Method and system for predicting argillaceous rock thickness data

Abstract

The invention belongs to the technical field of logging evaluation, and particularly relates to a method and system for predicting argillaceous rock thickness data. The method comprises the following steps: collecting logging data of a to-be-predicted target interval of a drilled well, and determining the reservoir interval apparent thickness of the target interval according to the logging data; determining the shale rock apparent thickness of the target layer section according to the reservoir section apparent thickness, and determining shale rock and stratum ratio grid data according to the shale rock apparent thickness; seismic data and seismic stratification data of a research area are collected, the seismic stratum apparent thickness of a target layer section of the research area is obtained, and seismic stratum apparent thickness grid data are determined. The method aims to solve the problems that the pure mudstone distribution range of the mixed lithofacies development area cannot completely represent the hydrocarbon generation center range, mudstone logging is difficult to recognize, the hydrocarbon generation center range is difficult to predict due to the fact that various lithofacies cannot be distinguished, drilling wells on the area are few, and the cost is low. And the problems of low calculation accuracy of the mudstone accumulated thickness and inaccurate plane prediction caused by incomplete logging information are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of well logging evaluation, and in particular relates to a method and system for predicting argillaceous rock thickness data. Background Art

[0002] Sediments in saline lake basins often form mixed sedimentary facies, a type of rock formed by the mixed deposition of clastic rocks, carbonate rocks, and clay minerals at different scales. With the continuous advancement of oil and gas exploration, the exploration direction has gradually shifted from structural reservoirs to lithologic reservoirs, and the exploration target has gradually shifted from clastic rocks and carbonate rocks to mixed sedimentary rock reservoirs in the transition zone. Currently, oil and gas exploration breakthroughs have been achieved in areas with mixed sedimentary facies development, such as the western Qaidam Basin, Jimsar in the Junggar Basin, and the Bohai Bay.

[0003] Planar prediction of hydrocarbon generation centers relies on mudstone thickness parameters. In areas with conventional clastic and carbonate facies, mudstone and non-mudstone are relatively easy to distinguish. Therefore, mudstone thickness is often simply calculated from mud logging lithology. The distribution range of hydrocarbon generation centers is predicted by statistically analyzing and mapping the cumulative thickness of mudstone in a single well. However, the determination of hydrocarbon generation centers in mixed sedimentary facies presents two unique challenges: ① The distribution scale of pure mudstone in mixed sedimentary facies is very small, and according to analytical results, transitional lithologies (including argillaceous limestone, silty mudstone, and limy mudstone) all have certain hydrocarbon generation capabilities. Therefore, the distribution range of hydrocarbon generation centers cannot be simply represented by the pure mudstone range; ② Lithology identification is difficult, and the quality of mud logging data is uneven. The accuracy of lithology identification using well logging curves is much higher than that using mud logging data. Summary of the Invention

[0004] In order to solve the above problems, the present invention provides a method for predicting argillaceous rock thickness data, which comprises:

[0005] Collect well logging data of the target layer to be predicted that has been drilled, and determine the apparent reservoir thickness of the target layer based on the well logging data;

[0006] Determine the apparent thickness of the argillaceous rock in the target layer based on the apparent thickness of the reservoir layer, and determine the grid data of the argillaceous rock to formation ratio based on the apparent thickness of the argillaceous rock;

[0007] Collect seismic data and seismic layer data in the study area, obtain the apparent thickness of the seismic strata in the target layer of the study area, and determine the grid data of the apparent thickness of the seismic strata;

[0008] The argillaceous rock thickness plane grid data of the target layer is determined based on the argillaceous rock to formation ratio grid data and the seismic formation apparent thickness grid data.

[0009] Preferably, the logging data of the target layer include: uranium-free gamma and shallow lateral resistivity logging data of the mixed rock layer.

[0010] Preferably, determining the apparent thickness of the reservoir section of the target layer according to the well logging data comprises:

[0011] The logging characteristics of the reservoir section in the target layer are established based on the logging data, the reservoir section is identified, and the apparent thickness of the reservoir section is obtained.

[0012] Preferably, determining the apparent thickness of the mudstone in the target interval according to the apparent thickness of the reservoir interval includes:

[0013] Get the total thickness of the target layer;

[0014] The apparent thickness of the mudstone in the target layer is determined based on the difference between the total thickness of the target layer and the apparent thickness of the reservoir section.

[0015] Preferably, determining the argillaceous rock to formation ratio grid data according to the apparent thickness of the argillaceous rock includes:

[0016] Obtain the apparent thickness of the target layer;

[0017] The argillaceous rock to formation ratio grid data is constructed according to the ratio of the argillaceous rock apparent thickness to the formation apparent thickness.

[0018] Preferably, obtaining the seismic stratum apparent thickness of the target layer section in the study area and determining the seismic stratum apparent thickness grid data includes:

[0019] Obtain seismic data of the target layer in the study area;

[0020] The depth value between the top and bottom surfaces of the target layer in the study area is obtained based on seismic data;

[0021] Determine the apparent thickness of the seismic strata in the target layer of the study area according to the depth value;

[0022] According to the apparent thickness of the seismic strata in the target layer section of the study area, the seismic strata apparent thickness grid data is determined.

[0023] Preferably, the seismic data includes two-dimensional seismic data and three-dimensional seismic data.

[0024] Preferably, the determining of the argillaceous rock thickness plane grid data of the target layer according to the argillaceous rock to formation ratio grid data and the seismic formation apparent thickness grid data comprises:

[0025] The argillaceous rock thickness plane grid data of the target layer is determined according to the product of the argillaceous rock to formation ratio grid data and the seismic formation apparent thickness grid data.

[0026] The present invention also provides a system for predicting argillaceous rock thickness data, the system comprising:

[0027] A collection module is used to collect logging data of the target layer to be predicted after drilling, and determine the apparent thickness of the reservoir section of the target layer based on the logging data;

[0028] A first determination module is used to determine the apparent thickness of the argillaceous rock of the target layer according to the apparent thickness of the reservoir layer, and to determine the grid data of the argillaceous rock to formation ratio according to the apparent thickness of the argillaceous rock;

[0029] The second determination module is used to collect seismic data and seismic layer data in the study area, obtain the seismic stratum apparent thickness of the target layer section in the study area, and determine the seismic stratum apparent thickness grid data;

[0030] The data processing module is used to determine the argillaceous rock thickness plane grid data of the target layer segment based on the argillaceous rock to formation ratio grid data and the seismic formation apparent thickness grid data.

[0031] Preferably, the collection module is used to determine the apparent thickness of the reservoir section of the target layer according to the well logging data, including:

[0032] The collection module is used to establish the logging corresponding characteristics of the reservoir section in the target layer according to the logging data, identify the reservoir section, and obtain the apparent thickness of the reservoir section.

[0033] Preferably, the first determining module is used to determine the apparent thickness of mudstone in the target layer according to the apparent thickness of the reservoir layer, including:

[0034] The first determination module is used to obtain the total thickness of the target layer;

[0035] The apparent thickness of the mudstone in the target layer is determined based on the difference between the total thickness of the target layer and the apparent thickness of the reservoir section.

[0036] Preferably, the first determining module is used to determine the argillaceous rock to formation ratio grid data according to the apparent thickness of the argillaceous rock, including:

[0037] The first determination module is used to obtain the apparent thickness of the target layer;

[0038] The argillaceous rock to formation ratio grid data is constructed based on the ratio of the apparent thickness of the argillaceous rock to the apparent thickness of the bottom layer.

[0039] Preferably, the second determination module is used to obtain the seismic stratum apparent thickness of the target layer section in the study area and determine the seismic stratum apparent thickness grid data, including:

[0040] The second determination module is used to obtain seismic data of the target layer section in the study area;

[0041] The depth value between the top and bottom surfaces of the target layer in the study area is obtained based on seismic data;

[0042] Determine the apparent thickness of the seismic strata in the target layer of the study area according to the depth value;

[0043] According to the apparent thickness of the seismic strata in the target layer section of the study area, the seismic strata apparent thickness grid data is determined.

[0044] Preferably, the data processing module is used to determine the argillaceous rock thickness plane grid data of the target layer according to the argillaceous rock to formation ratio grid data and the seismic formation apparent thickness grid data, including:

[0045] The data processing module is used to determine the argillaceous rock thickness plane grid data of the target layer according to the product of the argillaceous rock to formation ratio grid data and the seismic formation apparent thickness grid data.

[0046] The present invention also provides a device for predicting argillaceous rock thickness data, comprising:

[0047] processor and memory;

[0048] The processor calls the computer program stored in the memory to execute any one of the above-mentioned methods for predicting argillaceous rock thickness data.

[0049] The present invention also provides a computer-readable storage medium,

[0050] The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor is enabled to execute any one of the above-mentioned methods for predicting argillaceous rock thickness data.

[0051] The present invention has the following beneficial effects:

[0052] The present invention mainly obtains a method for predicting the planar distribution range of hydrocarbon generation centers in salt lake facies mixed sedimentary rock development areas under seismic horizon constraints, so as to solve the problems that the distribution range of pure mudstone in mixed sedimentary rock development areas cannot fully represent the range of hydrocarbon generation centers, mudstone logging is difficult to identify, various types of lithofacies cannot be distinguished, resulting in difficulty in predicting the range of hydrocarbon generation centers, and there are few wells drilled in the area and incomplete logging data, resulting in low accuracy in calculating the cumulative thickness of mudstone and inaccurate planar prediction.

[0053] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0055] Figure 1 A diagram showing a method for predicting argillaceous rock thickness data according to an embodiment of the present invention;

[0056] Figure 2 A schematic diagram showing the response characteristics of clastic rock and limestone reservoirs in the northwest region of Qaidam according to an embodiment of the present invention;

[0057] Figure 3 A schematic diagram of dividing reservoir sections in an embodiment of the present invention is shown;

[0058] Figure 4 A plan view showing the thickness ratio of mudstone to stratum in the northwest region of Qaidam according to an embodiment of the present invention is shown;

[0059] Figure 5 The plane distribution diagram of hydrocarbon generation center in northwest China in the embodiment of the present invention is shown;

[0060] Figure 6 A diagram showing a prediction system for mudstone thickness data according to an embodiment of the present invention;

[0061] Figure 7 A diagram showing a device for predicting mudstone thickness data in an embodiment of the present invention. DETAILED DESCRIPTION

[0062] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that the present disclosure will be more comprehensive and complete and will fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced while omitting one or more of the specific details, or that other methods, components, devices, steps, etc. may be employed. In other cases, well-known technical solutions are not shown or described in detail to avoid obscuring various aspects of the present disclosure.

[0063] In addition, the accompanying drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware units or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0064] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all steps. For example, some steps may be decomposed, while some steps may be combined or partially combined, so the actual execution order may change according to actual circumstances.

[0065] The terms "first," "second," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, such that the embodiments of the application described herein can, for example, be implemented in an order other than that illustrated or described herein.

[0066] In addition, the terms "comprise" and "have" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus that includes a series of steps or submodules is not necessarily limited to those steps or submodules explicitly listed, but may include other steps or submodules not explicitly listed or inherent to such process, method, product or apparatus.

[0067] like Figure 1 As shown, the present invention proposes a method for predicting argillaceous rock thickness data, which includes the following steps:

[0068] S1 collects the well logging data of the target layer to be predicted that has been drilled, and determines the apparent reservoir thickness of the target layer based on the well logging data;

[0069] S2 determines the apparent thickness of the argillaceous rock in the target layer according to the apparent thickness of the reservoir layer, and determines the grid data of the ratio of argillaceous rock to formation according to the apparent thickness of the argillaceous rock;

[0070] S3 collects seismic data and seismic layer data in the study area, obtains the seismic stratum apparent thickness of the target layer in the study area, and determines the seismic stratum apparent thickness grid data;

[0071] S4 determines the argillaceous rock thickness plane grid data of the target layer segment based on the argillaceous rock to formation ratio grid data and the seismic formation apparent thickness grid data.

[0072] Specifically, the logging data of the target interval in S1 include: uranium-free gamma and shallow lateral resistivity logging data of the mixed sedimentary layer;

[0073] S1 determines the apparent thickness of the reservoir section of the target layer based on the well logging data, including:

[0074] The logging characteristics of the reservoir section in the target layer are established based on the logging data, the reservoir section is identified, and the apparent thickness of the reservoir section is obtained.

[0075] Specifically, S2 determines the apparent thickness of the mudstone in the target layer according to the apparent thickness of the reservoir layer, including:

[0076] Get the total thickness of the target layer;

[0077] The apparent thickness of the mudstone in the target layer is determined based on the difference between the total thickness of the target layer and the apparent thickness of the reservoir section.

[0078] Specifically, S2 determines the argillaceous rock to formation ratio grid data based on the apparent thickness of the argillaceous rock, including:

[0079] Obtain the apparent thickness of the target layer;

[0080] The argillaceous rock to formation ratio grid data is constructed based on the ratio of the apparent thickness of the argillaceous rock to the apparent thickness of the bottom layer.

[0081] Specifically, S3 obtains the seismic stratigraphic apparent thickness of the target layer in the study area and determines the seismic stratigraphic apparent thickness grid data, including:

[0082] Acquire seismic data of the target layer in the study area; wherein the seismic data includes two-dimensional seismic data and three-dimensional seismic data;

[0083] The depth value between the top and bottom surfaces of the target layer in the study area is obtained based on seismic data;

[0084] Determine the apparent thickness of the seismic strata in the target layer of the study area according to the depth value;

[0085] According to the apparent thickness of the seismic strata in the target layer section of the study area, the seismic strata apparent thickness grid data is determined.

[0086] Specifically, S4 determines the argillaceous rock thickness plane grid data of the target layer according to the argillaceous rock to formation ratio grid data and the seismic formation apparent thickness grid data, including:

[0087] The argillaceous rock thickness plane grid data of the target layer is determined according to the product of the argillaceous rock to formation ratio grid data and the seismic formation apparent thickness grid data.

[0088] This method is applicable to areas with developed mixed sedimentary facies. It has two advantages: first, it includes transitional facies such as muddy limestone, gray mudstone, and silty mudstone into the hydrocarbon generation center, which is more consistent with the actual situation; second, it solves the problems of difficult mudstone logging identification in areas with developed mixed sedimentary facies, lack of regional well data, and discontinuous logging data of the target layer.

[0089] Example 1: Taking the study of the hydrocarbon generation center of the salt lake mixed sedimentary facies in the northwest area of ​​Qaidam Basin as an example, the patent of the present invention is further explained in detail.

[0090] N1-N2 in the northwest of Qaidam Basin 1 The sedimentary structure during this period was generally shallow lake facies, with distinct lithologic mixed-depositional characteristics and diverse mineral compositions, primarily quartz, feldspar, carbonate minerals, and clay minerals. The mixed-depositional characteristics were evident, with carbonate minerals predominating, and pyrite and anhydrite commonly present.

[0091] The following steps are mainly used:

[0092] 1. In this case, we selected the northwest of Qaidam as the study area and collected uranium-free gamma-ray and shallow lateral resistivity logging data from the mixed sedimentary rock intervals in the study area. We conducted quality control on this data, eliminated data with severe wellbore expansion, and obtained high-quality data as the sample data set.

[0093] 2. Integrate conventional logging data and core X-ray diffraction mineral data to establish reservoir logging identification parameters. In this case, after comprehensive analysis of various conventional curves, it is believed that the response characteristics of clastic rock and limestone reservoirs in the northwest Qaidam area are low uranium-free gamma radiation (GR) and high resistivity shallow lateral resistivity (LLS). In this case, the threshold values ​​are GR < 55 API and LLS > 7 Ω·m. Figure 2 As shown;

[0094] 3. Based on the previously determined reservoir logging response characteristics, identify and divide the reservoir segments, such as Figure 3 As shown;

[0095] 4. Subtract the total thickness of the reservoir section from the total thickness of the formation to obtain the total thickness of the mudstone in the target section;

[0096] 5. Using the identified reservoir sections and geological stratification data, calculate the shale / formation thickness ratio data of the target layer section where the well has been drilled, as shown in Table 1;

[0097] Table 1

[0098]

[0099] 6. Based on the mudstone / formation thickness ratio data of the target layer of a single well, the mudstone / formation thickness ratio regional grid data is calculated, such as Figure 4 As shown;

[0100] 7. Obtaining the apparent thickness of the target formation based on the acquired two-dimensional seismic data and three-dimensional seismic data, including: obtaining a depth value between a top surface and a bottom surface of the target formation from the two-dimensional seismic data and the three-dimensional seismic data; and performing subtraction calculation on the depth value to obtain the apparent thickness of the target formation.

[0101] 8. Multiply the argillaceous rock to formation ratio grid data with the formation apparent thickness grid data to obtain the argillaceous rock thickness grid data and compile a plan view, such as Figure 5 As shown;

[0102] 9. The mudstone apparent thickness plan map drawn using the above method and device is used for geological research such as source rock evaluation and sedimentary phase analysis.

[0103] like Figure 6 As shown, the present invention also proposes a prediction system for argillaceous rock thickness data, the system comprising:

[0104] The collection module 10 is used to collect the logging data of the target layer section to be predicted after drilling, and determine the apparent thickness of the reservoir section of the target layer section based on the logging data;

[0105] A first determining module 20 is configured to determine the apparent thickness of the argillaceous rock of the target layer according to the apparent thickness of the reservoir layer, and to determine the grid data of the argillaceous rock to formation ratio according to the apparent thickness of the argillaceous rock;

[0106] The second determination module 30 is used to collect seismic data and seismic layer data in the study area, obtain the seismic stratum apparent thickness of the target layer section in the study area, and determine the seismic stratum apparent thickness grid data;

[0107] The data processing module 40 is used to determine the argillaceous rock thickness plane grid data of the target layer segment based on the argillaceous rock to formation ratio grid data and the seismic formation apparent thickness grid data.

[0108] like Figure 7As shown, corresponding to the above-mentioned method for predicting argillaceous rock thickness data, the present invention also provides a device for predicting argillaceous rock thickness data. Because the embodiment of this device is similar to the above-mentioned method embodiment, the description is relatively simple. For relevant details, please refer to the description of the above-mentioned method embodiment. The device described below is merely illustrative. The device may include: a processor 1, a memory 2, a communication bus (i.e., the above-mentioned device bus), and a search engine. The processor 1 and memory 2 communicate with each other via the communication bus and communicate with the outside world via a communication interface. The processor 1 can call logic instructions in the memory 2 to execute the method for predicting argillaceous rock thickness data.

[0109] In addition, the logic instructions in the above-mentioned memory 2 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: a memory chip, a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0110] On the other hand, an embodiment of the present invention further provides a processor-readable storage medium, on which a computer program 3 is stored. When the computer program 3 is executed by the processor 1, the method for predicting mudstone thickness data provided in the above embodiments is implemented.

[0111] The processor-readable storage medium can be any available medium or data storage device that can be accessed by the processor 1, including but not limited to magnetic storage (such as floppy disks, hard disks, tapes, magneto-optical disks (MO)), optical storage (such as CDs, DVDs, BDs, HVDs, etc.), and semiconductor storage (such as ROMs, EPROMs, EEPROMs, non-volatile memories (NANDFLASH), solid-state drives (SSDs)), etc.

[0112] Those skilled in the art should understand that although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible to modify the technical solutions described in the aforementioned embodiments, or to make equivalent replacements for some of the technical features therein; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting argillaceous rock thickness data, characterized in that: The method comprises: Collect well logging data of the target layer to be predicted that has been drilled, and determine the apparent reservoir thickness of the target layer based on the well logging data; Determine the apparent thickness of the argillaceous rock in the target layer based on the apparent thickness of the reservoir layer, and determine the grid data of the argillaceous rock to formation ratio based on the apparent thickness of the argillaceous rock; Collect seismic data and seismic layer data in the study area, obtain the apparent thickness of the seismic strata in the target layer of the study area, and determine the grid data of the apparent thickness of the seismic strata; The argillaceous rock thickness plane grid data of the target layer is determined based on the argillaceous rock to formation ratio grid data and the seismic formation apparent thickness grid data.

2. The method for predicting argillaceous rock thickness data according to claim 1, characterized in that: The logging data of the target layer section include: uranium-free gamma and shallow lateral resistivity logging data of the mixed rock layer.

3. The method for predicting argillaceous rock thickness data according to claim 1, characterized in that: Determining the apparent reservoir thickness of the target layer according to the well logging data includes: The logging characteristics of the reservoir section in the target layer are established based on the logging data, the reservoir section is identified, and the apparent thickness of the reservoir section is obtained.

4. The method for predicting argillaceous rock thickness data according to claim 1, characterized in that: Determining the apparent thickness of the mudstone in the target layer according to the apparent thickness of the reservoir layer includes: Get the total thickness of the target layer; The apparent thickness of the mudstone in the target layer is determined based on the difference between the total thickness of the target layer and the apparent thickness of the reservoir section.

5. The method for predicting argillaceous rock thickness data according to claim 1, characterized in that: The method of determining the argillaceous rock to formation ratio grid data based on the apparent thickness of the argillaceous rock includes: Obtain the apparent thickness of the target layer; The argillaceous rock to formation ratio grid data is constructed according to the ratio of the argillaceous rock apparent thickness to the formation apparent thickness.

6. The method for predicting argillaceous rock thickness data according to claim 1, characterized in that: The method of obtaining the seismic stratum apparent thickness of the target layer section in the study area and determining the seismic stratum apparent thickness grid data includes: Obtain seismic data of the target layer in the study area; The depth value between the top and bottom surfaces of the target layer in the study area is obtained based on seismic data; Determine the apparent thickness of the seismic strata in the target layer of the study area according to the depth value; According to the apparent thickness of the seismic strata in the target layer section of the study area, the seismic strata apparent thickness grid data is determined.

7. The method for predicting argillaceous rock thickness data according to claim 6, characterized in that: The seismic data includes two-dimensional seismic data and three-dimensional seismic data.

8. The method for predicting argillaceous rock thickness data according to claim 1, characterized in that: The method of determining the argillaceous rock thickness plane grid data of the target layer according to the argillaceous rock to formation ratio grid data and the seismic formation apparent thickness grid data includes: The argillaceous rock thickness plane grid data of the target layer is determined according to the product of the argillaceous rock to formation ratio grid data and the seismic formation apparent thickness grid data.

9. A system for predicting argillaceous rock thickness data, characterized in that: The system comprises: A collection module is used to collect logging data of the target layer to be predicted after drilling, and determine the apparent thickness of the reservoir section of the target layer based on the logging data; A first determination module is used to determine the apparent thickness of the argillaceous rock of the target layer according to the apparent thickness of the reservoir layer, and to determine the grid data of the argillaceous rock to formation ratio according to the apparent thickness of the argillaceous rock; The second determination module is used to collect seismic data and seismic layer data in the study area, obtain the seismic stratum apparent thickness of the target layer section in the study area, and determine the seismic stratum apparent thickness grid data; The data processing module is used to determine the argillaceous rock thickness plane grid data of the target layer segment based on the argillaceous rock to formation ratio grid data and the seismic formation apparent thickness grid data.

10. The system for predicting argillaceous rock thickness data according to claim 9, characterized in that: The collection module is used to determine the apparent thickness of the reservoir section of the target layer according to the well logging data, including: The collection module is used to establish the logging corresponding characteristics of the reservoir section in the target layer according to the logging data, identify the reservoir section, and obtain the apparent thickness of the reservoir section.

11. The system for predicting argillaceous rock thickness data according to claim 9, characterized in that: The first determination module is used to determine the apparent thickness of mudstone in the target layer according to the apparent thickness of the reservoir layer, including: The first determination module is used to obtain the total thickness of the target layer; The apparent thickness of the mudstone in the target layer is determined based on the difference between the total thickness of the target layer and the apparent thickness of the reservoir section.

12. The system for predicting argillaceous rock thickness data according to claim 9, characterized in that: The first determination module is used to determine the argillaceous rock to formation ratio grid data according to the apparent thickness of the argillaceous rock, including: The first determination module is used to obtain the apparent thickness of the target layer; The argillaceous rock to formation ratio grid data is constructed based on the ratio of the apparent thickness of the argillaceous rock to the apparent thickness of the bottom layer.

13. The system for predicting argillaceous rock thickness data according to claim 9, characterized in that: The second determination module is used to obtain the seismic stratum apparent thickness of the target layer section in the study area and determine the seismic stratum apparent thickness grid data, including: The second determination module is used to obtain seismic data of the target layer section in the study area; The depth value between the top and bottom surfaces of the target layer in the study area is obtained based on seismic data; Determine the apparent thickness of the seismic strata in the target layer of the study area according to the depth value; According to the apparent thickness of the seismic strata in the target layer section of the study area, the seismic strata apparent thickness grid data is determined.

14. The system for predicting argillaceous rock thickness data according to claim 9, characterized in that: The data processing module is used to determine the argillaceous rock thickness plane grid data of the target layer according to the argillaceous rock to formation ratio grid data and the seismic formation apparent thickness grid data, including: The data processing module is used to determine the argillaceous rock thickness plane grid data of the target layer according to the product of the argillaceous rock to formation ratio grid data and the seismic formation apparent thickness grid data.

15. A device for predicting mudstone thickness data, characterized in that: include: processor and memory; The processor calls the computer program stored in the memory to execute the method for predicting mudstone thickness data according to any one of claims 1 to 8.

16. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor is enabled to execute the method for predicting mudstone thickness data according to any one of claims 1 to 8.

Images