Continental facies shale seismic anisotropy prediction method based on data driving and storage medium
By collecting ultrasonic test data of terrestrial shale, an exponential relationship between shale velocity and anisotropy parameters was established, and combined with water body salinity classification, the problem of seismic anisotropy prediction of terrestrial shale was solved, and high-accurate prediction results were achieved, providing reliable technical support for related fields.
Patent Information
- Application Number
- CN202411809258.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-10
- Publication Date
- 2025-04-04
AI Technical Summary
The prior art is difficult to effectively predict seismic anisotropy of terrestrial shales, especially due to the complex sedimentary environment and multiple factors of terrestrial shales, which makes it difficult for model-driven methods to fully characterize their anisotropy characteristics.
By collecting laboratory core ultrasonic test data of terrestrial organic matter-rich shale, the exponential change relationship between vertical longitudinal wave and transverse wave velocity and longitudinal and transverse wave anisotropy parameters were established, and the shale was predicted using a data-driven method, and the shale was classified according to the salinity of water bodies, and an empirical relationship model of different types of shale was constructed.
Accurate prediction of the anisotropy of terrestrial shale seismic anisotropy is achieved, which significantly reduces prediction errors, improves the accuracy and reliability of predictions, and provides more reliable seismic data support for geological exploration and oil and gas resource development.
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Figure CN119758439A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of geophysical technology, and in particular to a data-driven terrestrial shale seismic anisotropy prediction method and storage medium. Background Art
[0002] Shale in nature is a transversely isotropic medium with significant strong anisotropy due to the obvious bedding. This acoustic and mechanical anisotropy brings many challenges to seismic data processing, logging evaluation, reservoir sweet spot prediction, and fracturing design and construction, and has become the focus of extensive research. At present, most studies rely on rock physics experiments to explore the factors affecting shale anisotropy and construct elastic property models of unconventional shale reservoirs through model-driven methods. However, this model-driven approach has obvious limitations in characterizing the complex rock physical properties of natural shale reservoirs, and is mostly applicable to marine shale reservoirs. The anisotropy of shale is mainly driven by factors such as the directional arrangement of clay particles, layered organic matter parallel to the bedding, the distribution of microcracks, diagenesis, and thermal maturity. The mutual coupling between these factors further increases the complexity of understanding the source of shale elastic anisotropy.
[0003] In addition, compared with marine shales, continental organic-rich shales have stronger heterogeneity, complex lithology, rapid sedimentary phase change, high clay mineral content and low thermal evolution, which makes their anisotropic response mechanism more complicated. Therefore, the current understanding of how these multiple factors affect the elastic anisotropy of shales is still unclear, and it is difficult to fully characterize the anisotropic characteristics of shales by simply relying on model-driven research. Summary of the invention
[0004] The purpose of the present invention is to provide a data-driven method and storage medium for predicting the seismic anisotropy of continental shale in order to overcome the defects of the above-mentioned prior art. Based on the ultrasonic velocity test data of laboratory cores of continental shale reservoirs, an exponential relationship between the vertical P-wave and S-wave velocities and the P-wave and S-wave anisotropy parameters (epsilon and gamma) is established. The P-wave and S-wave velocities perpendicular to the bedding are generally easier to obtain from seismic data inversion, sonic logging, etc. Therefore, the empirical relationship between the anisotropy parameters and the wave velocities perpendicular to the bedding of the present invention can effectively solve the problem of predicting the seismic anisotropy of continental organic shale.
[0005] The purpose of the present invention can be achieved by the following technical solutions:
[0006] The first aspect of the present invention provides a data-driven method for predicting continental shale seismic anisotropy, comprising the following steps:
[0007] S1: Data acquisition:
[0008] Collect laboratory core ultrasonic test data of continental organic-rich shales, where the core data includes the P-wave velocity, S-wave velocity and corresponding anisotropy parameters perpendicular to the bedding;
[0009] S2: Data classification:
[0010] Based on the key control of the sedimentary environment on the elastic properties of rocks and the complex and changeable characteristics of the sedimentary environment of continental shale, the classification basis is determined, and the continental shale is classified according to the classification basis;
[0011] S3: Model building:
[0012] Using the data obtained from S1 and based on the classification results of S2, a quantifiable prediction model for different types of shales is established;
[0013] S4: Model application:
[0014] The logging elastic velocity is used as input and substituted into the prediction model for different types of shales established by S3, thereby realizing the prediction of seismic anisotropy of continental shale.
[0015] Furthermore, S1 specifically includes the following steps:
[0016] From the numerous core ultrasonic test data collected, the relevant data belonging to the continental organic-rich shale are selected, and the data records of the P-wave velocity, S-wave velocity and the corresponding anisotropy parameters that completely contain the vertical bedding are screened out, and the parts with missing data or data that do not meet the requirements are excluded;
[0017] Based on the screened qualified data, the longitudinal wave velocity, shear wave velocity and anisotropy parameter data are classified separately to form a standardized data table or data file.
[0018] Furthermore, the corresponding anisotropy parameters specifically include two items ε and γ.
[0019] Furthermore, in S2, the classification is based on water salinity.
[0020] Furthermore, S2 specifically includes the following steps:
[0021] Analyze the internal mechanism of the influence of sedimentary environment on the elastic properties of continental shale rocks, consider the complex and changeable characteristics of continental shale sedimentary environment, and use this as a theoretical basis;
[0022] Determine the water salinity as the classification basis for terrestrial shales, and demonstrate the rationality and scientificity of the classification basis by analyzing the correlation between water salinity and the elastic properties of terrestrial shales and the differences in different sedimentary environments;
[0023] According to the selected water salinity, the terrestrial shale related data collected in data acquisition S1 are classified into two categories: freshwater-brackish lake shale and saltwater lake shale, and the characteristics and data range corresponding to each type of shale are clearly defined.
[0024] Furthermore, in S3, model building includes the following steps:
[0025] For the shales of different sedimentary environments divided in S2, the vertical P-wave velocity and the anisotropy parameter ε, as well as the vertical S-wave velocity and the anisotropy parameter γ, are exponentially fitted to construct an empirical relationship between the vertical bedding velocity and the anisotropy parameter. For the freshwater-brackish lake phase shales, the first exponential equation is used to describe the relationship between the anisotropy and velocity, and for the saltwater lake phase shales, the second exponential equation is used to describe it.
[0026] Furthermore, in S3, model building specifically includes the following steps:
[0027] According to the two types of shales of different sedimentary environments, freshwater-slightly brackish water lake facies shales and saltwater lake facies shales classified in the data classification step S2, the vertical P-wave velocity and the anisotropy parameter ε and the vertical S-wave velocity and the anisotropy parameter γ are determined as fitting data objects for each type of shale;
[0028] The exponential fitting method is selected as a means to construct the empirical relationship between vertical bedding velocity and anisotropy parameters, and its applicability and advantages in shale data processing and reflecting the correlation relationship are analyzed;
[0029] For freshwater-slightly brackish lake facies shales, the exponential fitting method is used to fit the vertical P-wave velocity and anisotropy parameter ε, as well as the vertical S-wave velocity and anisotropy parameter γ. The first exponential equation is determined by combining the data change trend and error analysis. The operation is repeated for the saltwater lake facies shales to obtain the second exponential equation, thereby constructing the empirical relationship between the vertical bedding velocity and anisotropy parameters of different types of shales.
[0030] Furthermore, the first exponential equation describes the relationship between the anisotropy and velocity of freshwater-brackish lacustrine shale, specifically:
[0031] ε=8.7591*exp(-0.8825*V p0 )
[0032] γ=28.3816*exp(-2.0173*V s0 )
[0033] Among them, V p0 represents the longitudinal wave velocity, V S0 Represents the shear wave velocity.
[0034] Furthermore, the second exponential equation describes the relationship between the anisotropy and velocity of the saltwater lake shale, specifically:
[0035] ε=3.6903*exp(-0.9154*V p0 )
[0036] ε=3.8817*exp(-1.6816*V s0 )
[0037] Among them, V p0 represents the longitudinal wave velocity, V S0 Represents the shear wave velocity.
[0038] A second aspect of the present invention provides a storage medium comprising computer executable instructions, which, when executed by a computer processor, is used to execute the data-driven continental shale seismic anisotropy prediction method as described above.
[0039] Compared with the prior art, the present invention has the following beneficial effects:
[0040] 1) In the field of prediction of seismic anisotropy of continental shale, the present invention has groundbreaking significance. Previously, this field lacked an effective prediction method specifically for continental shale, and the emergence of the present invention fills this gap. Through unique technical ideas and research methods, a systematic continental shale seismic anisotropy prediction system has been established for the first time, providing a new direction and foundation for subsequent related research and practice, greatly promoting the development and progress of technology in this field, and is expected to trigger a series of innovative explorations and technological innovations around continental shale seismic research.
[0041] 2) Based on a deep understanding of the complexity of the sedimentary environment of continental shale, the present invention innovatively classifies lacustrine shales into two categories: freshwater-brackish lacustrine shales and saltwater lacustrine shales according to the salinity of water bodies. For different categories, accurate empirical relationships between anisotropy and vertical velocity are constructed respectively. This refined classification modeling method fully considers the differences in the physical properties of shale in different water salinity environments, and effectively avoids the limitations of traditional unified models in dealing with complex situations of continental shales. Verification through a large amount of experimental data and actual case applications have shown that this method can significantly reduce prediction errors and greatly improve the accuracy of seismic anisotropy prediction of continental shales, providing more reliable seismic data support and decision-making basis for geological exploration, oil and gas resource development and other fields, which will help improve the success rate of resource exploration and development benefits.
[0042] 3) The present invention adopts the core technical concept of data-driven, closely combined with the rich data resources actually obtained. The entire prediction method process design is concise and clear, the operation steps are simple and easy, and there is no need for complex and expensive experimental equipment and tedious calculation processes. This allows the method to be efficiently integrated into the actual workflow of seismic anisotropy prediction of continental shale, greatly shortening the cycle from data collection to output of prediction results, significantly improving work efficiency, and reducing manpower, material and time costs, providing highly competitive technical means and strong support for the development of continental shale related industries. BRIEF DESCRIPTION OF THE DRAWINGS
[0043] Figure 1 The relationship between elastic anisotropy parameters and vertical velocity of freshwater-slightly brackish water lacustrine shale in the embodiment. (a) ε and V p0 Relationship, (b) γ and V s0 Relationship, the solid line represents the exponential fitting result of the experimental data;
[0044] Figure 2 The relationship between elastic anisotropy parameters and vertical velocity of saline lake shale in the example. (a) ε and V p0 Relationship, (b) γ and V s0 Relationship, the solid line represents the exponential fitting result of the experimental data;
[0045] Figure 3 This is an example of using the data-driven continental shale seismic anisotropy-velocity model to predict the anisotropy of a shale oil reservoir in the Songliao Basin. DETAILED DESCRIPTION
[0046] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. Component models, material names, connection structures, control methods, algorithms and other features not clearly described in this technical solution are all considered to be common technical features disclosed in the prior art.
[0047] Example 1
[0048] This embodiment provides an economical and simple method for predicting seismic anisotropy of continental shale. Based on the ultrasonic test data of laboratory cores, an empirical relationship between the anisotropy parameters of longitudinal and transverse waves (epsilon and gamma) and the longitudinal and transverse wave velocities perpendicular to the bedding is established, which provides a concise and effective method for predicting seismic anisotropy parameters of longitudinal and transverse waves. This method has important practical application value in anisotropic velocity modeling and seismic imaging, anisotropic prestack seismic inversion, and geomechanical modeling.
[0049] The specific technical solutions of this embodiment include:
[0050] Step 1: Data acquisition: Collect laboratory core ultrasonic test data of continental organic-rich shales. The core data include P-wave velocity and S-wave velocity vertical to the bedding and the corresponding anisotropy parameters (epsilon and gamma).
[0051] In the specific implementation, in the study of seismic anisotropy prediction of continental shales, data acquisition is the basic and critical first step. Focusing on continental organic-rich shales, this type of shales has become a research focus due to their special geological composition and resource potential. Through cooperation and resource integration with many professional geological laboratories, scientific research institutions and related enterprises, their laboratory core ultrasonic test data are widely collected. Among them, the longitudinal wave velocity of the vertical bedding reflects the compression wave characteristics of the seismic wave when it propagates in the shale along the vertical bedding direction, while the shear wave velocity reflects the propagation of the shear wave. The corresponding anisotropic parameters epsilon and gamma are the key indicators for accurately quantifying the anisotropic characteristics of shale. For example, epsilon is closely related to the degree of influence of shale bedding structure on the anisotropy of longitudinal wave propagation, and gamma is more related to the change law of shear waves in anisotropic shale media.
[0052] Step 2: Data classification: In view of the important control of sedimentary environment on the elastic properties of rocks and the complex and changeable sedimentary environment of continental shales, continental shales are divided into two categories according to the salinity of water bodies: freshwater-brackish lake shales and saltwater lake shales.
[0053] In specific implementation, the sedimentary environment plays a decisive role in shaping the elastic properties of rocks. For continental shales, the complexity and variability of their sedimentary environments are particularly prominent. In this context, the present invention uses water salinity as the classification basis to divide continental shales. Differences in water salinity will lead to significant differences in its mineral composition, pore structure, and internal microstructure of the rock during the shale formation process, which will profoundly affect the elastic properties of the rock. It is of great significance to divide continental shales into two categories: freshwater-brackish lake shales and saltwater lake shales. For example, freshwater-brackish lake shales may be affected by low-salinity water bodies during their formation, and their clay mineral composition, organic matter distribution, and pore connectivity are significantly different from those of saltwater lake shales. These differences will be reflected in the elastic response of the rock, and ultimately reflected in the seismic anisotropy characteristics. Through this classification, we can more accurately establish models and analysis methods that fit the characteristics of different types of shales, thereby effectively improving the accuracy and reliability of seismic anisotropy predictions for continental shales, and providing a more scientific and detailed classification basis for subsequent model establishment and application.
[0054] Step 3: Model establishment: For shales in different sedimentary environments, the vertical P-wave velocity and the anisotropy parameter epsilon, as well as the vertical S-wave velocity and the anisotropy parameter gamma, are exponentially fitted to establish the empirical relationship between the vertical bedding velocity and the anisotropy parameters. For freshwater-brackish lacustrine shales, the elastic anisotropy parameters ε and γ decrease exponentially with the increase of the vertical P-wave velocity and S-wave velocity. The following exponential equation describes the relationship between the anisotropy and velocity of freshwater-brackish lacustrine shales (see Appendix Figure 1 ):
[0055] ε=8.7591*exp(-0.8825*V p0 )
[0056] γ=28.3816*exp(-2.0173*V s0 )
[0057] For the anisotropy-velocity relationship of saltwater lake shale (Appendix Figure 2 ):
[0058] ε=3.6903*exp(-0.9154*V p0 )
[0059] ε=3.8817*exp(-1.6816*V s0 )
[0060] Step 4: Model application: Use the logging elastic velocity as input and substitute it into the established exponential empirical relationship to predict the seismic anisotropy of continental shale.
[0061] This embodiment provides a data-driven method for predicting seismic anisotropy of continental shale. First, the vertical P-wave velocity, S-wave velocity and anisotropy parameters (epsilon and gamma) of freshwater-slightly brackish lake shale are collected, and then the vertical P-wave velocity and the anisotropy parameter epsilon and the S-wave velocity and the anisotropy parameter gamma are exponentially fitted to establish an empirical relationship between the vertical bedding velocity and the anisotropy parameter. This empirical relationship is applied to the logging data of freshwater lake shale, and the logging anisotropy prediction effect of a well in the Songliao Basin is verified.
[0062] Figure 3The comparison results of the anisotropy calculated by the data-driven continental shale seismic anisotropy prediction method in this paper and the measured anisotropy are presented. The results show that for unconventional shale reservoirs, the anisotropy parameters predicted by the empirical relationship are consistent with the measured value trend, proving the effectiveness and applicability of the model in anisotropy parameter prediction. The anisotropy prediction method of the present invention only needs to input the vertical velocity to easily obtain the anisotropy parameters (epsilon and gamma), which has the advantages of high efficiency and practicality.
[0063] This embodiment also provides a storage medium containing computer executable instructions, and when the storage medium of the computer executable instructions is executed by a computer processor, it is used to execute the above-mentioned data-driven terrestrial shale seismic anisotropy prediction method. The storage medium serves as a key carrier for information storage and instruction bearing, and the computer executable instructions stored inside are based on the data-driven concept. The processor first reads and preliminarily processes the collected laboratory core ultrasonic test data of terrestrial organic-rich shale, including extracting key data information such as the longitudinal wave velocity, shear wave velocity and corresponding anisotropy parameters (epsilon and gamma) of the vertical stratification; then, according to the classification rules preset in the instructions, the terrestrial shale data is classified into two categories according to the salinity of the water body, freshwater-slightly brackish lake shale and saltwater lake shale, so as to facilitate subsequent Targeted processing; then in the model building stage, the processor exponentially fits the vertical P-wave velocity and epsilon, and the vertical S-wave velocity and gamma of different types of shales to build an accurate empirical relationship model; finally, according to the model application instructions, the logging elastic velocity is input into the established model to complete the prediction of seismic anisotropy of continental shale. The whole process is automated, intelligent and highly accurate, which greatly improves the efficiency and reliability of seismic anisotropy prediction of continental shale, and provides strong technical support and decision-making basis for related geological research and resource exploration and development.
[0064] The above description of the embodiments is to facilitate the understanding and use of the invention by those skilled in the art. It is obvious that those skilled in the art can easily make various modifications to these embodiments and apply the general principles described herein to other embodiments without creative work. Therefore, the present invention is not limited to the above embodiments, and improvements and modifications made by those skilled in the art based on the disclosure of the present invention without departing from the scope of the present invention should be within the scope of protection of the present invention.
Claims
1. A data-driven method for predicting continental shale seismic anisotropy, characterized in that: The following steps are involved: S1: Data acquisition: Collect laboratory core ultrasonic test data of continental organic-rich shales, where the core data includes the P-wave velocity, S-wave velocity and corresponding anisotropy parameters perpendicular to the bedding; S2: Data classification: Based on the key control of the sedimentary environment on the elastic properties of rocks and the complex and changeable characteristics of the sedimentary environment of continental shale, the classification basis is determined, and the continental shale is classified according to the classification basis; S3: Model building: Using the data obtained from S1 and based on the classification results of S2, a quantifiable prediction model for different types of shales is established; S4: Model application: The logging elastic velocity is used as input and substituted into the prediction model for different types of shales established by S3, thereby realizing the prediction of seismic anisotropy of continental shale.
2. The data-driven continental shale seismic anisotropy prediction method according to claim 1, characterized in that: S1 specifically includes the following steps: From the numerous core ultrasonic test data collected, the relevant data belonging to the continental organic-rich shale were selected, and the data records of the P-wave velocity, S-wave velocity and the corresponding anisotropy parameters that completely contain the vertical bedding were screened out, and the parts with missing data or those that do not meet the requirements were excluded; Based on the screened qualified data, the longitudinal wave velocity, shear wave velocity and anisotropy parameter data are classified separately to form a standardized data table or data file.
3. The data-driven continental shale seismic anisotropy prediction method according to claim 2, characterized in that: The corresponding anisotropy parameters specifically include two items: ε and γ.
4. The data-driven continental shale seismic anisotropy prediction method according to claim 2, characterized in that: In S2, the classification is based on water salinity.
5. The data-driven continental shale seismic anisotropy prediction method according to claim 4, characterized in that: S2 specifically includes the following steps: Analyze the internal mechanism of the influence of sedimentary environment on the elastic properties of continental shale rocks, consider the complex and changeable characteristics of continental shale sedimentary environment, and use this as a theoretical basis; Determine water salinity as the classification basis for continental shales, and demonstrate the rationality and scientificity of the classification basis by analyzing the correlation between water salinity and the elastic properties of continental shales and the differences in different sedimentary environments; According to the selected water salinity, the terrestrial shale related data collected in data acquisition S1 are classified into two categories: freshwater-brackish lake shale and saltwater lake shale, and the characteristics and data range corresponding to each type of shale are clearly defined.
6. The data-driven continental shale seismic anisotropy prediction method according to claim 1, characterized in that: In S3, model building includes the following steps: For the shales of different sedimentary environments divided in S2, the vertical P-wave velocity and the anisotropy parameter ε, as well as the vertical S-wave velocity and the anisotropy parameter γ, are exponentially fitted to construct an empirical relationship between the vertical bedding velocity and the anisotropy parameter. For the freshwater-brackish lake phase shales, the first exponential equation is used to describe the relationship between the anisotropy and velocity, and for the saltwater lake phase shales, the second exponential equation is used to describe it.
7. The data-driven continental shale seismic anisotropy prediction method according to claim 6, characterized in that: In S3, model building specifically includes the following steps: According to the two types of shales of different sedimentary environments, freshwater-slightly brackish water lake facies shales and saltwater lake facies shales classified in the data classification step S2, the vertical P-wave velocity and the anisotropy parameter ε and the vertical S-wave velocity and the anisotropy parameter γ are determined as fitting data objects for each type of shale; The exponential fitting method is selected as a means to construct the empirical relationship between vertical bedding velocity and anisotropy parameters, and its applicability and advantages in shale data processing and reflecting the correlation relationship are analyzed; For freshwater-slightly brackish lake facies shales, the exponential fitting method is used to fit the vertical P-wave velocity and anisotropy parameter ε, as well as the vertical S-wave velocity and anisotropy parameter γ. The first exponential equation is determined by combining the data change trend and error analysis. The second exponential equation is obtained for the saltwater lake facies shales, thus constructing the empirical relationship between the vertical bedding velocity and anisotropy parameters of different types of shales.
8. The data-driven method for predicting continental shale seismic anisotropy according to claim 6, characterized in that: The first exponential equation describes the relationship between the anisotropy and velocity of freshwater-brackish lacustrine shale, specifically: ε=8.7591*exp(-0.8825*V p0 ) γ=28.3816*exp(-2.0173*V s0 ) Among them, V p0 represents the longitudinal wave velocity, V S0 Represents the shear wave velocity.
9. The data-driven continental shale seismic anisotropy prediction method according to claim 6, characterized in that: The second exponential equation describes the relationship between anisotropy and velocity of saline lake shale, specifically: ε=3.6903*exp(-0.9154*V p0 ) ε=3.8817*exp(-1.6816*V s0 ) Among them, V p0 represents the longitudinal wave velocity, V S0 Represents the shear wave velocity.
10. A storage medium containing computer executable instructions, characterized in that: The storage medium of the computer executable instructions is used to execute the data-driven continental shale seismic anisotropy prediction method according to any one of claims 1 to 9 when executed by a computer processor.