Seismic petrophysical prediction method for key parameters of deep shale reservoir and related equipment

Abstract

The present disclosure provides a deep shale reservoir key parameter seismic rock physics prediction method and related equipment, and relates to the field of seismic rock physics. The method comprises the following steps: obtaining mineral component data, porosity data and hydrocarbon generation pyrolysis data of a target deep shale; based on the mineral component data and the porosity data, a lamellar clay polycrystalline orientation distribution function containing a microstructure correction term is established, and the equivalent elastic modulus of the lamellar clay is calculated based on the polycrystalline orientation distribution function; based on the hydrocarbon generation pyrolysis data, an organic pore interpretation model reflecting hydrocarbon generation is established, and the equivalent elastic modulus of organic matter and pore fluid is obtained; the equivalent elastic modulus of the lamellar clay and the equivalent elastic modulus of the organic matter and the pore fluid are combined to construct a deep shale seismic rock physics model; and the key parameters of the reservoir of the target deep shale are predicted by using the deep shale seismic rock physics model. The present disclosure can improve the reliability of the deep shale rock physics model.

Description

Technical Field

[0001] This disclosure relates to the field of seismic rock physics, and in particular to a seismic rock physics prediction method and related equipment for key parameters of deep shale reservoirs. Background Technology

[0002] Deep shale gas, as an important unconventional oil and gas resource, has become a crucial area for current oil and gas exploration and development. Accurate prediction of deep shale reservoir parameters (such as porosity and total organic carbon content) is of paramount importance for evaluating reservoir quality, delineating "sweet spots," and guiding horizontal well trajectory design. Currently, using seismic rock physics models to convert seismic elastic parameters into reservoir physical properties is the mainstream technique for reservoir prediction.

[0003] However, existing seismic rock physics modeling techniques for deep shale still have significant shortcomings when dealing with complex geological conditions, mainly in the following aspects:

[0004] First, traditional models, when constructing the framework of platy clay minerals, typically assume that clay particles exhibit an ideal parallel layered arrangement under compaction (i.e., a laterally isotropic medium). However, in actual sedimentary environments of deep shale, there is often a high proportion of non-platy particles (rigid particles) such as quartz and feldspar. The presence of these rigid particles significantly interferes with the directional arrangement of clay particles, disrupting the ideal layered structure.

[0005] Secondly, deep shale is rich in organic matter, which generates a large number of organic pores during thermal evolution. Existing rock physics models typically treat organic matter as a simple solid mineral component or use empirical formulas to estimate porosity, lacking quantitative characterization methods based on hydrocarbon generation kinetics. Specifically, existing models cannot accurately reflect the contribution of organic matter porosity to the overall elastic properties of the rock, thus reducing the accuracy of reservoir parameter inversion using seismic data.

[0006] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0007] This application discloses a seismic petrophysical prediction method and related equipment for key parameters of deep shale reservoirs, which helps to enhance the understanding of the influence of factors such as microstructure, mineral composition, and diagenesis on the elastic anisotropy of deep shale, and provides a petrophysical basis for the seismic description of sweet spots and compressibility of deep shale oil and gas reservoirs.

[0008] Other features and advantages of this disclosure will become apparent from the following detailed description, or may be learned in part from practice of this disclosure.

[0009] According to one aspect of this disclosure, a seismic petrophysical prediction method for key parameters of deep shale reservoirs is provided, comprising: acquiring mineral composition data, porosity data, and hydrocarbon generation pyrolysis data of the target deep shale; establishing a polycrystalline orientation distribution function of platy clay including a microstructure correction term based on the mineral composition data and porosity data, and calculating the equivalent elastic modulus of the platy clay based on the polycrystalline orientation distribution function; establishing an organic porosity interpretation model reflecting hydrocarbon generation based on the hydrocarbon generation pyrolysis data, and obtaining the equivalent elastic modulus of organic matter and pore fluids; constructing a seismic petrophysical model of deep shale by combining the equivalent elastic modulus of platy clay and the equivalent elastic modulus of organic matter and pore fluids; and using the seismic petrophysical model of deep shale to predict the key reservoir parameters of the target deep shale.

[0010] In one embodiment of this disclosure, establishing a polycrystalline orientation distribution function for platy clay that includes a microstructure correction term includes: obtaining the volume percentage of non-platy particles and the initial porosity in deep shale; determining an equivalent incompressible porosity correction term based on the ratio of the volume percentage of non-platy particles to the initial porosity; introducing the equivalent incompressible porosity correction term into the original compaction parameters to determine the corrected compaction parameters; and constructing a polycrystalline orientation distribution function for platy clay based on the corrected compaction parameters.

[0011] In one embodiment of this disclosure, an equivalent incompressible porosity correction term is determined based on the ratio of the volume percentage of non-plate-like particles to the initial porosity, and the corrected compaction parameters are determined. This includes: constructing a mapping relationship between the volume percentage of non-plate-like particles and the ratio of the initial porosity to determine the equivalent incompressible porosity correction term; and using the equivalent incompressible porosity correction term as a multiplier factor or a summing factor to perform calculations with the original compaction parameters to obtain the corrected compaction parameters.

[0012] In one embodiment of this disclosure, an organic porosity interpretation model reflecting hydrocarbon generation is established, including: obtaining the total organic carbon content and pyrolytic hydrocarbon content of deep shale; based on the total organic carbon content and pyrolytic hydrocarbon content, the organic porosity is analytically calculated through the mass and volume conservation relationship; and based on the organic porosity, the equivalent elastic modulus of organic matter and pore fluid is determined.

[0013] In one embodiment of this disclosure, the organic porosity is analytically calculated based on the total organic carbon content and the pyrolytic hydrocarbon content using the mass-volume conservation relationship. This includes: determining the amount of organic matter mass loss during the hydrocarbon generation process based on the total organic carbon content and the pyrolytic hydrocarbon content; calculating the volume expansion caused by the hydrocarbon generation process based on the amount of organic matter mass loss and the mass-volume conservation relationship, and determining the volume expansion as the organic porosity.

[0014] In one embodiment of this disclosure, constructing a deep shale seismic rock physics model includes: based on the anisotropic medium theory, coupling the equivalent elastic modulus of sheet clay with the equivalent elastic modulus of organic matter and pore fluid to construct a transversely isotropic background medium model with vertical symmetry; introducing vertical cracks into the transversely isotropic background medium model to construct an orthotropic medium model as a deep shale seismic rock physics model.

[0015] In one embodiment of this disclosure, constructing a transversely isotropic background medium model with vertical symmetry includes: using a self-consistent approximation model or a differential equivalent medium model, mixing the sheet-like clay component with the organic matter / pore fluid component, and calculating the equivalent elastic modulus of the transversely isotropic background medium.

[0016] In one embodiment of this disclosure, a seismic rock physics model of deep shale is used to predict key reservoir parameters of a target deep shale, including: acquiring seismic inversion elastic parameter data of the target area; inputting the seismic inversion elastic parameter data into the seismic rock physics model of deep shale to invert and obtain the porosity and total organic carbon content of the deep shale; and delineating favorable exploration and development areas of the deep shale based on the porosity and total organic carbon content.

[0017] According to another aspect of this disclosure, an electronic device is provided, comprising: a memory for storing instructions; and a processor for calling the instructions stored in the memory to implement the above-described seismic petrophysical prediction method for key parameters of deep shale reservoirs.

[0018] According to another aspect of this disclosure, a computer-readable storage medium is provided having computer instructions stored thereon, which, when executed by a processor, implement the above-described seismic petrophysical prediction method for key parameters of deep shale reservoirs.

[0019] According to another aspect of this disclosure, a computer program product is provided, which stores instructions that, when executed by a computer, cause the computer to implement the above-described seismic rock physics prediction method for key parameters of deep shale reservoirs.

[0020] The seismic petrophysical prediction method for key parameters of deep shale reservoirs provided in this disclosure effectively overcomes the interference of non-plate-like rigid particles on the directional arrangement of clay in deep shale by introducing a polycrystalline orientation distribution function of platy clay that includes a microstructure correction term, significantly improving the calculation accuracy of the equivalent elastic modulus of the clay skeleton. Simultaneously, it innovatively establishes an organic porosity interpretation model reflecting hydrocarbon generation, accurately quantifying organic porosity from a physical mechanism perspective based on hydrocarbon generation pyrolysis data, thus compensating for the elastic parameter estimation bias caused by traditional models treating organic matter as a pure solid component. This method deeply couples the above two aspects, constructing a petrophysical model that highly fits the complex geological characteristics of deep shale, thereby significantly improving the accuracy of inverting key reservoir parameters (such as porosity and total organic carbon content) using seismic data, providing more reliable theoretical and data support for the refined evaluation and efficient development of deep shale gas "sweet spots."

[0021] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0022] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0023] Obviously, the accompanying drawings described below are merely some embodiments of this disclosure. Those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0024] Figure 1 This diagram illustrates a seismic rock physics prediction method for key parameters of deep shale reservoirs according to an embodiment of the present disclosure.

[0025] Figure 2 This diagram illustrates the flowchart for constructing the polycrystalline orientation distribution function of sheet clay in an embodiment of this disclosure.

[0026] Figure 3 This diagram illustrates a rock physics modeling process for deep shale OA media considering compaction and hydrocarbon generation in an embodiment of this disclosure.

[0027] Figure 4 This diagram illustrates the change in rock volume caused by particle filling in an embodiment of the present disclosure.

[0028] Figure 5 This diagram illustrates the variation of the orientation degree of the flaky clay with the quartz particle content in embodiments of this disclosure.

[0029] Figure 6a and Figure 6b This diagram shows a comparison of the rock physics model prediction results for the elastic modulus parameter in the embodiments of this disclosure.

[0030] Figure 7a and Figure 7b This diagram shows a comparison between the P-wave and S-wave velocity model estimation and the actual well measurement results in the embodiments of this disclosure;

[0031] Figure 8a and Figure 8b This diagram illustrates the error distribution of predicted P-wave and S-wave velocities in an embodiment of this disclosure.

[0032] Figure 9 A structural block diagram of an electronic device according to an embodiment of the present disclosure is shown. Detailed Implementation

[0033] The exemplary implementation will now be described more fully with reference to the accompanying drawings.

[0034] It should be noted that the example implementation can be implemented in many forms and should not be construed as being limited to the examples set forth herein.

[0035] Seismic petrophysical modeling of anisotropic deep shale oil and gas is an important approach to revealing the intrinsic relationships between the material composition, microstructure, and elastic anisotropy of deep shale. However, the formation conditions of deep shale are complex, generally controlled by high-frequency cycles, and the lithological variations within the strata are complex, with poor continuity and strong heterogeneity. Due to long-term compaction and hydrocarbon generation, the microstructure and mechanical properties of the rocks can undergo significant transformations, mainly reflected in the geophysical properties of the reservoir, such as elasticity, pressure, and brittleness. The microscopic response mechanisms of these characteristics to the elasticity, anisotropy, and compressibility of shale remain unclear.

[0036] Currently, there is considerable research on shale rock physics modeling methods. These studies have made numerous attempts to consider complex minerals, fluids, and porosity. However, anisotropic seismic rock physics modeling for deep shale remains in the exploratory stage. Due to long-term compaction, the microstructure and mechanical properties of shale undergo significant changes, primarily in reservoir properties and elastic anisotropy. On the one hand, compaction reduces internal porosity, leading to denser crystal packing and increased rock density. On the other hand, compaction also induces the rearrangement of clay minerals and the expulsion of intergranular water, altering the mechanical properties of clay minerals and overlying strata. This significantly impacts seismic wave propagation characteristics, 4D seismic monitoring, and wellbore stability during drilling. The coupling effects of elastic responses among various minerals and the complex microstructure of the rock make it difficult to incorporate compaction and non-plate-like particle effects into shale modeling. Although some studies have explored the impact of compaction on changes in shale pore microstructure and the preferential orientation of clay minerals, limitations remain in predicting shale anisotropy characteristics. In recent years, Backus averaging, self-consistent approximation theory, and differential equivalent medium theory have been widely used to incorporate clay minerals into shale physical modeling. However, their application in assessing the influence of clay mineral orientation on equivalent elastic anisotropy remains relatively weak. Due to limitations in observational methods, a comprehensive description of the spatial distribution of platy clay requires the use of orientation distribution functions (ODFs), which can effectively quantify the influence of compaction on the orientation of platy clay minerals. There are many types of ODFs in rock physical modeling, among which compaction-based ODFs and aggregation-based ODFs are widely used. Using Voigt averaging and Reuss methods, the elastic properties of anisotropic clay plates can be further obtained using the orientation distribution function of individual clay plates. In actual sedimentary rocks, clay typically exists in polycrystalline form, and scanning electron microscopy of shale samples shows clay plates stacked and aggregated together, also known as clay domains. For stacked clay polycrystals, their orientation distribution can generally be described by polar density profiles, which can then be used to extract compaction ODF function parameters to establish a model describing the orientation distribution characteristics of clay polycrystals in rock samples. However, actual rocks have complex mineral compositions and often contain large quartz grains, which obviously affect the orientation of clay platy crystals. Therefore, the conventional ODF function, due to its lack of consideration for non-platy grains, limits its application in anisotropic rock physics modeling of shale, leading to errors in rock physics predictions.To date, due to the lack of theory regarding the influence of non-platy particles (such as quartz) on clay orientation, some scholars have attempted to consider this effect through data fitting. For example, they have explained this effect by defining the initial porosity as a function of clay content, where rocks with high non-platy particle content have lower compaction coefficients. However, these empirical models typically have limitations. They usually require high-quality and extensive observational data to establish reliable models, and a greater problem is that the lack of theoretical foundation makes these empirical models difficult to apply in different contexts. Therefore, it is of great significance to consider the complex mineral composition, clay orientation, and the influence of non-platy particles on the elastic anisotropy of clay domains in the anisotropic rock physics modeling of deep shale.

[0037] Furthermore, changes in organic carbon content during hydrocarbon generation significantly affect the elasticity and anisotropy of shale rocks. The main sources of organic carbon in shale are kerogen and hydrocarbon components, which can be represented by the pyrolysis parameters S2 and S1, respectively. Kerogen is a complex, three-dimensional amorphous organic polymer with multiple structures, often characterized by low velocity and low density; its distribution characteristics influence the elastic anisotropy of shale. As an amorphous organic matter in sedimentary rocks, kerogen often appears in actual rocks as patches, bands, and infills. In rock physics modeling, kerogen is typically considered in two ways: one is as the background matrix, and the other is as inclusions. In addition, the maturity of kerogen also affects the rock's rock physics characteristics. By considering kerogen characteristics at different maturity levels in shale rock physics modeling, the accuracy of the model's prediction of elastic anisotropy parameters can be effectively improved. Because the maturity of kerogen itself requires complex experimental methods to measure, and the elastic modulus of kerogen at different maturity levels exhibits significant uncertainty, it is difficult to directly consider the impact of maturity on shale rock in practical shale modeling. On the other hand, during the thermal evolution of kerogen, some kerogen is converted into hydrocarbons and stored in the organic and inorganic pores of shale. This distribution of mixed hydrocarbon components in the shale also significantly affects the rock's anisotropy. Therefore, hydrocarbon generation has a significant impact on both the microstructure and macroscopic composition of shale. Considering the influence of hydrocarbon generation on shale petrophysical modeling can effectively improve the reliability and accuracy of deep shale petrophysical models.

[0038] The following detailed description of this exemplary implementation method is provided in conjunction with the accompanying drawings and embodiments.

[0039] Figure 1 This invention discloses a flowchart of a seismic rock physics prediction method for key parameters of deep shale reservoirs, as shown in an embodiment of the present disclosure. Figure 1As shown, the seismic rock physics prediction method for key parameters of deep shale reservoirs includes steps S110-S150.

[0040] In S110, acquire mineral composition data, porosity data, and hydrocarbon generation pyrolysis data of the target deep shale.

[0041] This step primarily involves the collection and processing of basic data. Specifically, the volume percentages of mineral components such as quartz, feldspar, and clay minerals can be obtained by performing X-ray diffraction (XRD) experiments on core samples of the target deep shale; total porosity data can be obtained through helium porosity measurements or nuclear magnetic resonance (NMR) experiments; and hydrocarbon generation pyrolysis parameters such as total organic carbon (TOC), free hydrocarbon content (S1), and cracked hydrocarbon content (S2) can be obtained through rock pyrolysis experiments. These data will serve as the input basis for subsequent rock physics modeling.

[0042] In S120, based on mineral composition data and porosity data, a polycrystalline orientation distribution function for flaky clay with microstructure correction terms is established, and the equivalent elastic modulus of flaky clay is calculated based on the polycrystalline orientation distribution function.

[0043] In this step, the interference effect of non-plate-like particles (such as rigid particles like quartz and feldspar) on the clay depositional arrangement is first analyzed. A microstructure correction term (e.g., a correction coefficient based on the volume fraction of non-plate-like particles or initial porosity) is introduced to modify the traditional compaction orientation distribution function (ODF), constructing a polycrystalline orientation distribution function for platy clay that reflects the true microstructure of deep shale. Subsequently, the distribution function is integrally calculated using the Voigt-Reuss-Hill averaging method or adaptive self-consistent approximation theory to obtain the equivalent elastic modulus (including the stiffness matrix) of the platy clay skeleton after considering the interference effect of non-plate-like particles.

[0044] In S130, based on hydrocarbon generation pyrolysis data, an organic pore interpretation model reflecting hydrocarbon generation is established, and the equivalent elastic modulus of organic matter and pore fluid is obtained.

[0045] This step aims to quantitatively characterize the impact of organic matter evolution on rock physical properties. Using hydrocarbon generation pyrolysis data (especially TOC and S1), and combining the laws of mass conservation and volume conservation, the volumetric expansion caused by the conversion of solid organic matter into fluids during kerogen pyrolysis is calculated, thereby determining the organic porosity. Based on this organic porosity, an organic matter complex model incorporating kerogen, organic pores, and pore fluids is constructed. The bulk modulus of the pore fluid is calculated using fluid substitution equations (such as the Gassmann equation or the Wood formula), ultimately yielding the equivalent elastic modulus of the mixture of organic matter and pore fluids.

[0046] In S140, a seismic rock physics model of deep shale is constructed by combining the equivalent elastic modulus of flaky clay and the equivalent elastic modulus of organic matter and pore fluid.

[0047] In this step, the components obtained in the previous steps are assembled. Using the equivalent elastic modulus of the flaky clay calculated in step S120 as the background matrix, the organic matter complex obtained in step S130, and inorganic pores (such as cracks and rigid pores) are considered as inclusions or inclusions. Using effective medium theories (such as Differential Equivalent Medium Theory (DEM), Self-Consistent Approximation (SCA), or Anisotropic Differential Equivalent Medium Model), the components are coupled according to their volume proportions and geometric morphologies in the rock, ultimately establishing a complete seismic petrophysical model capable of characterizing the anisotropic features of deep shale.

[0048] In S150, the key reservoir parameters of the target deep shale are predicted using a seismic rock physics model for deep shale.

[0049] Finally, the constructed rock physics model is used for forward simulation or inversion applications. By inputting elastic parameters (such as P-wave velocity, S-wave velocity, density, anisotropy parameters, etc.) obtained from well logging data or seismic inversion into the model, or by matching the model's predicted values ​​with the measured values ​​through iterative optimization algorithms, key reservoir parameters of the target deep shale, such as total porosity, organic porosity, total organic carbon content (TOC), gas saturation, and brittleness index, are inverted and predicted, providing data support for reservoir evaluation.

[0050] This disclosure, through the construction of a clay orientation distribution function incorporating microstructure correction terms, effectively quantifies the interference of non-plate-like particles on the directional arrangement of clay, overcoming the limitations of traditional models in describing compaction. Simultaneously, by combining hydrocarbon generation pyrolysis data to establish an organic porosity evolution model, it achieves a refined characterization of the elastic properties of organic matter and fluids. This method significantly improves the prediction accuracy of elastic anisotropy and key reservoir parameters in deep shale, providing a reliable petrophysical basis for "sweet spot" identification and compressibility evaluation.

[0051] In some embodiments, establishing a polycrystalline orientation distribution function for lamellar clay that includes microstructure correction terms may include... Figure 2 S201-S204 are shown.

[0052] In S201, the volume percentage of non-plate-like particles and the initial porosity in deep shale are obtained;

[0053] In S202, the equivalent incompressible porosity correction term is determined based on the ratio of the volume percentage of non-plate particles to the initial porosity.

[0054] In S203, the equivalent incompressible porosity correction term is introduced into the original compaction parameters to determine the corrected compaction parameters;

[0055] In S204, a polycrystalline orientation distribution function for lamellar clay is constructed based on the modified compaction parameters.

[0056] In some embodiments, determining an equivalent incompressible porosity correction term and a corrected compaction parameter based on the ratio of the volume percentage of non-plate-like particles to the initial porosity includes: constructing a mapping relationship between the volume percentage of non-plate-like particles and the ratio of the initial porosity to determine the equivalent incompressible porosity correction term; and using the equivalent incompressible porosity correction term as a multiplier factor or an additive factor to perform calculations with the original compaction parameter to obtain the corrected compaction parameter.

[0057] This disclosure constructs an equivalent incompressible porosity correction term by introducing the ratio of the volume percentage of non-platy particles to the initial porosity, and uses this term to correct the original compaction parameters through multiplication and addition operations. This technique effectively quantifies the interference of rigid particles on the directional arrangement of clay minerals, overcoming the limitation of traditional models that only consider ideal compaction environments. The corrected compaction parameters better match the true microstructure of deep shale, significantly improving the accuracy of the polycrystalline orientation distribution function of platy clay, and thus improving the calculation accuracy of the rock's equivalent elastic modulus.

[0058] In some embodiments, an organic porosity interpretation model reflecting hydrocarbon generation is established, including: obtaining the total organic carbon content and pyrolytic hydrocarbon content of deep shale; based on the total organic carbon content and pyrolytic hydrocarbon content, the organic porosity is analytically calculated through the mass and volume conservation relationship; and based on the organic porosity, the equivalent elastic modulus of organic matter and pore fluid is determined.

[0059] In some embodiments, the organic porosity is analytically calculated based on the total organic carbon content and the pyrolysis hydrocarbon content using the mass-volume conservation relationship. Specifically, this includes: determining the amount of organic matter mass loss during the hydrocarbon generation process based on the total organic carbon content and the pyrolysis hydrocarbon content; calculating the volume expansion caused by the hydrocarbon generation process based on the amount of organic matter mass loss and the mass-volume conservation relationship, and determining the volume expansion as the organic porosity.

[0060] In some embodiments, constructing a deep shale seismic rock physics model specifically includes: based on the anisotropic medium theory, coupling the equivalent elastic modulus of sheet clay with the equivalent elastic modulus of organic matter and pore fluid to construct a transversely isotropic (VTI) background medium model with vertical symmetry; introducing vertical fractures into the transversely isotropic background medium model to construct an orthotropic (OA) medium model as a deep shale seismic rock physics model.

[0061] In some embodiments, constructing a transversely isotropic background medium model with vertical symmetry specifically includes: using a self-consistent approximation model or a differential equivalent medium model, mixing the sheet-like clay component with the organic matter / pore fluid component, and calculating the equivalent elastic modulus of the transversely isotropic background medium.

[0062] In some embodiments, a seismic rock physics model of deep shale is used to predict key reservoir parameters of the target deep shale, including: acquiring seismic inversion elastic parameter data of the target area; inputting the seismic inversion elastic parameter data into the seismic rock physics model of deep shale to invert and obtain the porosity and total organic carbon content of the deep shale; and delineating favorable exploration and development areas of the deep shale based on the porosity and total organic carbon content.

[0063] During the formation of deep shale, compaction and hydrocarbon generation primarily affect the microstructure of the rock, manifesting in the directional arrangement of platy clay and the formation of organic pores. These two factors are among the main causes of anisotropy in deep shale. Existing methods lack consideration for the influence of non-platy particles on platy clay minerals, resulting in low reliability and poor applicability of rock physics models. To reveal the main causes of clay mineral anisotropy, this disclosure first establishes a theoretical model considering the influence of compaction and non-platy particles on the preferred orientation of clay slabs, deriving the orientation distribution function (ODF) based on the effects of compaction and non-platy particles, making the established shale anisotropy rock physics model more reasonable and reliable. Secondly, to reveal the influence mechanism of hydrocarbon generation on the microscopic response of deep shale rock physics, an organic pore interpretation model influenced by hydrocarbon generation is derived, providing an effective solution for obtaining organic pore curves from wellbore data. Finally, combining compaction theory, hydrocarbon generation, and anisotropic rock physics, an anisotropic seismic rock physics model for deep shale oil and gas reservoirs was established, considering both compaction and hydrocarbon generation. This model effectively couples the effects of compaction and hydrocarbon generation, improving the accuracy of shale elastic anisotropic rock physics predictions. Theoretical analysis and verification with actual data show that, compared to conventional rock physics modeling methods, the deep shale seismic rock physics model established by this method is more reliable and accurate.

[0064] The following is for reference. Figure 3 This disclosure provides a detailed description of the embodiments. First, the equivalent elastic modulus of platy minerals in shale is obtained using the ODF orientation distribution function based on compaction and non-platy particle effects. Second, the equivalent elastic modulus of organic and inorganic phases is obtained using an organic porosity interpretation model based on the influence of hydrocarbon generation. Finally, a petrological model of deep shale OA medium considering compaction and hydrocarbon generation is constructed by coupling vertical fractures and fluids.

[0065] First, we introduce the ODF orientation distribution function of platy polycrystalline minerals under the influence of coupled compaction and non-flat particles, which is the orientation distribution function of platy clay polycrystalline minerals that includes microstructure correction terms mentioned earlier.

[0066] Platy clay polycrystalline material, as an important component of shale, is one of the main sources of shale's elastic anisotropy. Especially during shale formation, the directional alignment of platy clay polycrystalline material due to compaction is the primary reason for the layered anisotropy of shale. The distribution pattern of platy clay polycrystalline material in shale can be quantitatively characterized by the ODF (Oriented Distribution Function) based on the influence of compaction. The ODF based on formation compaction mainly reflects that, under formation compaction alone, the orientation of sedimentary minerals in the medium tends to become increasingly uniform, primarily used to characterize the orientation of clay minerals, and is represented by a normalized directional distribution function.

[0067] (1)

[0068] in, The maximum extreme density value of the directional alignment of rocks can be obtained by X-ray measurement of the directional distribution of single crystals in rocks, and its value is greater than 1; This represents the extreme orientation angle of the clay flakes, providing a good quantification of the distribution pattern of clay minerals under compaction. In this formula, the orientation of the clay flakes is determined solely by the degree of compaction. To control, from the initial porosity Porosity after compaction The ratio is used to characterize:

[0069] (2)

[0070] The conventional compaction ODF function described above can effectively describe the orientation of clay polycrystals, but its drawback is that it only considers the effect of compaction on the orientation of clay flakes, while ignoring the influence of the presence of other particles (such as quartz) on the orientation of clay flakes. Quartz particles are always present in shale, and the preferred orientation of clay minerals is limited by the presence of non-plate particles. Therefore, further considering the influence of particles on the ODF function based on compaction is the key issue to be addressed in this section. Formula (2), as the input parameter of the ODF function, is mainly achieved through the relative change in pore volume. Therefore, when considering the influence of non-plate particles on the ODF result of platy clay minerals, it is assumed that the volume percentage of non-plate particles is... Similar to the effect of relative changes in pore volume on the directional arrangement of clay. For example... Figure 4 As shown, when the volume of the rock containing no particulate minerals is assumed to be... The volume of the particles is The presence of particles will cause changes in the volume of the rock, thus affecting the oriented arrangement of clay platy crystals. Therefore, for clay platy crystals containing non-platy particles, we have:

[0071] (3)

[0072] In the formula, and These represent the volume percentage of non-flat particle minerals and the initial volume percentage in the clay, respectively.

[0073] The main significance of formula (3) is that it equates the influence of non-plate-like particles on compaction parameters to incompressible pores, which cannot be compressed by formation compaction. Because the initial porosity volume is large in shale rocks, and clay, as the main constituent mineral, accounts for a high proportion, the initial volume percentage of other particles is relatively small. Therefore, for shale with large porosity, it can be... Approximately equal to 0. When When the value is 0, formula (3) is equal to formula (2). From formula (3), it can be found that the particle content... and A negative correlation indicates that as particle content increases, compaction parameters decrease, thereby inhibiting the directional arrangement of platy clay minerals. A similar pattern is observed in some reports: as the content of non-flat quartz particles increases, the Mrds of platy clay minerals gradually decreases. Mrds represents the degree of particle orientation; a decrease in its value indicates a reduction in the spatial orientation of platy clay minerals, leading to a more randomized distribution. Figure 5 As shown. Therefore, the preferred orientation of clay-rich rocks is mainly influenced by compaction, but the presence of non-slab particles limits its orientation, consistent with previous analyses. To further simplify the expression of formula (3), let , which represents the ratio of particle content to porosity, mainly reflects the relative degree of particle content, and can be further expressed as the compaction parameter in formula (3) containing the influence of non-plate particles as:

[0074] (4)

[0075] Substituting formula (4) into formula (1), we can obtain the ODF function expression that considers compaction and particle effects:

[0076] (5)

[0077] Next, we will introduce an organic pore explanation model based on the influence of hydrocarbon generation.

[0078] The pore types in organic-rich shale mainly include inorganic pores and organic pores. Considering the differences in microstructure and distribution between these two types is of great significance in the petrophysical modeling of deep shale. Inorganic pores are usually formed by the interaction between inorganic minerals, while organic pores exist in organic matter (kerogen, bitumen, etc.). Since the proportion of organic pores is difficult to obtain through direct well logging methods in the petrophysical modeling of deep shale, more sophisticated experimental methods are usually required. Obtaining well logging curves for organic pores is crucial for predicting the elastic anisotropy of rock physics. Generally, the strength of hydrocarbon generation is closely related to the formation of organic pores. Therefore, this invention establishes an organic pore interpretation model based on the influence of hydrocarbon generation, according to pyrolysis theory, and analyzes the relationship between the shale oil and gas sweet spot parameter (TOC), the pyrolysis parameter (S1), and the rock property parameter (organic porosity).

[0079] If the volume of organic matter contained per unit volume of rock before hydrocarbon generation is The volume consumed in hydrocarbon generation is Assume that the organic carbon in the organic matter is relatively uniformly distributed and that its distribution density is constant. Then the following formula always holds true:

[0080] (6)

[0081] In the formula, , S1 is the pyrolysis parameter, mg / g; TOC is the total organic carbon content per unit volume of rock, g / g. If the rock unit volume is... Formula (6) can be written in the following form:

[0082] (7)

[0083] Since the volume of organic matter consumed by hydrocarbon generation can be approximately equal to the volume of organic matter pores, the porosity of organic matter can be expressed as:

[0084] (8)

[0085] The volume percentage of remaining organic matter can be expressed as:

[0086] (9)

[0087] Substituting formulas (7) and (9) into (8), we can obtain the expression for organic pores as follows:

[0088] (10)

[0089] Finally, we introduce anisotropic seismic rock physics modeling of deep shale based on compaction and hydrocarbon generation. That is, as mentioned above, we combine the equivalent elastic modulus of platy clay and the equivalent elastic modulus of organic matter and pore fluid to construct a seismic rock physics model of deep shale.

[0090] (1) Determine the elastic modulus of platy clay minerals based on the effect of compaction. .

[0091] The equivalent elasticity theory of polycrystalline platy clay based on directional distribution function (ODF) is used to perform equivalent averaging of platy minerals in different directions. The directional arrangement characteristics of platy clay are estimated by the ODF of formula (5) proposed in this invention.

[0092] (2) Estimation of equivalent elastic modulus of inorganic and organic phases.

[0093] The anisotropic SCA model can be used to effectively estimate the elastic moduli of inorganic and organic components. Specifically, based on step (1), after considering the directional arrangement of the flaky clay, other inorganic minerals are added to obtain the equivalent elastic modulus. :

[0094] (11)

[0095] In the formula, and They represent the first Stiffness parameters and volume fraction of each component; The fourth-order tensor is represented.

[0096] The organic components mainly consist of kerogen and organic porosity, with the organic porosity estimated using a hydrocarbon generation model. Considering the relatively small pore volume, the elastic modulus of the organic components can also be estimated using an anisotropic SCA model. In this step, the influence of pyrolysis parameters S1 and TOC can be effectively incorporated into the shale petrophysical model through the organic porosity model.

[0097] (3) Estimation of the background elastic modulus of shale VTI.

[0098] Under the influence of compaction, clay minerals exhibit directional alignment and kerogen and pore distribution characteristics along the layers, allowing the mineral background to be assumed to have VTI characteristics. Therefore, based on the known elastic moduli of inorganic and organic phases, the VTI background of rocks without vertical cracks can be estimated using an anisotropic DEM model, including its stiffness matrix. It can be represented as:

[0099] (12)

[0100] (4) The influence of vertical cracks is added.

[0101] When a set of vertical fractures coexist in shale, the shale can be assumed to be an OA medium. Under the assumption of weak anisotropy, its stiffness matrix can be estimated using the Schenberg linear slip model and the Hudson model:

[0102] (13)

[0103] In the formula, This represents the perturbation of the flexibility matrix caused by the vertical crack, and the stiffness parameter of the OA medium can be expressed as the following formula (14):

[0104]

[0105]

[0106]

[0107]

[0108]

[0109]

[0110]

[0111]

[0112]

[0113] (5) Anisotropic fluid replacement

[0114] Fluid substitution was performed using the Brown-Korringa fluid substitution formula to obtain the saturated rock weakness matrix:

[0115] (15)

[0116] in, and These represent the flexibility tensor of rock under saturated fluid conditions and the elastic flexibility of the dry rock skeleton under unsaturated fluid conditions, respectively. This corresponds to the elastic flexibility tensor of the background rock; and These represent the compressibility of fluids and minerals in rocks, respectively.

[0117] This led to the establishment of a rock physics model for fractured shale based on the OA medium theory, and further coupled the effects of compaction and hydrocarbon generation on the elastic anisotropy of the rock, thus laying the foundation for reliably obtaining seismic rock physics parameters.

[0118] Figure 6a and Figure 6b A comparison of the rock physics model prediction results for the elastic modulus parameter is shown. The solid line represents the predicted stiffness parameter considering compaction, while the dashed line represents the predicted stiffness parameter without considering compaction. In sample A2080, the dashed line represents the predicted equivalent stiffness parameter without considering non-plate particles, and the solid line represents the predicted equivalent stiffness parameter considering the influence of non-plate particles. It can be observed that for both A0100 and A2080, porosity decreases and stiffness parameters increase with increasing compaction. The modulus parameter estimated considering compaction shows better agreement with experimental data than the predicted result of the dashed line without considering compaction, further illustrating the important role of the ODF function coupled with compaction and non-plate particles proposed in this paper in rock physics model prediction. This function can be used reliably and effectively to predict rock physics characteristics such as elastic anisotropy of shale.

[0119] Figure 7a and Figure 7b The figure shows a comparison between the P-wave and S-wave velocity model estimation and the measured results from the well. As can be seen from the figure, the measured P-wave and S-wave curves are basically consistent with the model predictions, and the overall trend of the predicted results is the same as that of the measured results, showing a high degree of agreement. Therefore, this demonstrates that the rock physical model proposed in this invention can effectively predict the elastic parameters of deep shale.

[0120] Figure 8a and Figure 8b To predict the error distribution of P-wave and S-wave velocities, the P-wave error distribution is mostly within 0.1, while the S-wave error distribution is mainly within 0.2. This indicates that the established rock physics model can reliably estimate the elastic parameters at the well site.

[0121] Furthermore, although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result.

[0122] In some embodiments, certain steps may be omitted, multiple steps may be combined into one step for execution, and / or one step may be broken down into multiple steps for execution.

[0123] Based on the same inventive concept, this disclosure also provides an electronic device. Figure 9 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Figure 9As shown, an embodiment of the present invention provides an electronic device including: one or more processors 901, a memory 902, and one or more I / O interfaces 903. The memory 902 stores one or more programs, which, when executed by the one or more processors, enable the one or more processors to implement any of the seismic rock physics prediction methods for key parameters of deep shale reservoirs as described in the above embodiments; the one or more I / O interfaces 903 are connected between the processor and the memory, configured to enable information interaction between the processor and the memory.

[0124] Among them, processor 901 is a device with data processing capabilities, including but not limited to central processing unit (CPU); memory 902 is a device with data storage capabilities, including but not limited to random access memory (RAM, more specifically SDRAM, DDR, etc.), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory (FLASH); I / O interface (read-write interface) 903 is connected between processor 901 and memory 902, and can realize information interaction between processor 901 and memory 902, including but not limited to data bus (Bus).

[0125] In some embodiments, the processor 901, memory 902, and I / O interface 903 are interconnected via bus 904, and thus connected to other components of the computing device.

[0126] In some embodiments, the one or more processors 901 include a field-programmable gate array.

[0127] This invention also provides a computer-readable medium. The computer-readable medium stores a computer program, which, when executed by a processor, implements the steps in any of the seismic petrophysical prediction methods for key parameters of deep shale reservoirs described in the above embodiments. The computer-readable storage medium can be volatile or non-volatile.

[0128] This invention also provides a computer program product, including computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code. When the computer-readable code is run in the processor of an electronic device, the processor in the electronic device executes the above-mentioned seismic rock physics prediction method for key parameters of deep shale reservoirs.

[0129] Those skilled in the art will understand that all or part of the steps of the above embodiments can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, which can be collectively referred to as "circuit", "module" or "system".

[0130] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein.

[0131] This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the appended claims.

Claims

1. A seismic rock physics prediction method for key parameters of deep shale reservoirs, characterized in that, include: Obtain mineral composition data, porosity data, and hydrocarbon generation pyrolysis data of the target deep shale; Based on the mineral composition data and the porosity data, a polycrystalline orientation distribution function for flaky clay with microstructure correction terms is established, and the equivalent elastic modulus of flaky clay is calculated based on the polycrystalline orientation distribution function. Based on the hydrocarbon generation pyrolysis data, an organic pore interpretation model reflecting hydrocarbon generation was established, and the equivalent elastic modulus of organic matter and pore fluid was obtained. By combining the equivalent elastic modulus of the flaky clay and the equivalent elastic modulus of the organic matter and pore fluid, a deep shale seismic rock physical model is constructed. Using the aforementioned seismic rock physics model for deep shale, key reservoir parameters of the target deep shale are predicted.

2. The method according to claim 1, characterized in that, The establishment of the polycrystalline orientation distribution function of plate-like clay, which includes a microstructure correction term, includes: Obtain the volume percentage of non-plate-like particles and the initial porosity in deep shale; Based on the ratio of the volume percentage of the non-plate-like particles to the initial porosity, an equivalent incompressible porosity correction term is determined. The equivalent incompressible pore correction term is introduced into the original compaction parameters to determine the corrected compaction parameters. Based on the corrected compaction parameters, the polycrystalline orientation distribution function of the flaky clay is constructed.

3. The method according to claim 2, characterized in that, The determination of the equivalent incompressible porosity correction term and the corrected compaction parameters based on the ratio of the volume percentage of the non-plate-like particles to the initial porosity includes: A mapping relationship is established between the volume percentage of the non-plate-like particles and the ratio of the initial porosity to determine the equivalent incompressible porosity correction term; The equivalent incompressible pore correction term is used as a multiplier or summing factor and calculated with the original compaction parameters to obtain the corrected compaction parameters.

4. The method according to claim 1, characterized in that, The establishment of an organic pore explanation model reflecting hydrocarbon generation includes: To obtain the total organic carbon content and pyrolytic hydrocarbon content of deep shale; Based on the total organic carbon content and the pyrolytic hydrocarbon content, the organic porosity is calculated analytically using the mass and volume conservation relationship. Based on the organic porosity, the equivalent elastic modulus of the organic matter and the pore fluid is determined.

5. The method according to claim 1, characterized in that, The organic porosity is analytically calculated based on the total organic carbon content and the pyrolytic hydrocarbon content using the mass and volume conservation relationship, including: Based on the total organic carbon content and the pyrolysis hydrocarbon content, the amount of organic matter mass loss during the hydrocarbon generation and conversion process is determined. Based on the amount of organic matter mass loss and in conjunction with the mass-volume conservation relationship, the volume expansion caused by hydrocarbon generation conversion is calculated, and the volume expansion is determined as the organic porosity.

6. The method according to claim 1, characterized in that, The construction of the deep shale seismic rock physics model includes: Based on the theory of anisotropic media, the equivalent elastic modulus of the sheet clay and the equivalent elastic modulus of the organic matter and pore fluid are coupled to construct a transversely isotropic background medium model with vertical symmetry. Vertical cracks are introduced into the transversely isotropic background medium model to construct an orthotropic medium model as the seismic petrophysical model of the deep shale.

7. The method according to claim 6, characterized in that, The construction of a transversely isotropic background medium model with vertical symmetry includes: By using a self-consistent approximation model or a differential equivalent medium model, the flaky clay component is mixed with the organic matter / pore fluid component, and the equivalent elastic modulus of the transversely isotropic background medium is calculated.

8. The method according to claim 1, characterized in that, The method of using the deep shale seismic rock physics model to predict key reservoir parameters of the target deep shale includes: Obtain seismic inversion elastic parameter data for the target area; The seismic inversion elastic parameter data is input into the seismic rock physics model of the deep shale to obtain the porosity and total organic carbon content of the deep shale. Based on the porosity and total organic carbon content, favorable exploration and development areas for deep shale are delineated.

9. An electronic device, characterized in that, include: Memory, used to store instructions; A processor is configured to invoke instructions stored in the memory to implement the seismic petrophysical prediction method for key parameters of deep shale reservoirs as described in any one of claims 1-8.

10. A computer-readable storage medium storing computer instructions thereon, characterized in that, When the computer instructions are executed by the processor, they implement the seismic rock physics prediction method for key parameters of deep shale reservoirs as described in any one of claims 1-8.

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