Seismic simultaneous prediction method and related equipment for shale reservoir fluid and brittleness

Abstract

The present disclosure provides a method and related equipment for synchronously predicting shale reservoir fluid and brittleness, and relates to the technical field of oil and gas exploration geophysics. The method comprises: constructing a target reflectivity equation under a vertical transverse isotropic medium, the target reflectivity equation being represented by a compaction decoupling fluid factor and a brittleness indicator; the compaction decoupling fluid factor is a parameter obtained after solid-liquid decoupling of a compaction coefficient representing the compaction of the stratum, and the brittleness indicator is a parameter constructed directly based on the Lame parameter; based on the dispersion and attenuation characteristics of seismic waves propagating in the shale reservoir, the target reflectivity equation is converted into a frequency-dependent reflectivity equation; based on the frequency-dependent reflectivity equation and prestack seismic data, an inversion target function is constructed; the inversion target function is optimized and solved to obtain the compaction decoupling fluid factor and the brittleness indicator of the shale reservoir. The present disclosure can improve the prediction accuracy and reliability of the shale reservoir fluid and brittleness.

Description

Technical Field

[0001] This disclosure relates to the field of geophysical technology for oil and gas exploration, and in particular to a method and related equipment for simultaneous seismic prediction of fluid and brittleness in shale reservoirs. Background Technology

[0002] Shale oil and gas, as an important unconventional oil and gas resource, occupies an increasingly important position in the global energy structure. In shale oil and gas exploration and development, accurate prediction of reservoir fluid distribution (geological sweet spot) and rock brittleness (engineering sweet spot) is crucial for horizontal well trajectory design, fracturing operations, and production capacity assessment.

[0003] Currently, conventional methods for predicting fluid and brittleness in shale reservoirs typically suffer from the following limitations: First, existing technologies usually treat fluid identification and brittleness assessment as independent processes. Since fluid and brittleness are inherently coupled in their geophysical responses, separate predictions are not only computationally inefficient but also susceptible to parameter ambiguity, failing to meet the demands for efficient shale oil and gas evaluation. Second, shale reservoirs are typically vertically and laterally isotropic (VTI) media, exhibiting significant viscoelastic characteristics (such as dispersion and attenuation) when seismic waves propagate within them. Existing reflection coefficient equations are mostly based on conventional pure elastic theory, failing to adequately characterize the complex viscoelastic characteristics of shale reservoirs and the compaction effect of the deep rock skeleton, thus limiting their applicability and prediction accuracy in complex shale reservoirs.

[0004] 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

[0005] This application discloses a method and related equipment for simultaneous seismic prediction of shale reservoir fluids and brittleness, which can improve the accuracy and reliability of shale reservoir fluid and brittleness prediction.

[0006] 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.

[0007] According to one aspect of this disclosure, a method for simultaneous seismic prediction of fluids and brittleness in shale reservoirs is provided, comprising: acquiring pre-stack seismic data of the study area; constructing a target reflection coefficient equation under vertically and laterally isotropic media, wherein the target reflection coefficient equation is directly characterized by a compaction-decoupled fluid factor and a brittleness indicator factor; wherein the compaction-decoupled fluid factor is a parameter obtained by introducing a compaction coefficient characterizing formation compaction and performing solid-liquid decoupling, and the brittleness indicator factor is a parameter directly constructed based on Lamé parameters; transforming the target reflection coefficient equation into a frequency-varying reflection coefficient equation based on the dispersion and attenuation characteristics of seismic waves propagating in the shale reservoir; constructing an inversion objective function based on the frequency-varying reflection coefficient equation and pre-stack seismic data; and optimizing and solving the inversion objective function to simultaneously obtain the compaction-decoupled fluid factor and the brittleness indicator factor of the shale reservoir.

[0008] In one embodiment of this disclosure, the process of constructing the compaction decoupled fluid factor includes: obtaining an initial fluid factor; based on a rock physics decoupling model that considers formation compaction, introducing a compaction coefficient to perform solid-liquid decoupling processing on the initial fluid factor to obtain an initial compaction decoupled fluid factor; and converting the initial compaction decoupled fluid factor into a compaction decoupled fluid factor represented by Lamé parameters.

[0009] In one embodiment of this disclosure, the process of constructing a brittleness indicator factor includes: obtaining Lamé parameters of saturated rock and Lamé parameters of dry rock; and constructing a brittleness indicator factor based on the Lamé parameters of saturated rock and dry rock.

[0010] In one embodiment of this disclosure, constructing a target reflection coefficient equation for a vertically and laterally isotropic medium includes: obtaining a conventional reflection coefficient equation expressed in terms of Lamé parameters, the ratio of P-wave to S-wave velocity in saturated rock, and anisotropic parameters of the vertically and laterally isotropic medium; and substituting the compaction decoupling fluid factor and the brittleness indicator factor into the conventional reflection coefficient equation for variable substitution to obtain a target reflection coefficient equation containing coefficient terms of the compaction decoupling fluid factor and the brittleness indicator factor.

[0011] In one embodiment of this disclosure, converting the target reflection coefficient equation into a frequency-varying reflection coefficient equation includes: expressing the elastic parameters in the target reflection coefficient equation as complex numbers that vary with frequency; performing a Taylor expansion on the target reflection coefficient equation expressed in complex form at a preset reference frequency to extract the frequency variation term; and subtracting the reflection coefficient equation at the reference frequency from the expanded equation to obtain the frequency-varying reflection coefficient equation.

[0012] In one embodiment of this disclosure, an inversion objective function is constructed based on the frequency-varying reflection coefficient equation and pre-stack seismic data, including: constructing a likelihood function based on the Bayesian inversion framework and combining the frequency-varying reflection coefficient equation and pre-stack seismic data; constructing a priori model constraint term that includes the compaction decoupling fluid factor, the brittle indicator factor, and the anisotropic parameters of the vertically and laterally isotropic medium; and constructing the inversion objective function by combining the likelihood function and the priori model constraint term.

[0013] In one embodiment of this disclosure, obtaining pre-stack seismic data of the study area includes: obtaining partial angle-stacked seismic profiles of different center incident angles in the study area; performing frequency division processing on the partial angle-stacked seismic profiles based on wavelet transform to obtain frequency-divided seismic profiles of multiple main frequencies under different center incident angles; and using the frequency-divided seismic profiles as pre-stack seismic data.

[0014] In one embodiment of this disclosure, after simultaneously obtaining the compaction decoupled fluid factor and brittleness indicator factor of the shale reservoir, the method further includes: simultaneously obtaining the anisotropic parameters of the vertically and laterally isotropic medium; and performing an integrated matching evaluation of geological sweet spots and engineering sweet spots based on the compaction decoupled fluid factor, brittleness indicator factor, and anisotropic parameters of the vertically and laterally isotropic medium.

[0015] 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 method for synchronous seismic prediction of shale reservoir fluids and brittleness.

[0016] 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 method for simultaneous seismic prediction of shale reservoir fluids and brittleness.

[0017] The seismic synchronous prediction method for shale reservoir fluids and brittleness provided in this disclosure constructs a target reflection coefficient equation directly characterized by compaction-decoupled fluid factors and brittleness indicator factors in a vertically and laterally isotropic medium. It then introduces seismic wave dispersion and attenuation characteristics to transform this equation into a frequency-varying reflection coefficient equation, fully aligning with the anisotropic and viscoelastic physical nature of shale reservoirs and significantly improving the applicability and prediction resolution of the inversion equation. By introducing a compaction coefficient to decouple the fluid factors from solids and fluids and directly constructing a brittleness indicator factor based on Lamé parameters, it effectively eliminates the interference of deep formation compaction effects on fluid response and avoids the cumulative errors caused by traditional multi-level parameter conversion, greatly improving the accuracy and reliability of prediction parameters. Based on the frequency-varying reflection coefficient equation and pre-stack seismic data, an inversion objective function is constructed and optimized, achieving integrated synchronous prediction of fluid and brittleness parameters. This overcomes the shortcomings of traditional independent prediction, which is prone to multiple solutions, and effectively meets the practical needs of efficient integrated "geological-engineering" evaluation of shale oil and gas.

[0018] 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

[0019] 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.

[0020] 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.

[0021] Figure 1 This diagram illustrates a method for simultaneous seismic prediction of fluid and brittleness in shale reservoirs according to an embodiment of the present disclosure.

[0022] Figure 2 This paper shows a comparison of the AVO of the new reflection coefficient equation constructed by the present invention and the conventional reflection coefficient equation.

[0023] Figures 3a to 3c Partial angle superposition profiles with central incident angles of 10°, 20° and 30° are shown respectively;

[0024] Figures 4a to 4d The 5Hz, 15Hz, 25Hz and 35Hz frequency-division seismic profiles obtained based on small-angle seismic data are shown respectively.

[0025] Figures 5a to 5d The 5Hz, 15Hz, 25Hz and 35Hz frequency-division seismic profiles obtained based on mid-angle seismic data are shown respectively.

[0026] Figures 6a to 6d The 5Hz, 15Hz, 25Hz and 35Hz frequency-division seismic profiles obtained based on large-angle seismic data are shown respectively.

[0027] Figures 7a to 7d The inversion results of the brittleness factor, fluid factor, and anisotropy parameter of the embodiments of this disclosure are shown respectively;

[0028] Figure 8 A structural block diagram of an electronic device according to an embodiment of the present disclosure is shown. Detailed Implementation

[0029] The exemplary implementation will now be described more fully with reference to the accompanying drawings.

[0030] 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.

[0031] Shale reservoirs are important targets for exploration and development of unconventional oil and gas resources. These reservoirs are generally characterized by low porosity, low permeability, and strong heterogeneity. Their high density makes it difficult to form natural, efficient oil and gas flow channels, resulting in significant exploration and development challenges. Influenced by well-developed sedimentary bedding, directional arrangement of clay minerals, diagenetic compaction, and tectonic stress, shale reservoirs exhibit significant vertical-to-lateral isotropic (VTI) medium characteristics. Seismic waves propagating in VTI shale media exhibit anisotropic dispersion, velocity attenuation, and propagation polarization effects, differing from the seismic response mechanisms of conventional isotropic reservoirs. Efficient shale oil and gas recovery relies on hydraulic fracturing. The fluid characteristics of the reservoir determine the scale of oil and gas enrichment, while the brittleness of the reservoir controls the effectiveness of fracturing. Therefore, fluid factors and brittleness factors are two key parameters for evaluating the geological and engineering sweet spots of VTI shale reservoirs and guiding oil and gas exploration and development.

[0032] For unconventional reservoirs, existing technologies have developed various methods for predicting fluid factors and evaluating brittleness indices, enabling qualitative or semi-quantitative evaluation of reservoir fluid properties and brittleness characteristics. Among these, pre-stack seismic amplitude variation with offset (AVO) inversion is a commonly used technique for predicting reservoir elastic parameters, fluid parameters, and brittleness parameters. Existing shale fluid prediction methods are mainly divided into two categories: one is based on multi-attribute fusion using pre-stack inversion results to delineate the fluid distribution range; the other relies on rock physics mechanisms to construct fluid-sensitive factors, achieving quantitative fluid prediction through pre-stack inversion solutions. Commonly used factors include Russell fluid factor, solid-liquid decoupled fluid factor, and viscoelastic jet fluid factor. Shale brittleness evaluation mainly relies on the elastic parameter method, indirectly calculating reservoir brittleness by inverting elastic parameters such as Young's modulus and Poisson's ratio, combined with the brittleness index formula, to complete the fracturability evaluation. However, existing shale reservoir seismic inversion evaluation technology systems have many unavoidable technical defects and cannot meet the accurate evaluation needs of VTI-mediated shale reservoirs. First, existing technologies generally employ a step-by-step, independent inversion model, separating reservoir fluid prediction from brittleness assessment. Fluid factors are inverted separately to identify hydrocarbon-rich areas, and then the brittleness index is calculated independently to assess fracturing capability. While many technologies can predict these two types of parameters separately, techniques for simultaneous and coordinated inversion of fluid and brittleness factors in VTI shale reservoirs are extremely scarce. The step-by-step inversion process is cumbersome, inefficient, and suffers from poor parameter matching, making it difficult to achieve integrated and accurate matching and evaluation of geological and engineering sweet spots. Furthermore, existing shale brittleness index calculation methods generally suffer from large cumulative errors due to multi-level inversions. Currently, mainstream brittleness assessments are all based on indirect calculation models, requiring the acquisition of basic elastic parameters such as Young's modulus, Poisson's ratio, and Lamé parameters through pre-stack inversion, followed by iterative calculation of the brittleness index using empirical formulas. Multi-level parameter conversions and step-by-step iterative calculations continuously amplify inversion noise and systematic errors, significantly reducing the accuracy of brittleness index prediction and failing to meet the high-precision requirements for refined fracturing assessment of VTI shale reservoirs. More importantly, most existing inversion models are constructed based on the assumption of isotropic media, ignoring the inherent VTI anisotropic characteristics of shale reservoirs. This makes it impossible to accurately reproduce the propagation law of seismic waves in shale media, resulting in a large deviation between the inversion results of fluid factors and brittle factors and the actual geological conditions.

[0033] In summary, existing technologies struggle to simultaneously address the dual requirements of identifying hydrocarbon enrichment in VTI shale reservoirs and accurately evaluating their fracturing capabilities, thus hindering the precise prediction and efficient development of sweet spots in shale oil and gas reservoirs. To address the technical problems of existing shale reservoir seismic evaluation methods, such as neglecting reservoir anisotropy characteristics, step-by-step inversion of fluid and brittle parameters, and large cumulative errors from indirect calculations, this disclosure provides a method for simultaneous seismic prediction of fluid and brittleness in shale reservoirs. This disclosure relies on pre-stack seismic inversion theory, coupling fluid sensitivity factors with brittleness evaluation parameters, aiming to achieve integrated and accurate prediction of both geological sweet spots and fracturing stimulation engineering sweet spots in VTI shale reservoirs.

[0034] The following detailed description of this exemplary implementation method is provided in conjunction with the accompanying drawings and embodiments.

[0035] Figure 1 This invention discloses a flowchart of a method for simultaneous seismic prediction of fluid and brittleness in shale reservoirs, as shown in an embodiment of the present disclosure. Figure 1 As shown, the method for simultaneous seismic prediction of shale reservoir fluids and brittleness includes steps S101-S105.

[0036] In S101, pre-stack seismic data of the study area are acquired.

[0037] Prestack seismic data is seismic gather data containing information on different offsets or incident angles. It contains rich information on the elastic parameters and fluids of subsurface rocks and is the basic data source for subsequent prestack seismic inversion.

[0038] In S102, a target reflection coefficient equation is constructed for a vertically transverse isotropic (VTI) medium. The target reflection coefficient equation is directly characterized by the compaction decoupled fluid factor and the brittleness indicator factor.

[0039] Among them, VTI medium is used to characterize the anisotropic physical characteristics of shale reservoirs with vertical symmetry axes due to the development of sedimentary bedding and the directional arrangement of clay minerals; the target reflection coefficient equation is a mathematical model describing the variation of the reflection amplitude of seismic waves at the VTI medium interface with the incident angle and frequency; the compaction decoupling fluid factor is a parameter obtained by introducing the compaction coefficient, which characterizes the formation compaction, and performing solid-liquid decoupling. Its physical significance is to eliminate the interference of deep formation compaction effect on fluid response and highlight the true fluid characteristics; the brittleness indicator factor is a parameter directly constructed based on Lamé parameters, used to directly characterize the rock's ability to resist fracture, avoiding the errors caused by indirect conversion through Young's modulus and Poisson's ratio in traditional methods.

[0040] In S103, based on the dispersion and attenuation characteristics of seismic waves propagating in shale reservoirs, the target reflection coefficient equation is transformed into a frequency-varying reflection coefficient equation.

[0041] Dispersion and attenuation characteristics refer to the velocity variation (dispersion) and energy loss (attenuation) phenomena that occur when seismic waves propagate in shale viscoelastic media and pore fluids. The frequency-varying reflection coefficient equation introduces a frequency variable, enabling the reflection coefficient model to characterize the differences in reflection response of seismic waves at different frequencies, thereby more accurately depicting the viscoelastic physical nature of shale reservoirs.

[0042] In S104, an inversion objective function is constructed based on the frequency-varying reflection coefficient equation and pre-stack seismic data.

[0043] The inversion objective function is a mathematical function that characterizes the data residual between the theoretical seismic data obtained by forward modeling from the frequency-varying reflection coefficient equation and the actual pre-stack seismic data. The update of the model parameters is constrained by minimizing this data residual.

[0044] In S105, the inversion objective function is optimized and solved, and the compaction decoupling fluid factor and brittleness indicator factor of the shale reservoir are obtained simultaneously.

[0045] Optimization solution refers to using an optimization algorithm to iteratively find the model parameters that minimize the inversion objective function; simultaneous acquisition refers to outputting fluid and brittle parameters simultaneously under a unified inversion framework and the same objective function constraint, ensuring a high degree of matching between the two in spatial distribution and physical logic.

[0046] This embodiment of the invention constructs a frequency-varying reflection coefficient equation under VTI medium, which fully aligns with the inherent anisotropy and viscoelastic physical nature of shale reservoirs, improving the inversion model's ability to characterize actual geological conditions. It directly uses the compaction-decoupled fluid factor and brittleness indicator factor as characterization parameters of the target reflection coefficient equation, and simultaneously optimizes and solves them within a unified inversion objective function. This breaks down the barriers of traditional step-by-step independent inversion, achieving integrated and collaborative prediction of geological sweet spots (fluid) and engineering sweet spots (brittleness), ensuring the physical matching between parameters. The brittleness indicator factor is directly constructed based on Lamé parameters, and the fluid factor incorporates the compaction coefficient for decoupling, effectively eliminating the cumulative error of multi-level indirect conversion and the interference of deep compaction effects, significantly improving the prediction accuracy and reliability of shale reservoir fluid identification and brittleness evaluation.

[0047] In some embodiments, the construction process of the compaction decoupled fluid factor includes: obtaining an initial fluid factor; this initial fluid factor is an initial parameter containing fluid information derived from a conventional rock physics model, specifically such as the Russell fluid factor, which is simultaneously affected by the rock skeleton and the properties of the stored fluid, and is prone to fluid identification artifacts; based on a rock physics decoupling model that considers formation compaction, a compaction coefficient is introduced to perform solid-liquid decoupling processing on the initial fluid factor to obtain the initial compaction decoupled fluid factor; wherein, formation compaction refers to the physical process in which the pressure of the overlying strata leads to a decrease in rock porosity and an increase in skeleton stiffness as the burial depth increases. This rock physics decoupling model, based on conventional solid-liquid decoupling (such as based on the Nur critical porosity model), further introduces consideration of the rock skeleton changes caused by formation compaction. The system employs a correction mechanism; the introduced compaction coefficient is used to quantitatively characterize the degree of formation compaction. Specifically, correction parameters are constructed in conjunction with theories such as the Lee model, aiming to mathematically separate the solid elastic response of the rock skeleton from the liquid elastic response of the pore fluid, thereby eliminating the masking and interference of deep strong compaction effects on the fluid response and overcoming the defect that traditional fluid factors cannot adapt to changes in formation compaction. The initial compaction decoupled fluid factor is converted into a compaction decoupled fluid factor expressed by Lamé parameters. This conversion aims to uniformly represent the fluid factor as a function of the first Lamé parameter and the second Lamé parameter, where the first Lamé parameter specifically refers to the Lamé constant and the second Lamé parameter specifically refers to the shear modulus, so as to directly match the parameter form in the subsequently constructed VTI medium frequency-varying reflection coefficient equation, facilitating the direct solution of pre-stack seismic inversion.

[0048] In this embodiment, a compaction coefficient is introduced into the fluid factor construction to decouple the solid and liquid components. This effectively removes the interference of the rock skeleton on fluid identification and overcomes the shortcomings of traditional fluid factors, such as the tendency to produce identification artifacts and the inability to adapt to changes in skeleton parameters under formation compaction. This allows the decoupled fluid factor to reflect the true underground fluid characteristics more purely and sensitively, significantly improving the accuracy of fluid identification in VTI shale reservoirs. At the same time, it is converted into a Lamé parameter representation, achieving seamless integration with the parameter form of the inversion equation.

[0049] In some embodiments, the construction process of the brittleness indicator factor includes: obtaining the Lamé parameters of saturated rocks and the Lamé parameters of dry rocks; wherein, the Lamé parameters of saturated rocks characterize the in-situ elastic mechanical characteristics of rocks in a fluid-containing state, and the Lamé parameters of dry rocks characterize the elastic mechanical characteristics of the pure rock skeleton after the displacement of fluid, both including the Lamé constant and shear modulus of the corresponding state; based on the Lamé parameters of saturated rocks and the Lamé parameters of dry rocks, a brittleness indicator factor is constructed; this construction process directly quantifies the influence of fluid replacement on the mechanical properties of rocks by extracting the differences or combinations of the Lamé parameters of saturated rocks and dry rocks, thereby characterizing the brittleness of rocks in resisting fracture. The brittleness indicator factor (such as the brittleness indicator factor BI) is specifically constructed directly based on the Lamé constant and shear modulus, abandoning the indirect calculation mode of relying on Young's modulus and Poisson's ratio for complex nonlinear conversion in traditional methods.

[0050] In this embodiment, the brittleness indicator factor is constructed directly using Lamé parameters from saturated and dry rocks, completely eliminating the inversion noise and accumulated systematic errors caused by multi-level conversion and iterative calculation of traditional elastic parameters. This enables a direct and accurate quantitative evaluation of the brittleness characteristics of shale reservoirs. Furthermore, it unifies the mathematical expressions of brittleness parameters and fluid parameters within the Lamé parameter system, breaking through the technical bottleneck of separate steps and poor parameter matching between fluid prediction and brittleness evaluation. This lays a solid parameter foundation for the subsequent integrated synchronous and collaborative inversion of geological and engineering sweet spots.

[0051] In some embodiments, constructing a target reflection coefficient equation for a vertically and laterally isotropic medium includes: obtaining a conventional reflection coefficient equation expressed in terms of Lamé parameters, the ratio of P-wave to S-wave velocity in saturated rock, and anisotropic parameters of the vertically and laterally isotropic medium; wherein, the Lamé parameters are introduced to maintain the consistency of the parameter system with the previously constructed fluid factor and brittleness factor, while the ratio of P-wave to S-wave velocity in saturated rock and anisotropic parameters are introduced to accurately characterize the inherent vertically and laterally isotropic characteristics of shale reservoirs and the propagation law of seismic waves within them, thereby overcoming the model bias caused by the traditional isotropic medium assumption; substituting the compaction-decoupled fluid factor and brittleness indicator factor into the conventional reflection coefficient equation for variable substitution, resulting in a target reflection coefficient equation containing coefficient terms of the compaction-decoupled fluid factor and brittleness indicator factor. This variable substitution process aims to transform traditional purely elastic parameters (such as P-wave and S-wave velocity, density, etc.) into fluid parameters that directly characterize geological sweet spots and brittleness parameters that characterize engineering sweet spots, thereby establishing a direct mathematical connection between seismic reflection amplitude and underground reservoir geological and engineering properties, eliminating errors caused by intermediate parameter conversion at the source.

[0052] In some embodiments, converting the target reflection coefficient equation into a frequency-varying reflection coefficient equation includes: expressing the elastic parameters in the target reflection coefficient equation as complex numbers that vary with frequency; since shale reservoirs have complex pore structures and fluid storage characteristics, they exhibit significant viscoelastic properties overall, and when seismic waves pass through them, they cause obvious dispersion and attenuation phenomena. Therefore, using a complex form can simultaneously and accurately characterize the velocity dispersion effect (complex real part) and the energy attenuation effect (complex imaginary part); performing a Taylor expansion on the target reflection coefficient equation expressed in complex form at a preset reference frequency to extract the frequency variation term; this reference frequency is usually selected as the dominant frequency or center frequency of the seismic data. The complex nonlinear frequency variation relationship is linearized or approximated through Taylor expansion, thereby separating the disturbance term that is highly sensitive to frequency changes; subtracting the reflection coefficient equation at the reference frequency from the expanded equation yields the frequency-varying reflection coefficient equation. The purpose of this subtraction operation is to eliminate background reflection at the reference frequency (i.e., the zero-order term independent of frequency) and purely extract the reflection coefficient perturbation caused by frequency variation. This perturbation directly reflects the viscoelastic dispersion and attenuation characteristics of the formation and is extremely sensitive to underground fluids and microfractures.

[0053] In the construction of the target reflection coefficient equation, this disclosure fully considers the vertical and lateral isotropic characteristics of shale reservoirs. Through variable substitution, the seismic response is directly mapped to the compaction-decoupled fluid factor and brittleness indicator factor, achieving direct coupling between the anisotropic physical model and geological and engineering sweet spot parameters. This provides a rigorous and suitable physical model foundation for synchronous inversion. In the transformation of the frequency-varying reflection coefficient equation, by introducing complex forms and Taylor expansion techniques, the frequency dimension information in the seismic data is fully exploited. This expands the traditional analysis relying solely on amplitude to a frequency-varying analysis combining amplitude and frequency, effectively characterizing the viscoelastic dispersion and attenuation characteristics of shale reservoirs and significantly improving the inversion model's sensitivity to complex pore structures and fluids. This frequency-varying reflection coefficient equation provides solid mathematical support for subsequent construction of objective functions within a Bayesian framework and the realization of high-precision, high-stability synchronous collaborative inversion of fluids and brittleness.

[0054] In some embodiments, an inversion objective function is constructed based on the frequency-varying reflection coefficient equation and pre-stack seismic data, including: constructing a likelihood function based on a Bayesian inversion framework, combining the frequency-varying reflection coefficient equation and pre-stack seismic data; this likelihood function is used to quantify the degree of data fit between the actual observed seismic data and the theoretical synthetic data obtained by forward modeling of the frequency-varying reflection coefficient equation, thereby establishing a data-driven term; constructing a priori model constraint term including compaction decoupling fluid factor, brittleness indicator factor, and anisotropic parameters of vertically and laterally isotropic media; since seismic inversion has inherent ambiguity and ill-conditioned nature, the introduction of a priori model constraint term aims to use the prior information provided by well logging data or geological laws to reasonably constrain the space of the inversion solution, prevent non-physical drastic oscillations in the inversion results, and ensure that the inversion results conform to the actual petrophysical characteristics of shale reservoirs; and constructing the inversion objective function by combining the likelihood function and the priori model constraint term. This objective function transforms the complex geophysical inversion problem into a mathematical optimization problem by weighting and combining data fitting terms with prior constraints. By finding the parameter combination that minimizes the objective function, the optimal inversion result can be obtained.

[0055] In some embodiments, acquiring pre-stack seismic data of the study area includes: acquiring partial angle-stacked seismic profiles with different center incident angles in the study area; profiles with different center incident angles contain rich information on amplitude variation with offset, where small-angle data mainly reflects P-wave impedance characteristics, while large-angle data is more sensitive to S-wave impedance and density variations. Angle-segmented processing can more comprehensively extract the elastic response characteristics of the reservoir; frequency-segmented seismic profiles with multiple dominant frequencies are obtained based on wavelet transform; compared with traditional Fourier transform, wavelet transform has better time-frequency localization resolution, which can more accurately separate and extract the amplitude and phase characteristics of seismic signals in specific frequency bands, effectively avoiding frequency aliasing; the frequency-segmented seismic profiles are used as pre-stack seismic data. Decomposing broadband seismic data into multiple narrow-band data provides direct data support for subsequent frequency-varying inversion using the frequency-varying reflection coefficient equation, thereby fully exploring the dispersion and attenuation information generated when seismic waves propagate in shale viscoelastic media.

[0056] In some embodiments, after simultaneously obtaining the compaction-decoupled fluid factor and brittleness indicator factor of the shale reservoir, the method further includes simultaneously obtaining the anisotropic parameters of the vertically and laterally isotropic medium. The bedding and microfractures developed in the shale reservoir lead to strong anisotropy. Anisotropic parameters not only affect the propagation law of seismic waves but also directly control the expansion direction of the hydraulic fracture network, serving as a key bridge connecting geological and engineering evaluations. Based on the compaction-decoupled fluid factor, brittleness indicator factor, and anisotropic parameters of the vertically and laterally isotropic medium, an integrated matching evaluation of geological sweet spots and engineering sweet spots is performed. This evaluation process jointly analyzes and overlays the fluid factor characterizing the degree of hydrocarbon enrichment with the brittleness factor and anisotropic parameters characterizing fracturability and fracture development potential within a unified three-dimensional spatial grid. This breaks through the limitations of parameter spatial mismatch and fragmented evaluation standards in traditional step-by-step evaluations, directly delineating the optimal target area that combines high gas content and good fracturability.

[0057] The following detailed description of the seismic synchronous prediction method for shale reservoir fluids and brittleness is provided through a specific embodiment.

[0058] Russell fluid factor Due to its strong correlation with reservoir fluid properties, it has become an important tool for fluid identification. Its theoretical expression is:

[0059]

[0060] in, and These are the longitudinal and transverse wave velocities, respectively. It is density; It is the ratio of longitudinal and transverse wave velocities in dry rock.

[0061] However, because this fluid factor is simultaneously influenced by the rock skeleton and the properties of the stored fluids, it is prone to fluid identification artifacts, hindering efficient and accurate reservoir exploration. To address this issue, a solid-liquid decoupled fluid factor can be obtained based on the Nur critical porosity model, thereby effectively separating the influence of the rock skeleton and the fluid. However, the above method is based on a fixed critical porosity value and fails to consider the changes in the rock skeleton caused by formation compaction. Therefore, in this disclosure, a compaction decoupled fluid factor is adopted. First, the relationship between the compaction decoupled fluid factor and the Russell fluid factor can be expressed as:

[0062]

[0063] in, It is the compaction decoupling fluid factor; ; ; It refers to porosity.

[0064] By introducing the Lee model:

[0065]

[0066] The expression for the final compaction fluid factor can then be obtained:

[0067]

[0068] in, This represents the compaction coefficient. For ease of subsequent derivation, the compaction fluid factor is re-expressed using Lamé parameters:

[0069]

[0070] in, and This refers to the Lamé parameters. Similarly, in this disclosure, the fragility indicator BI is constructed using the Lamé parameters:

[0071]

[0072] To construct a reflection coefficient equation directly characterized by the compaction decoupled fluid factor and the brittleness indicator factor, a reflection coefficient equation for VTI media expressed in terms of Lamé parameters is introduced:

[0073]

[0074] in, It is the ratio of longitudinal and transverse wave velocities in saturated rock; and These are the anisotropic parameters of the VTI medium. Substituting the compaction decoupling fluid factor and the brittleness indicator factor, we can obtain the final reflection coefficient equation:

[0075]

[0076] The coefficient term is:

[0077]

[0078] Figure 2 The diagram shows a comparison of the AVO (Aspect-Value Occurrence) between the new reflection coefficient equation constructed in this disclosure and the conventional reflection coefficient equation. The red line represents the new equation; the blue line represents the conventional equation. From... Figure 2 As can be seen, the new equation agrees well with the conventional equation, verifying the accuracy of this method.

[0079] Due to the complex pore structure and fluid storage characteristics of the reservoir, it exhibits viscoelastic properties overall. When seismic waves pass through it, they undergo dispersion and attenuation, causing the reflection characteristics of the earthquake to vary with frequency. Therefore, the new reflection coefficient equation can be written as:

[0080]

[0081] Apply the above equation at the reference frequency Performing a Taylor expansion at this point yields:

[0082]

[0083] in, , , , Subtract the reference frequency from the above equation. The equation for the reflection coefficient can be obtained as follows:

[0084]

[0085] Based on the above equations, an objective function is constructed within a Bayesian framework. The final inversion result can then be obtained through optimization.

[0086] This disclosed solution addresses the problems of existing shale reservoir seismic inversion techniques, such as neglecting VTI anisotropy characteristics, poor matching between step-by-step inversion of fluid and brittle parameters, susceptibility to identification artifacts from traditional fluid factors and inability to adapt to formation compaction variations, large cumulative errors in indirect calculation of the brittle index, and insufficient utilization of seismic frequency information. These issues lead to low accuracy in reservoir sweet spot evaluation and distortion in the evaluation of hydrocarbon enrichment zones and fracturing capabilities. Specific benefits include:

[0087] Significantly improved fluid identification accuracy: By adopting compaction-decoupled fluid factors, the interference of the rock skeleton on fluid identification is effectively removed, overcoming the shortcomings of traditional fluid factors that are prone to identification artifacts and cannot adapt to changes in skeleton parameters under formation compaction. It accurately depicts the fluid distribution and gas-bearing characteristics of VTI shale reservoirs, and the fluid identification results are more consistent with the measured well logging data.

[0088] The brittleness assessment error is significantly reduced: The brittleness indicator factor is directly constructed based on Lamé parameters, which abandons the indirect solution method of multi-level conversion and iterative calculation of traditional elastic parameters. This completely eliminates the inversion noise and accumulated systematic error caused by multi-level parameter conversion, and realizes the direct and accurate quantitative evaluation of the brittleness characteristics and fracturability of shale reservoirs.

[0089] Simultaneous inversion of geological and engineering sweet spot parameters: Breaking the technical bottleneck of separate fluid prediction and brittleness evaluation with poor parameter matching, by constructing a frequency-varying reflection coefficient equation, the gas content and brittleness parameters of reservoir fluids are obtained simultaneously, realizing the integrated and accurate evaluation of geological and engineering sweet spots, and greatly improving inversion efficiency and sweet spot matching accuracy.

[0090] The technology is highly practical and adaptable: it makes full use of seismic amplitude and frequency information, and optimizes the solution based on the Bayesian framework. The model has high stability and strong anti-interference ability. It has been verified by actual seismic data in the work area. The inversion results are in high agreement with the well logging interpretation results. It can effectively adapt to the needs of fine exploration and development evaluation of complex VTI shale reservoirs.

[0091] The inventors used actual work area data to verify the effectiveness of the disclosed method. Figures 3a to 3c This paper presents partially stacked seismic profiles of a certain work area with different center incident angles. Based on these three pre-stack profiles, wavelet transform can be used to obtain seismic profiles with different dominant frequencies. The frequency division results are shown in Figure 4-6. Figures 4a to 4d The following are frequency-divided seismic profiles obtained based on small-angle seismic data: (a) 5Hz, (b) 15Hz, (c) 25Hz, and (d) 35Hz. Figures 5a to 5d The following are frequency-divided seismic profiles obtained based on mid-angle seismic data: (a) 5Hz, (b) 15Hz, (c) 25Hz, and (d) 35Hz. Figures 6a to 6d The following are the frequency-divided seismic profiles obtained from large-angle seismic data: (a) 5Hz, (b) 15Hz, (c) 25Hz, and (d) 35Hz.

[0092] Using the above as input, the final inversion result shown in Figure 7 can be obtained through the constructed pre-stack frequency variation inversion method. Figure 7(a) shows the brittleness indicator result, Figure 7(b) shows the gas content indicator result, and Figures 7(c) and 7(d) show the anisotropy parameter indicator results. As can be seen from the figures, the inversion results agree well with the well logging indicator results, verifying the effectiveness of the new method disclosed in this paper.

[0093] 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.

[0094] 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.

[0095] Based on the same inventive concept, this disclosure also provides an electronic device. Figure 8 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Figure 8 As shown, an embodiment of the present invention provides an electronic device including: one or more processors 801, a memory 802, and one or more I / O interfaces 803. The memory 802 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 synchronous prediction methods for shale reservoir fluids and brittleness as described in the above embodiments; the one or more I / O interfaces 803 are connected between the processor and the memory, configured to enable information interaction between the processor and the memory.

[0096] Among them, processor 801 is a device with data processing capabilities, including but not limited to central processing unit (CPU); memory 802 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) 803 is connected between processor 801 and memory 802, and can realize information interaction between processor 801 and memory 802, including but not limited to data bus (Bus).

[0097] In some embodiments, the processor 801, memory 802, and I / O interface 803 are interconnected via bus 804, and thus connected to other components of the computing device.

[0098] In some embodiments, the one or more processors 801 include a field-programmable gate array.

[0099] 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 above-described methods for synchronous seismic prediction of shale reservoir fluids and brittleness. The computer-readable storage medium can be volatile or non-volatile.

[0100] 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-described method for synchronous prediction of shale reservoir fluids and brittleness.

[0101] 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".

[0102] 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.

[0103] 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 method for simultaneous seismic prediction of fluid and brittleness in shale reservoirs, characterized in that, include: Acquire pre-stack seismic data for the study area; A target reflection coefficient equation is constructed for a vertically and laterally isotropic medium. The target reflection coefficient equation is directly characterized by a compaction decoupling fluid factor and a brittleness indicator factor. The compaction decoupling fluid factor is a parameter obtained by introducing a compaction coefficient that characterizes the compaction effect of the formation and performing solid-liquid decoupling. The brittleness indicator factor is a parameter directly constructed based on Lamé parameters. Based on the dispersion and attenuation characteristics of seismic waves propagating in shale reservoirs, the target reflection coefficient equation is transformed into a frequency-varying reflection coefficient equation. Based on the frequency-varying reflection coefficient equation and the pre-stack seismic data, an inversion objective function is constructed; The inversion objective function is optimized and solved to simultaneously obtain the compaction decoupling fluid factor and the brittleness indicator factor of the shale reservoir.

2. The method according to claim 1, characterized in that, The process of constructing the compaction decoupling fluid factor includes: Obtain the initial fluid factor; Based on the rock physics decoupling model that takes into account formation compaction, the compaction coefficient is introduced to perform solid-liquid decoupling on the initial fluid factor to obtain the initial compaction decoupled fluid factor. The initial compaction decoupling fluid factor is converted into the compaction decoupling fluid factor represented by the Lamé parameter.

3. The method according to claim 1, characterized in that, The process of constructing the fragility indicator includes: Obtain the Lamé parameters for saturated rocks and dry rocks; The brittleness indicator factor is constructed based on the Lamé parameters of the saturated rock and the Lamé parameters of the dry rock.

4. The method according to claim 1, characterized in that, The construction of the target reflection coefficient equation under a vertically and laterally isotropic medium includes: Obtain the conventional reflection coefficient equation expressed in terms of Lamé parameters, the P-wave to S-wave velocity ratio of saturated rock, and the anisotropic parameters of the vertical and transverse isotropic medium. By substituting the compaction decoupling fluid factor and the brittleness indicator factor into the conventional reflection coefficient equation for variable substitution, the target reflection coefficient equation containing the coefficient terms of the compaction decoupling fluid factor and the brittleness indicator factor is obtained.

5. The method according to claim 1, characterized in that, The step of converting the target reflection coefficient equation into a frequency-varying reflection coefficient equation includes: The elastic parameter in the target reflection coefficient equation is expressed as a complex number that varies with frequency; At a preset reference frequency, the target reflection coefficient equation, expressed in complex form, is subjected to Taylor expansion to extract the frequency variation term; Subtracting the reflection coefficient equation at the reference frequency from the expanded equation yields the frequency-varying reflection coefficient equation.

6. The method according to claim 1, characterized in that, The inversion objective function is constructed based on the frequency-varying reflection coefficient equation and the pre-stack seismic data, including: Based on the Bayesian inversion framework, a likelihood function is constructed by combining the frequency-varying reflection coefficient equation and the pre-stack seismic data. Construct a priori model constraint term that includes the compaction decoupling fluid factor, the brittleness indicator factor, and the anisotropic parameters of the vertical and transverse isotropic medium; The inversion objective function is constructed by combining the likelihood function and the prior model constraints.

7. The method according to claim 1, characterized in that, The acquisition of pre-stack seismic data for the study area includes: Obtain partial angle-stacked seismic profiles of different center incident angles in the study area; Based on wavelet transform, the partial angle superimposed seismic profiles are frequency-divided to obtain multiple dominant frequency seismic profiles under different center incident angles. The frequency-divided seismic profile is used as the pre-stack seismic data.

8. The method according to any one of claims 1 to 7, characterized in that, After simultaneously obtaining the compaction decoupling fluid factor and the brittleness indicator factor of the shale reservoir, the method further includes: The anisotropic parameters of the vertically and laterally isotropic medium are obtained simultaneously. Based on the compaction decoupling fluid factor, the brittleness indicator factor, and the anisotropic parameters of the vertical and lateral isotropic medium, an integrated matching evaluation of geological sweet spots and engineering sweet spots is conducted.

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 synchronous prediction method for shale reservoir fluids and brittleness 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 synchronous prediction method for shale reservoir fluids and brittleness as described in any one of claims 1-8.

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