation layer, a front accumulation layer and a bottom accumulation layer in a Gilbert type delta deposition mode do not exist. These features make geophysical identification of reservoirs in shallow water delta more difficult. In general, the thickness of a typical delta sedimentary sequence is only a few meters to twenty-three meters, no front layer and no fracture zone exist on a seismic section, and the thickness of the stratum is not changed greatly, so that the prediction of the reservoir distribution characteristics and the development degree in a shallow water lake basin is difficult.

At present, research on shallow water delta in shallow water lakes and basins is mainly limited to areas within a drilling range or in areas with particularly good seismic data quality, and causes and distribution characteristics of the shallow water delta are analyzed from drilling data. Therefore, the research limit is large, and the prediction difficulty is high.

In view of the above problems, no effective solution has been proposed.

Disclosure of Invention

The application aims at providing a shallow water lake basin reservoir prediction method and device based on micro-ancient landform restoration, terminal equipment and a storage medium, so as to solve the problem that the prediction result is not accurate when the shallow water lake basin is predicted only by seismic data.

The application provides a shallow water lake basin reservoir prediction method and device for recovering micro-ancient landforms, terminal equipment and a storage medium, which are realized in the following way:

a shallow water lake basin reservoir prediction method based on micro-paleotopographic restoration, comprising the following steps:

determining the position of a diversion river channel in a shallow water lake basin of a target layer system by recovering the micro-ancient landform of the shallow water lake basin of the target layer system and determining the seismic facies of the target layer system;

restoring the micro ancient landform of the shallow water lake basin of the target layer system according to a deposition compensation principle, and determining the distribution range of the diversion river;

Determining the reservoir distribution characteristics of the shallow water lake basin of the target layer system according to the distribution range of the diversion river and the micro ancient landform after deposition compensation;

determining wave impedance attributes that can quantitatively characterize the reservoir distribution characteristics;

according to the determined wave impedance attribute, performing wave impedance seismic waveform indication inversion to obtain a wave impedance seismic waveform indication inversion data volume;

slicing the data volume to obtain a plurality of stratigraphic slices;

and determining the deposition system and reservoir changes of the shallow water lake basin of the target layer according to the plurality of stratigraphic slices.

In one embodiment, the determining the position of the diversion river channel in the shallow water lake basin of the target layer system by recovering the micro ancient landform of the shallow water lake basin of the target layer system and determining the seismic facies of the target layer system comprises the following steps:

calibrating the target layer series bottom boundary of the target layer series shallow water lake basin through the principle of seismic stratigraphy, and calibrating the fluctuation change of the target layer series shallow water lake basin bottom boundary on the seismic section;

determining a low-lying area and a raised area in the micro ancient landform of the target layer series shallow water lake basin according to fluctuation changes of the bottom boundary of the target layer series shallow water lake basin on the seismic section;

acquiring a drilling curve of the shallow water lake basin of the target layer;

Determining whether the paleo-zones are sandy deposits according to the well drilling curve;

and calibrating the position of the diversion river channel in the shallow water lake basin of the target layer system according to the position of the paleo-low region on the seismic section and the seismic facies characteristics.

In one embodiment, according to the deposition compensation principle, the recovering of the micro ancient landform of the shallow water lake basin of the target layer system and the determining of the distribution range of the diversion river channel comprise:

converting the top of the targeted layer system calibrated on the seismic section into a first depth domain;

converting the bottom boundary of the target layer system calibrated on the seismic section into a second depth domain;

taking the difference value between the first depth domain and the second depth domain as a recovered micro ancient landform;

carrying out three-dimensional visualization processing on the recovered micro-ancient landform to obtain a three-dimensional micro-ancient landform image;

and according to the seismic facies characteristics of the diversion river channel on the seismic section, marking the diversion river channel in the three-dimensional micro ancient apparent map.

In one embodiment, the wave impedance properties characterizing the reservoir distribution can be quantified, including at least one of: natural gamma curve, resistivity curve, natural potential curve.

In one embodiment, according to the determined wave impedance attribute, performing wave impedance seismic waveform indication inversion to obtain a wave impedance seismic waveform indication inversion data volume, including:

determining sample number and high-frequency components by analyzing the number of the bottom boundary exploratory wells drilled in the shallow water lake basin of the target layer system and the quality of seismic data by taking the wave impedance attribute as a constraint condition, and establishing an initial model;

and (3) on the basis of the initial model, obtaining a wave impedance seismic waveform indication inversion data volume by using seismic waveform indication inversion.

In one embodiment, slicing the data volume to obtain a plurality of stratigraphic slices comprises:

and slicing the data volume under a five-level sequence grid to obtain a plurality of stratigraphic slices, wherein the slicing interval takes the minimum value meeting the thickness of sandstone or the minimum sand group as the slicing interval in the slicing operation process.

In one embodiment, determining depositional system and reservoir changes for the shallow water lake basin of the interest layer from the plurality of stratigraphic slices comprises:

determining reservoir changes of sandstone reservoirs in the shallow water lake basin of the target layer series according to the high resistance states in the plurality of stratum slices;

and determining reservoir changes in the shallow water lake basin of the target layer according to the low resistivity state in the plurality of stratigraphic slices.

A shallow water lake basin reservoir prediction device based on micro-ancient landform restoration comprises:

the system comprises a first determination module, a second determination module and a third determination module, wherein the first determination module is used for determining the position of a diversion river channel in a shallow water lake basin of a target layer system by recovering the micro-ancient landform of the shallow water lake basin of the target layer system and determining the seismic facies of the target layer system;

the restoration module is used for restoring the micro ancient landform of the shallow water lake basin of the target layer system according to a deposition compensation principle and determining the distribution range of the diversion river channel;

the second determination module is used for determining the reservoir distribution characteristics of the shallow water lake basin of the target layer system according to the distribution range and the sedimentary micro-ancient landform of the diversion river channel;

a third determination module for determining a wave impedance attribute that quantifiably characterizes the reservoir distribution feature;

the inversion module is used for performing wave impedance seismic waveform indication inversion according to the determined wave impedance attribute to obtain a wave impedance seismic waveform indication inversion data volume;

the slicing module is used for slicing the data body to obtain a plurality of stratigraphic slices;

and the fourth determination module is used for determining the deposition system and the reservoir change of the shallow water lake basin of the target layer according to the plurality of stratigraphic slices.

A terminal device comprising a processor and a memory for storing processor-executable instructions, the instructions when executed by the processor implementing the steps of the method of:

slicing the data volume to obtain a plurality of stratigraphic slices;

A computer readable storage medium having stored thereon computer instructions which, when executed, implement the steps of a method comprising:

slicing the data volume to obtain a plurality of stratigraphic slices;

The reservoir prediction method for the shallow water lake basin is based on micro ancient landform restoration, diversion river channel identification and distribution characteristic prediction, reservoir distribution characteristic prediction is conducted under guidance of diversion river channels and basin filling characteristics, inversion is indicated through seismic waveforms, reservoir development degree and distribution range under an ancient landform background are predicted, a stratum slicing technology is used for dynamically representing a deposition system and reservoir evolution rules in the shallow water lake basin, and therefore reservoir distribution and development degree of the shallow water lake basin can be accurately determined.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without any creative effort.

FIG. 1 is a flow chart of a method of an embodiment of a method for reservoir prediction of shallow water lakes provided herein;

fig. 2 is a schematic diagram of a three-dimensional overlay display of a micro-ancient landform and a diversion river provided by the present application;

FIG. 3 is a schematic cross-sectional view of the continuous well sedimentary phase analysis of W1-W2-W3-W4-W7-W8 provided herein;

FIG. 4 is a plot of a drilling sandstone-shale wave impedance distribution histogram and a sandstone thickness versus wave impedance intersection provided by the present application;

FIG. 5 is a schematic representation of a W1-W2-W3-W4 continuous well wave impedance seismic waveform indicating inversion profile provided herein;

FIG. 6 is a schematic representation of a W1-W5-W6 continuous well wave impedance seismic waveform indicating inversion section provided herein;

FIG. 7 is a schematic illustration of a stratigraphic slice made based on a wave impedance seismic waveform indicative inversion data volume as provided herein;

Fig. 8 is a schematic block diagram of an embodiment of a reservoir device of a shallow water lake basin provided by the present application.

Detailed Description

In order to make those skilled in the art better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

Aiming at the problems of the existing research on shallow water deltas in shallow water lakes, the embodiment provides a reservoir prediction method for shallow water lakes, which predicts reservoir distribution characteristics under the guidance of diversion river channels and basin filling characteristics on the basis of micro-paleotopographic restoration, diversion river channel identification and distribution characteristic prediction. And (3) utilizing seismic waveform indication inversion to predict the development degree and distribution range of the reservoir under the ancient landform background, and verifying the reservoir by using actual drilling. And finally, dynamically representing a deposition system and a reservoir evolution rule in the shallow water lake basin by utilizing a stratigraphic slicing technology, thereby completing the efficient and accurate prediction of the reservoir distribution of the shallow water lake basin and effectively predicting the reservoir distribution range and the development degree in the shallow water lake basin.

Fig. 1 is a flowchart of a method of an embodiment of a shallow water lake basin reservoir prediction method based on micro paleotopographic restoration according to the present application. Although the present application provides method operational steps or apparatus configurations as illustrated in the following examples or figures, more or fewer operational steps or modular units may be included in the methods or apparatus based on conventional or non-inventive efforts. In the case of steps or structures which do not logically have the necessary cause and effect relationship, the execution sequence of the steps or the module structure of the apparatus is not limited to the execution sequence or the module structure described in the embodiments and shown in the drawings of the present application. When the described method or module structure is applied in an actual device or end product, the method or module structure according to the embodiments or shown in the drawings can be executed sequentially or executed in parallel (for example, in a parallel processor or multi-thread processing environment, or even in a distributed processing environment).

Specifically, as shown in fig. 1, a shallow water lake basin reservoir prediction method based on micro paleotopographic restoration according to an embodiment of the present application may include the following steps:

step 101: determining the position of a diversion river channel in a shallow water lake basin of a target layer system by recovering the micro-ancient landform of the shallow water lake basin of the target layer system and determining the seismic facies of the target layer system;

Specifically, the principle of seismic stratigraphy can be utilized to finely explain the bottom boundary of the target layer system, and the micro fluctuation change on the seismic section is shown as much as possible in the explanation process. Furthermore, the well logging curves of the drilled wells such as a natural gamma curve, a resistivity curve or a natural potential curve and the like can be comprehensively used for identifying whether the ancient low-lying areas in the micro ancient landform are sandy sediments, and then the drilled wells can be subjected to fine calibration of synthetic records to determine the specific positions and seismic facies characteristics of the ancient low-lying areas on the seismic section, namely the diversion river channels are explained as finely as possible by combining the seismic facies characteristics of the ancient low-lying areas.

Step 102: restoring the micro ancient landform of the shallow water lake basin of the target layer system according to a deposition compensation principle, and determining the distribution range of the diversion river;

when the method is realized, the micro ancient landform can be recovered by using a deposition compensation principle, specifically, all explained target layer series tops and bottoms can be converted into depth domains on seismic profiles in a research area and then subtracted to obtain the micro ancient landform, and then the micro ancient landform obtained by subtracting the depth domains is compared with the micro ancient landform obtained in a time domain to determine the overall change trend and whether the micro ancient landform conforms to geological rules.

Then, the obtained micro ancient landforms of the depth domain and the time domain can be subjected to three-dimensional visualization, so that the stratum change amplitude is relatively large as much as possible, mainly because the micro ancient landforms have relatively large stratum change amplitude, and the diversion river channel can be better identified. After the three-dimensional micro-ancient apparent image is obtained, the split river channels can be drawn one by one on the three-dimensional micro-ancient apparent image by combining the seismic facies characteristics of the split river channels on the seismic section.

Step 103: determining the reservoir distribution characteristics of the shallow water lake basin of the target layer system according to the distribution range of the diversion river and the micro ancient landform after deposition compensation;

for example, the distribution characteristics of the reservoir in the shallow water lake basin can be predicted under the guidance of the distribution and basin filling characteristic principle of the diversion river channel (namely, the reservoir mainly develops in the place where the diversion river channel develops) according to the restored micro ancient landform and the diversion river channel overlay.

Considering that the diversion river channel identified on the seismic section is in the descending period of the datum level, the residual accommodation space formed by the erosion of the ground surface is generally developed along the sequence interface, and is an ancient low-lying area for transporting and accumulating sediments into the basin. Usually, the sand sediment is preferentially filled in the diversion river channel, and the mud sediment is deposited outside the diversion river channel in the form of sheet flow or diffuse flow. That is, the wells drilled in the strata of interest in the study are typically filled with sandy deposits in the developed part of the diversion channel.

Based on the characteristics of reservoir development in shallow water lakes, well-connecting sections parallel to the direction of a source can be selected in a research area for sedimentary facies analysis, and particularly, sand sediments or mud sediments can be analyzed above and below the bottom boundary of a target layer, and in places filled with diversion river channels on seismic sections, on wells. Through sedimentary facies analysis, it can be found that on the seismic section, the micro-ancient low-lying area is basically a diversion river channel position and is mainly filled with sandy sediments, and the sandy sediments at the middle and lower parts of the target layer system develop and the muddy sediments at the top develop. Therefore, the seismic profile of the well connection can be selected, and wave impedance seismic waveform indication is made to carry out inversion prediction on reservoir distribution characteristics and development degree.

Step 104: determining wave impedance attributes that can quantitatively characterize the reservoir distribution characteristics;

specifically, a wave impedance curve can be used as a constraint condition, reasonable sample number and proper high-frequency components are preferably selected through analyzing the number of exploratory wells and the quality of seismic data, and a reasonable initial model is established; then, on the basis of the initial model, performing inversion by using seismic waveform indication to obtain the development degree and the distribution rule of the reservoir on the well-connecting section; and then, the inversion result can be compared with a natural gamma curve and a wave impedance curve, if the inversion result has a better matching effect with the aboveground curve, the inversion result is reliable, and an inversion data body of the target layer system wave impedance seismic waveform indication inversion of the research area can be further obtained through seismic waveform indication inversion.

Step 105: according to the determined wave impedance attribute, performing wave impedance seismic waveform indication inversion to obtain a wave impedance seismic waveform indication inversion data volume;

in the process of seismic waveform inversion, seismic waveform information with dense spatial distribution is fully utilized in the transverse direction, and drilling information with high resolution is fully utilized in the longitudinal direction. The seismic waveform is densely distributed spatial structural data and reflects the spatial change of the combination of the deposition environment and lithology, so that the seismic waveform indication inversion can utilize seismic waveform similarity to optimize related well samples, establish an initial model by referring to the sample spatial distribution distance and curve distribution characteristics, and replace a variation function to analyze a spatial variation structure to perform unbiased optimal estimation on high-frequency components. Specifically, the inversion can be performed in three steps as follows:

s1: loading earthquake and well logging data and performing fine calibration on synthetic records;

s2: preferably, related well samples (namely, the number of drilled wells is not larger or smaller, but is suitable for selection), and the initial model is established by referring to the spatial distribution distance and the curve distribution characteristics of the samples;

s3: the seismic waveform most similar to the wave impedance curve is preferably selected near the well point, a global optimization algorithm is adopted, the seismic waveform is popularized to a well-free area, the inversion certainty is greatly enhanced, and the inversion certainty is gradually determined from complete randomness. And repeatedly comparing the inverted well-connected seismic profile with the natural gamma curve and the wave impedance curve until a proper geological model is established, and applying the geological model to the whole seismic data body to achieve the purpose of inversion prediction of a reservoir in a shallow water lake basin.

Step 106: slicing the data volume to obtain a plurality of stratigraphic slices;

step 107: and determining the deposition system and reservoir changes of the shallow water lake basin of the target layer according to the plurality of stratigraphic slices.

Through the seismic waveform indication inversion, the ancient low-lying area in the micro ancient landform can be found as a diversion river channel position, and sandy sediments are filled in the ancient low-lying area. In order to quantitatively characterize the sand-mud rock, the sand-mud rock impedance of a target layer system in a research area can be analyzed, and the sandstone wave impedance is higher and the mudstone wave impedance is lower, although the sand-mud rock wave impedance is partially repeated, the rule approximately exists. Secondly, performing intersection analysis on the sandstone thickness and the wave impedance on all the drilled wells drilled in the target layer system, and also finding that the wave impedance tends to increase along with the increase of the sandstone thickness. The knowledge that the high-wave impedance on the wave impedance inversion section is sandstone and the low-wave impedance is mudstone is obtained.

When the method is realized, the wave impedance seismic waveform indication inversion data body is made into the stratigraphic section under the five-level sequence grid, and the section interval meets the minimum value of the thickness of the sandstone or the minimum sand group in the research area, so that the stratigraphic section of a series of wave impedance bodies is obtained. Through the analysis of the series of stratigraphic sections, the evolution law of the sedimentation system in the shallow water lake basin can be more intuitively known, and the evolution law of the reservoir in the shallow water lake basin can be further mastered.

In the above example, based on the micro-paleotopographic restoration, the diversion river channel identification and the distribution characteristic prediction, the reservoir distribution characteristic is predicted under the guidance of the diversion river channel and basin filling characteristics, the seismic waveform indication inversion is utilized to predict the reservoir development degree and the distribution range under the paleotopographic background, and the stratigraphic slicing technology is utilized to dynamically represent the deposition system and the reservoir evolution law in the shallow water lake basin, so that the reservoir distribution and the development degree of the shallow water lake basin can be accurately determined.

In the step 101, determining the position of the diversion river channel in the shallow lake basin of the target layer system by recovering the micro ancient landform of the shallow lake basin of the target layer system and determining the seismic facies of the target layer system may include: calibrating the bottom boundary of the target layer series shallow water lake basin through seismic stratigraphy, and calibrating fluctuation on the seismic section of the bottom boundary of the target layer series shallow water lake basin; according to fluctuation changes on the seismic section of the bottom boundary of the target layer series shallow water lake basin, determining an ancient low-lying area in the micro ancient landform of the target layer series shallow water lake basin; acquiring a drilling curve of the shallow water lake basin of the target layer; determining whether the paleo-zones are sandy deposits according to the well drilling curve; and calibrating the position of the diversion river channel in the shallow water lake basin of the target layer system according to the position of the paleo-low region on the seismic section and the seismic facies characteristics. Namely, based on seismic stratigraphy and the like, the position of the diversion river channel in the shallow water lake basin can be accurately calibrated by combining the micro ancient landform and the seismic facies, so that the calibrated position of the diversion river channel is more accurate.

When the micro ancient landform of the shallow water lake basin of the target layer series is restored according to the deposition compensation principle and the distribution range of the diversion river is determined, the top of the target layer series calibrated on the seismic section can be converted into a first depth domain; converting the calibrated target layer bottom boundary on the seismic section into a second depth domain; and taking the difference value between the first depth domain and the second depth domain as the recovered micro ancient landform. Carrying out three-dimensional visualization processing on the recovered micro-ancient landform to obtain a three-dimensional micro-ancient landform image; and according to the seismic facies characteristics of the diversion river channel on the seismic section, marking the diversion river channel in the three-dimensional micro ancient apparent map. The method comprises the steps of converting the top and the bottom of all explained target layer systems into depth domains on seismic sections in a research area, subtracting the depth domains to obtain micro-ancient landforms, and carrying out three-dimensional visualization processing on the micro-ancient landforms, so that the position of a diversion river channel can be marked in a three-dimensional micro-ancient landform map, and the position of the diversion river channel is clearer.

The wave impedance properties described above that can quantitatively characterize the reservoir distribution may include, but are not limited to, at least one of: natural gamma curve, resistivity curve, natural potential curve.

In one embodiment, according to the determined wave impedance attribute, performing wave impedance seismic waveform indication inversion to obtain a wave impedance seismic waveform indication inversion data volume, wherein the sample number and high-frequency components are determined by analyzing the number of exploratory wells of a shallow lake basin of a target layer system and the quality of seismic data by taking the wave impedance attribute as a constraint condition, and an initial model is established; and (3) on the basis of the initial model, obtaining a wave impedance seismic waveform indication inversion data volume by using seismic waveform indication inversion.

The above method is described below with reference to a specific example, however, it should be noted that the specific example is only for better describing the present application and is not to be construed as limiting the present application.

In this example, a shallow water lake basin reservoir prediction method is provided, which may include the following steps:

step 1: recovering the micro-ancient landform and identifying the diversion river channel:

firstly, in the process of stratigraphic interpretation, the principle of seismography is utilized to finely interpret the bottom boundary of a target layer, and particularly, micro relief change on an earthquake section is interpreted as far as possible from micro relief areas in the micro ancient landforms.

Secondly, the diversion river channel on the seismic section is identified, specifically, target well drilling can be analyzed firstly, and especially the logging curve characteristics of the sandstone and the mudstone, for example, the logging curve characteristics of the sandstone and the mudstone can be determined through a natural gamma curve or a natural potential curve, a resistivity curve logging curve and the like.

And then, performing fine synthesis record calibration on the drilled well to determine the sandstone development place and seismic facies characteristics, and determining whether sandstone filling exists in the low-lying areas in the bottom boundary and the micro-ancient landforms.

And then, restoring the micro-ancient landform by using a deposition compensation principle, specifically, converting the top and the bottom of all the explained target layer systems into depth domains on the seismic section of the research area and subtracting the depth domains to obtain the micro-ancient landform. And comparing the obtained micro-ancient landforms in the time domain to determine the overall change trend and whether the geological rules are met.

And finally, carrying out three-dimensional visualization processing on the obtained micro ancient landform image, and connecting the low-lying areas of the terrain in the previously identified micro ancient landform by lines to serve as a diversion river channel, wherein the identification of the low-lying areas of the terrain in the micro ancient landform needs to turn over the seismic sections one by one in the three-dimensional seismic work area from two directions of lines and channels, and correspondingly, the more the seismic sections are checked, the more the low-lying areas of the terrain in the identified micro ancient landform are accurate.

As shown in fig. 2, namely, a schematic diagram is displayed by three-dimensional superposition of the micro ancient landform recovered by the above method and the diversion river, a black line in the diagram is the diversion river, and as can be seen from fig. 2, a depression in the micro ancient landform is a diversion river development area.

Step 2: under the characteristics of distribution of the diversion river and basin filling, the distribution range and the development degree of the reservoir are predicted:

based on the characteristic that a reservoir layer mainly develops in a place where a diversion river channel develops and an ancient relief area in a micro ancient landform is mainly filled with sandy sediments, a well connecting section parallel to the direction of a source can be selected in a three-dimensional earthquake work area to perform sedimentary facies analysis, and particularly, the sandy sediments or the muddy sediments on a drilled well are determined at the place where the diversion river channel fills on the earthquake section above and below the bottom boundary of a target layer. The results of the sedimentary phase analysis show that: as shown in fig. 3, in the seismic section, the micro-ancient low-lying area is basically a diversion channel position and is mainly filled with sandy sediments, and the sandy sediments in the lower part of the target layer develop and the muddy sediments in the top part develop.

In order to quantitatively represent the sand shale, the sand shale wave impedance or other logging curves of a drilling target layer system in a three-dimensional seismic work area can be screened so as to determine the logging curve capable of representing the sand shale property, and seismic waveform indication inversion can be carried out based on the logging curve. As shown in fig. 4, the sensitivity curve analysis indicates that the wave impedance can better distinguish sand mudstones, generally speaking, high wave impedance is sandstone, and low wave impedance is mudstone. By combining the characteristic of reservoir development, the well-connected seismic profile can be selected, wave impedance seismic waveform indication is made, inversion prediction is conducted on reservoir distribution characteristics and development degree, and after the top boundary of the target layer system is leveled, sandstone with almost all micro high impedance (high wave impedance) is filled in low-lying positions of the terrain in the micro ancient landform as shown in fig. 5. As shown in fig. 6, the inversion profile is indicated for another connected-well wave impedance seismic waveform parallel to the source direction, and after the top boundary of the target layer is also leveled, it can be seen that the low-lying relief in the micro-paleo-landform is filled with sandstone with high impedance, in fig. 5 and 6, the gray color indicates the reservoir, and the dark color indicates the non-reservoir.

And step 3: and quantitatively representing the deposition system and reservoir evolution in the shallow water lake basin through stratigraphic slices.

Through quantitative characterization of the optimal attribute-wave impedance of the sand shale in the step 2, in a five-level sequence grid, stratum slicing is carried out on the wave impedance seismic waveform indication inversion data body obtained in the step 2, and in the slicing process, the slicing interval needs to meet the minimum value of the sandstone thickness or the minimum sand group thickness in the three-dimensional work area, so that a series of stratum slices are obtained, as shown in fig. 7, the stratum slices can find that the evolution rule of a deposition system is completely related to the diversion river channel position identified in the step 1, and the deposition system is different in different periods. The development characteristics of the reservoir in the shallow lake basin can be predicted according to the change rule of the sedimentation system, namely: the local reservoir of the diversion river channel is relatively developed, which is consistent with the view of the diversion river channel in the shallow water delta as the main reservoir thereof and also conforms to the characteristics of the reservoir development of the shallow water lake basin.

In the above example, the seismic facies characteristics of the diversion channel are identified in the seismic profile, and then the drilling log facies are used to verify that the diversion channel filler is sandstone. Secondly, by using the principle of earthquake stratigraphy, fine structure explanation is developed, and the micro-ancient landform and the distribution characteristics of the diversion river channel are restored. Under the background of a micro-ancient landform and a diversion river channel, the sensitive attribute of sand-shale can be preferably and quantitatively represented, seismic waveform indication inversion is carried out to predict a reservoir, verification is carried out by actual drilling, the inversion result shows that sandstone is filled in a low-lying part of the micro-ancient landform, finally, seismic waveform indication inversion is carried out on the sensitive attribute, a stratum slice is manufactured on the basis of the inversion body, high resistance in the stratum slice represents sandstone change, low resistance represents mudstone change, and the series of stratum slices represent a deposition system in a shallow water lake basin and a reservoir change rule. The prediction method solves the problem of prediction of the distribution range and the development degree of the reservoir in the shallow lake basin, and achieves the aim of simply and efficiently predicting the reservoir.

Based on the same inventive concept, the embodiment of the invention also provides a shallow water lake basin reservoir prediction device based on micro ancient landform restoration, and the device is described in the following embodiment. Because the problem solving principle of the shallow water lake basin reservoir prediction device based on the micro-ancient landform restoration is similar to the prediction method of the shallow water lake basin reservoir based on the micro-ancient landform restoration, the implementation of the shallow water lake basin reservoir prediction device based on the micro-ancient landform restoration can be referred to the implementation of the shallow water lake basin reservoir prediction method based on the micro-ancient landform restoration, and repeated parts are not described again. As used hereinafter, the term "unit" or "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated. Fig. 8 is a block diagram of a structure of a shallow water lake basin reservoir prediction device based on micro ancient landform restoration according to an embodiment of the present invention, as shown in fig. 8, which may include: a first determining module 801, a restoring module 802, a second determining module 803, a third determining module 804, an inverting module 805, a slicing module 806, and a fourth determining module 807, the structure of which is described below.

The first determining module 801 is used for determining the position of a diversion river channel in a shallow water lake basin of a target layer system by recovering the micro ancient landform of the shallow water lake basin of the target layer system and determining the seismic facies of the target layer system;

the recovery module 802 is configured to recover the micro ancient landform of the shallow water lake basin of the target layer system according to a deposition compensation principle, and determine a distribution range of the diversion river;

the second determining module 803 is configured to determine the reservoir distribution characteristics of the shallow water lake basin of the target layer system according to the distribution range and the sedimentary micro-ancient landform of the diversion river;

a third determining module 804, configured to determine a wave impedance attribute that can quantitatively characterize the reservoir distribution characteristic;

the inversion module 805 is configured to perform wave impedance seismic waveform indication inversion according to the determined wave impedance attribute to obtain a wave impedance seismic waveform indication inversion data volume;

a slicing module 806, configured to slice the data volume to obtain a plurality of stratigraphic slices;

a fourth determining module 807 for determining depositional system and reservoir changes of the shallow water lake basin of the target layer based on the plurality of stratigraphic slices.

In one embodiment, the first determining module 801 may specifically calibrate a target layer series bottom boundary of the target layer series shallow water lake basin through a seismic stratigraphy principle, and calibrate fluctuation of the target layer series shallow water lake basin bottom boundary on a seismic section; determining a low-lying area and a raised area in the micro ancient landform of the target layer series shallow water lake basin according to fluctuation changes of the bottom boundary of the target layer series shallow water lake basin on the seismic section; acquiring a drilling curve of the shallow water lake basin of the target layer; determining whether the paleo-zones are sandy deposits according to the well drilling curve; and calibrating the position of the diversion river channel in the shallow water lake basin of the target layer system according to the position of the paleo-low region on the seismic section and the seismic facies characteristics.

In one embodiment, according to the deposition compensation principle, the recovering the micro ancient landform of the shallow water lake basin of the target layer system and determining the distribution range of the diversion river channel may include: converting the top of the targeted layer system calibrated on the seismic section into a first depth domain; converting the bottom boundary of the target layer system calibrated on the seismic section into a second depth domain; taking the difference value between the first depth domain and the second depth domain as a recovered micro ancient landform; carrying out three-dimensional visualization processing on the recovered micro-ancient landform to obtain a three-dimensional micro-ancient landform image; and according to the seismic facies characteristics of the diversion river channel on the seismic section, marking the diversion river channel in the three-dimensional micro ancient apparent map.

In one embodiment, the wave impedance properties that characterize the reservoir distribution quantitatively may include, but are not limited to, at least one of: natural gamma curve, resistivity curve, natural potential curve.

In one embodiment, the inversion module 805 may specifically determine the number of samples and high-frequency components by analyzing the number of bottom-bound exploratory wells drilled in the shallow water lake basin of the target layer system and the quality of seismic data with the wave impedance attribute as a constraint condition, and establish an initial model; and (3) on the basis of the initial model, obtaining a wave impedance seismic waveform indication inversion data volume by using seismic waveform indication inversion.

In one embodiment, the slicing module 806 may slice the data volume to obtain a plurality of stratigraphic slices in a five-level sequence trellis, wherein the slicing interval during the slicing operation is determined as the slicing interval according to a minimum value satisfying the thickness of the sandstone or the minimum sand group.

In one embodiment, the fourth determining module 807 may specifically determine the reservoir variation of the sandstone reservoir in the shallow water lake basin of the target layer according to the high resistance state in the plurality of stratigraphic slices; and determining reservoir changes in the shallow water lake basin of the target layer according to the low resistivity state in the plurality of stratigraphic slices.

The embodiment of the present application further provides a specific implementation manner of an electronic device capable of implementing all steps in the shallow water lake basin reservoir prediction method based on micro ancient landform restoration in the above embodiment, where the electronic device specifically includes the following contents: a processor (processor), a memory (memory), a communication Interface (Communications Interface), and a bus; the processor, the memory and the communication interface complete mutual communication through the bus; the processor is configured to call a computer program in the memory, and the processor executes the computer program to implement all the steps in the method for shallow water lake basin reservoir prediction based on micro paleotopographic restoration in the above embodiment, for example, the processor executes the computer program to implement the following steps:

Step 1: determining the position of a diversion river channel in a shallow water lake basin of a target layer system by recovering the micro ancient landform of the shallow water lake basin of the target layer system and determining the seismic facies of the target layer system;

step 2: restoring the micro ancient landform of the shallow water lake basin of the target layer system according to a deposition compensation principle, and determining the distribution range of the diversion river;

and step 3: determining the reservoir distribution characteristics of the shallow water lake basin of the target layer system according to the distribution range of the diversion river and the micro ancient landform after deposition compensation;

and 4, step 4: determining wave impedance attributes that can quantitatively characterize the reservoir distribution characteristics;

and 5: according to the determined wave impedance attribute, performing wave impedance seismic waveform indication inversion to obtain a wave impedance seismic waveform indication inversion data volume;

step 6: slicing the data volume to obtain a plurality of stratigraphic slices;

and 7: and determining the deposition system and reservoir changes of the shallow water lake basin of the target layer according to the plurality of stratigraphic slices.

From the above description, it can be known that the reservoir distribution characteristics are predicted under guidance of the filling characteristics of the diversion river channel and basin based on micro-ancient landform restoration, diversion river channel identification and distribution characteristic prediction, the reservoir development degree and the distribution range under the ancient landform background are predicted by utilizing seismic waveform indication inversion, and the sedimentary system and the reservoir evolution rule in the shallow lake basin are dynamically represented by utilizing the stratigraphic slicing technology, so that the reservoir distribution and the development degree of the shallow lake basin can be accurately determined.

Embodiments of the present application also provide a computer-readable storage medium capable of implementing all steps in the shallow water lake basin reservoir prediction method based on micro paleotopographic restoration in the above embodiments, where the computer-readable storage medium stores thereon a computer program, and the computer program, when executed by a processor, implements all steps of the shallow water lake basin reservoir prediction method based on micro paleotopographic restoration in the above embodiments, for example, the processor implements the following steps when executing the computer program:

step 1: determining the position of a diversion river channel in a shallow water lake basin of a target layer system by recovering the micro-ancient landform of the shallow water lake basin of the target layer system and determining the seismic facies of the target layer system;

Step 6: slicing the data volume to obtain a plurality of stratigraphic slices;

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the hardware + program class embodiment, since it is substantially similar to the method embodiment, the description is simple, and the relevant points can be referred to the partial description of the method embodiment.

The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.

Although the present application provides method steps as described in an embodiment or flowchart, additional or fewer steps may be included based on conventional or non-inventive efforts. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. When an actual apparatus or client product executes, it may execute sequentially or in parallel (e.g., in the context of parallel processors or multi-threaded processing) according to the embodiments or methods shown in the figures.

The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a vehicle-mounted human-computer interaction device, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

Although embodiments of the present description provide method steps as described in embodiments or flowcharts, more or fewer steps may be included based on conventional or non-inventive means. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. When an actual apparatus or end product executes, it may execute sequentially or in parallel (e.g., parallel processors or multi-threaded environments, or even distributed data processing environments) according to the method shown in the embodiment or the figures. The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, the presence of additional identical or equivalent elements in a process, method, article, or apparatus that comprises the recited elements is not excluded.

For convenience of description, the above devices are described as being divided into various modules by functions, and are described separately. Of course, in implementing the embodiments of the present description, the functions of each module may be implemented in one or more software and/or hardware, or a module implementing the same function may be implemented by a combination of multiple sub-modules or sub-units, and the like. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may therefore be considered as a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.

The embodiments of this specification may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The described embodiments may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment. In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of an embodiment of the specification. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.

The above description is only an example of the embodiments of the present disclosure, and is not intended to limit the embodiments of the present disclosure. Various modifications and variations to the embodiments described herein will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the embodiments of the present specification should be included in the scope of the claims of the embodiments of the present specification.

Claims

1. A shallow water lake basin reservoir prediction method based on micro-ancient landform restoration is characterized by comprising the following steps:

Slicing the data volume to obtain a plurality of stratigraphic slices;

2. The method of claim 1, wherein determining the position of the diversion river channel in the shallow water lake basin of the target layer system by recovering the micro paleotopographic of the shallow water lake basin of the target layer system and determining the seismic facies of the target layer system comprises:

according to the fluctuation change of the bottom boundary of the target layer series shallow water lake basin on the seismic section, determining an ancient low-lying area and an ancient raised area in the micro ancient landform of the target layer series shallow water lake basin;

acquiring a drilling curve of the shallow water lake basin of the target layer;

3. The method of claim 2, wherein the recovering the micro ancient landforms of the shallow water lake basin of the target layer system and the determining the distribution range of the diversion river according to the sedimentation compensation principle comprises:

4. The method of claim 1, wherein the wave impedance properties characterizing the reservoir distribution can be quantified, including at least one of: natural gamma curve, resistivity curve, natural potential curve.

5. The method of claim 1, wherein performing a wave impedance seismic waveform indication inversion according to the determined wave impedance attribute to obtain a wave impedance seismic waveform indication inversion data volume, comprises:

6. The method of claim 1, wherein slicing the data volume to obtain a plurality of stratigraphic slices comprises:

7. The method of claim 1, wherein determining depositional systems and reservoir changes for the shallow water lake basin of the target layer from the plurality of stratigraphic slices comprises:

8. The utility model provides a shallow water lakebasin reservoir prediction device based on little ancient landform resumes which characterized in that includes:

9. A terminal device comprising a processor and a memory for storing processor-executable instructions which, when executed by the processor, implement the steps of the method of any one of claims 1 to 7.

10. A computer readable storage medium having stored thereon computer instructions which, when executed, implement the steps of the method of any one of claims 1 to 7.