Stratum pressure prediction method and device, electronic equipment and storage medium

Abstract

The application provides a formation pressure prediction method and device, electronic equipment and storage medium. The method comprises the following steps: obtaining well logging data of a target formation; performing seismic inversion according to the well logging data of the target formation to obtain an inversion result; constructing a first pressure prediction model of the target formation corresponding to an undercompaction cause, a second pressure prediction model of the target formation corresponding to a hydrocarbon generation and pressure increase cause, and a third pressure prediction model of the target formation corresponding to a tectonic extrusion cause according to the inversion result; and fusing the first pressure prediction model, the second pressure prediction model and the third pressure prediction model by using a fuzzy logic algorithm to obtain a pressure prediction result of the target formation. The method realizes complex cause formation pressure prediction based on multi-information joint, and improves the drilling pre-prediction accuracy of formation pressure under complex geological conditions.

Description

Technical Field

[0001] This application relates to the field of petroleum exploration technology, and in particular to a formation pressure prediction method, apparatus, electronic device and storage medium. Background Technology

[0002] Anomalies in formation pressure are common worldwide, and their causes are diverse, potentially physical, chemical, or a combination of both. Oil and gas reservoirs often exhibit pressure anomalies, especially abnormally high pressure. Abnormally high pressure not only indicates the enrichment of oil and gas reservoirs but also has a significant impact on drilling operations. Accurate pre-drilling prediction of formation pressure is crucial for sweet spot prediction and pre-drilling risk assessment.

[0003] The mechanisms underlying abnormal pressure are highly complex, encompassing nearly ten factors, including: ① undercompaction of mudstone; ② hydrothermal pressurization; ③ organic matter degradation (hydrocarbon generation and volume expansion); ④ pressure transmission (permeability); ⑤ formation water permeability (osmosis); ⑥ density differences (buoyancy); ⑦ tectonic compression (faults, folds, lateral slip or strike-slip, compression from the downthrown block of faults, salt dome or mudstone diapir movement, earthquakes); ⑧ gas migration; and ⑨ clay mineral dehydration. These can be broadly categorized into three types: sedimentary, hydrocarbon-generating, and tectonic.

[0004] For pre-drilling formation pressure prediction, seismic data-based pressure prediction has become one of the main methods due to its forward-looking and global nature. Formation overpressure can lead to a decrease in seismic wave velocity; therefore, the primary method for predicting formation pressure is currently seismic velocity prediction. A key issue in pressure prediction is determining the formation velocity. Only by obtaining a high-precision, high-resolution velocity field can a relatively accurate pressure field be obtained. Seismic velocity-based formation pressure prediction is mainly applicable to compaction-originating formations. For unconventional oil and gas reservoirs like shale gas, which are self-generated and self-storing, there is a lack of sufficient theoretical support. Therefore, seismic attribute-based formation pressure prediction methods were later developed. Seismic attributes have higher resolution, and by selecting appropriate attributes, the uncertainty in the velocity-pressure relationship in shale gas reservoirs can be effectively avoided. However, this type of method is not robust enough; the optimal selection of attributes relies on fitting analysis using a large amount of measured data, which in most cases cannot meet this requirement. Summary of the Invention

[0005] To address the aforementioned problems, this application provides a method, apparatus, electronic device, and storage medium for predicting formation pressure, which solves the technical problem of the difficulty in accurately predicting formation pressure before drilling in the prior art.

[0006] In a first aspect, this application provides a method for predicting formation pressure, the method comprising:

[0007] Obtain well logging data of the target formation;

[0008] Based on the well logging data of the target strata, seismic inversion is performed to obtain the inversion results;

[0009] Based on the inversion results, a first pressure prediction model for undercompaction, a second pressure prediction model for hydrocarbon generation and pressurization, and a third pressure prediction model for tectonic compression are constructed for the target strata.

[0010] The first pressure prediction model, the second pressure prediction model, and the third pressure prediction model are fused using a fuzzy logic algorithm to obtain the pressure prediction result of the target formation.

[0011] According to an embodiment of this application, optionally, in the above formation pressure prediction method, the inversion result includes a pre-stack depth migration velocity model for describing the correspondence between pre-stack depth migration velocity and spatial location.

[0012] Based on the inversion results, a first pressure prediction model for the undercompaction cause corresponding to the target stratum is constructed, including the following steps:

[0013] Based on the pre-stack depth migration velocity model, the first pressure prediction model for the undercompacted formation corresponding to the target stratum is constructed using the following formula:

[0014]

[0015] Where P1(x) is the formation pressure at spatial location x due to undercompaction, FC(v) is the correction factor, v is Poisson's ratio, FC(v) is the positive function of Poisson's ratio v, Vmax and Vmin are the seismic wave velocities when porosity is close to zero and rigidity is close to zero, respectively. x P represents the pre-stack depth migration velocity at spatial location x. OV This refers to the pressure of the overlying strata.

[0016] According to an embodiment of this application, optionally, in the above-described formation pressure prediction method, seismic inversion is performed based on well logging data of the target formation to obtain inversion results, including the following steps:

[0017] Based on the well logging data of the target formation, a pre-stack time migration velocity model is constructed, and the pre-stack time migration velocity model is used as the initial velocity model for pre-stack depth migration.

[0018] CIP gathers of the target strata were generated using the Kirchhoff pre-stack depth migration method.

[0019] Based on the CIP gather, the initial velocity model is optimized and the velocity perturbation in the depth domain is obtained by solving the mesh tomography equations, resulting in the final pre-stack depth migration velocity model.

[0020] According to an embodiment of this application, optionally, in the above formation pressure prediction method, the inversion result includes the actual P-wave velocity of the target formation and the formation P-wave velocity under normal compaction conditions;

[0021] Based on the inversion results, a second pressure prediction model for the hydrocarbon generation and pressurization genesis corresponding to the target formation is constructed, including the following steps:

[0022] Based on the P-wave velocity of the target formation and the P-wave velocity of the formation under normal compaction conditions, a second pressure prediction model for the hydrocarbon generation and pressurization genesis corresponding to the target formation is constructed as follows:

[0023] P2(x)=P OV -(P OV -P h (V) x / V nct ) c ;

[0024] Where P2(x) is the formation pressure at spatial location x due to hydrocarbon generation and pressurization, P OV For the overlying formation pressure, P h For hydrostatic pressure, V x V represents the actual P-wave velocity at spatial location x of the target formation. nct Let c be the longitudinal wave velocity of the target formation at spatial location x under normal compaction conditions, where c is a constant.

[0025] According to an embodiment of this application, optionally, in the above-described formation pressure prediction method, seismic inversion is performed based on well logging data of the target formation to obtain inversion results, including the following steps:

[0026] Based on the well logging data of the target formation, seismic inversion is performed to obtain the actual P-wave velocity of the target formation;

[0027] Based on the well logging data of the target formation, calculate the elastic tensor of wet clay and the elastic tensor of sandy mixture in the target formation;

[0028] Based on the elastic tensor of the wet clay and the elastic tensor of the sandy mixture in the target formation, calculate the elastic tensor of the equivalent shale in the target formation.

[0029] The elastic tensor of the equivalent shale in the target formation is converted into acoustic transit time to obtain the longitudinal wave velocity of the target formation under normal compaction conditions.

[0030] According to an embodiment of this application, optionally, in the above formation pressure prediction method, the well logging data includes the total porosity of the rock, the volume content of clay and the porosity of wet clay in the target formation, as well as the type of each sandy mixture component, the volume content of each sandy mixture component and the elastic modulus of each sandy mixture component in the target formation.

[0031] Based on the well logging data of the target formation, the elastic tensor of the wet clay and the elastic tensor of the sandy mixture in the target formation are calculated, including the following steps:

[0032] Based on the total porosity of the rock, the volume content of clay, and the porosity of wet clay in the target stratum, the elastic tensor of the wet clay in the target stratum is calculated using the following formula:

[0033]

[0034]

[0035] Where κ is the porosity of the wet clay in the target stratum, and φ and f c These represent the total porosity of the rock and the volumetric content of clay in the target stratum, respectively. Let C be the elastic tensor of pure clay. ij Let be the elastic tensor of the wet clay in the target stratum. Assuming the clay has transverse isotropic symmetry, the elastic tensor of the clay can be characterized by five independent parameters using the following formula:

[0036]

[0037] Among them, C 11 C 13 C 33 C 44 and C 66 For the five independent parameters, The corresponding five independent parameters and They are respectively

[0038] Based on the types, volume fractions, and elastic moduli of the sandy mixtures in the target formation, the elastic modulus of the sandy mixtures in the target formation is calculated using the following formula, based on the Voight-Reuss-Hill model:

[0039]

[0040]

[0041]

[0042] Among them, f i M is the volume content of the i-th sandy mixture component, N is the number of sandy mixture components, and M is the volume content of the i-th sandy mixture component. i It is the elastic modulus of the i-th sandy mixture component, including bulk modulus K or shear modulus μ, M V M is the upper limit of the elastic modulus of the equivalent rock provided by the Voigt model. R M is the upper limit of the elastic modulus of the equivalent rock provided by the Reuss model. H It is the arithmetic mean of the upper and lower limits of the elastic modulus of the equivalent rock;

[0043] The elastic tensor of the sandy mixture in the target formation is calculated using the following formula based on the elastic modulus of the sandy mixture in the target formation:

[0044]

[0045] Among them, C xy Let be the elastic tensor of the sandy mixture in the target formation.

[0046] According to an embodiment of this application, optionally, in the above-described formation pressure prediction method, calculating the elastic tensor of the equivalent shale in the target formation based on the elastic tensor of the wet clay and the elastic tensor of the sandy mixture in the target formation includes the following steps:

[0047] Based on the elastic tensors of the clay and sand mixture in the target formation, the elastic tensor of the equivalent shale in the target formation is calculated using the following formula:

[0048]

[0049]

[0050]

[0051]

[0052]

[0053] C' 66 = <c 66 >;

[0054] C' 12 =C' 11 - <c 11 >+ <c 12 >;

[0055] Among them, C mn C' is the elastic tensor of the equivalent shale in the target formation.ij C mn The elastic stiffness component in the i-th row and j-th column, c ij Let represent the elastic stiffness components of the corresponding elastic tensors of wet clay, sandy mixtures, and organic matter. The elastic stiffness component of the elastic tensor of organic matter is a constant. Angle brackets <·> represent the volume-weighted average of the elastic stiffness components of the corresponding elastic tensors of wet clay, sandy mixtures, and organic matter within the brackets.

[0056] According to an embodiment of this application, optionally, in the above-described formation pressure prediction method, the elastic tensor of the equivalent shale in the target formation is converted into acoustic transit time to obtain the formation P-wave velocity under normal compaction conditions, including the following steps:

[0057] The elastic stiffness component of the elastic tensor of the equivalent shale in the target formation is converted into the formation P-wave velocity under normal compaction conditions using the following formula:

[0058]

[0059] Among them, V nct Let ρ be the longitudinal wave velocity of the target stratum at spatial location x under normal compaction conditions, and ρ be the rock density.

[0060] According to an embodiment of this application, optionally, in the above formation pressure prediction method, the inversion result includes the influence of the distance from the fault on the target formation pressure and the influence of the burial depth on the target formation pressure;

[0061] Based on the inversion results, a third pressure prediction model of tectonic compressional origin corresponding to the target strata is constructed, including the following steps:

[0062] Based on the influence of distance from the fault on the pressure of the target formation and the influence of burial depth on the pressure of the target formation, a third pressure prediction model corresponding to the tectonic compression of the target formation is constructed using the following formula:

[0063] P3(x)=a d *ΔP Dx +a t *ΔP Tx ;

[0064] Where P3(x) is the formation pressure caused by tectonic compression at spatial location x, ΔP Dx Let ΔP be the combined effect of the distance between point x (the point in space) and the fault on formation pressure. Tx To illustrate the effect of burial depth at spatial location x on formation pressure, a d and a tThese are the correction coefficients for the distance between spatial location x and the fault, and the burial depth at spatial location x, respectively.

[0065] According to an embodiment of this application, optionally, in the above formation pressure prediction method, the well logging data includes the distance between the target formation and each fault, and the time elapsed for the target formation;

[0066] Based on the well logging data of the target formation, seismic inversion is performed to obtain the inversion results, including the following steps:

[0067] The influence of the distance between the target formation and each fault on the pressure of the target formation is calculated using the following formula:

[0068]

[0069]

[0070] Where, ΔP Dxi The influence of the i-th fault on the formation pressure of the target stratum at spatial location x is represented by dis_DC. xi Let ci be the distance between the target stratum at spatial location x and the i-th fault, where ci is a constant and ci > 1, and ΔP Dx The combined effect of the distance between the target stratum and m faults at spatial location x on the formation pressure at spatial location x;

[0071] The impact of burial depth on the pressure in the target formation is calculated using the following formula, based on the time elapsed in the target formation:

[0072] ΔP Tx =β*time x +ε;

[0073] Among them, time x Let ε be the time elapsed at the target stratum at spatial location x, where β and ε are constants.

[0074] According to an embodiment of this application, optionally, in the above-described formation pressure prediction method, a fuzzy logic algorithm is used to fuse the first pressure prediction model, the second pressure prediction model, and the third pressure prediction model to obtain the pressure prediction result of the target formation, including the following steps:

[0075] The first pressure prediction model, the second pressure prediction model, and the third pressure prediction model are normalized to obtain the normalized results of the first pressure prediction model, the second pressure prediction model, and the third pressure prediction model.

[0076] Based on the normalization results, the membership functions corresponding to the first pressure prediction model, the second pressure prediction model, and the third pressure prediction model are constructed.

[0077] Based on the membership function, calculate the relative distance between the membership degrees of any two pressure prediction models among the first pressure prediction model, the second pressure prediction model, and the third pressure prediction model at any spatial location.

[0078] Define a fuzzy proximity function such that at any spatial location, the greater the relative distance between the membership degrees of two pressure prediction models, the smaller their fuzzy proximity; conversely, the smaller the relative distance, the greater their fuzzy proximity.

[0079] The fuzzy proximity function is used to obtain the comprehensive fuzzy proximity of the membership degree of each pressure prediction model to the membership degree of other pressure prediction models.

[0080] The comprehensive fuzzy proximity of the membership degrees of each stress prediction model to other stress prediction models is normalized to obtain the relative weights of each stress prediction model.

[0081] Based on the relative weights of each pressure prediction model, the first pressure prediction model, the second pressure prediction model, and the third pressure prediction model are fused to obtain the pressure prediction result of the target formation.

[0082] According to an embodiment of this application, optionally, in the above-described formation pressure prediction method, normalizing the first pressure prediction model, the second pressure prediction model, and the third pressure prediction model to obtain normalized results of the first pressure prediction model, the second pressure prediction model, and the third pressure prediction model includes the following steps:

[0083] The first pressure prediction model, the second pressure prediction model, and the third pressure prediction model are normalized using the following formula to obtain the normalized results of the first pressure prediction model, the second pressure prediction model, and the third pressure prediction model:

[0084]

[0085] Among them, w i (x) represents the normalized result of the pressure prediction model at spatial location x.

[0086] According to an embodiment of this application, optionally, in the above-described formation pressure prediction method, the membership function is:

[0087]

[0088] Where, μ i(x) represents the membership degree of the i-th pressure prediction model at spatial location x.

[0089] According to an embodiment of this application, optionally, in the above-described formation pressure prediction method, the relative distance between the membership degrees of any two pressure prediction models among the first pressure prediction model, the second pressure prediction model, and the third pressure prediction model is:

[0090] di j (x)=|μ i (x)-μ j (x)|;

[0091] Where, d ij (x) represents the relative distance between the membership degrees of the i-th pressure prediction model and the j-th pressure prediction model at spatial location x, μ i (x) represents the membership degree of the i-th pressure prediction model at spatial location x, μ j (x) represents the membership degree of the j-th pressure prediction model at spatial location x.

[0092] According to an embodiment of this application, optionally, in the above formation pressure prediction method, the fuzzy proximity function is:

[0093]

[0094] Among them, a ij (x) is the fuzzy proximity function between the i-th and j-th pressure prediction models at spatial location x, where max{d ij} represents the maximum relative distance between the membership degrees of the i-th pressure prediction model and the j-th pressure prediction model at each spatial location.

[0095] According to an embodiment of this application, optionally, in the above-described formation pressure prediction method, the comprehensive fuzzy proximity degree of the membership degree of each pressure prediction model and the membership degree of other pressure prediction models is obtained through the fuzzy proximity function, including the following steps:

[0096] The fuzzy proximity function is used to calculate the fuzzy proximity between any two pressure prediction models at any spatial location, thus obtaining the fuzzy proximity matrix:

[0097]

[0098] Where A(x) is the fuzzy proximity matrix;

[0099] Based on the fuzzy proximity matrix, the comprehensive fuzzy proximity u of the membership degrees of each pressure prediction model and the membership degrees of other pressure prediction models is calculated. iBased on the probability source merging theory, a set of nonnegative numbers b1(x), b2(x), and b3(x) are required to satisfy the following conditions:

[0100] u i (x)=b1(x)a i1 (x)+b1(x)a i2 (x)+…+b3(x)a i3 (x), i = 1, 2, 3;

[0101] Introduce two eigenvectors U(x) and B(x), and let U(x) = [u1(x), u2(x), u3(x)]. T B(x) = [b1(x), b2(x), b3(x)] T Thus, the following matrix form is obtained:

[0102] U(x) = A(x)B(x);

[0103] Define a maximum fuzzy eigenvalue λ such that λB(x) = A(x)B(x), obtain the eigenvector B(x), and use the corresponding fuzzy closeness matrix as the comprehensive fuzzy closeness of the membership degree of each pressure prediction model and the membership degree of other pressure prediction models; where the comprehensive fuzzy closeness of the membership degree of each pressure prediction model and the membership degree of other pressure prediction models is:

[0104] u i (x)=λb i (x)=b1(x)a i1 (x)+b1(x)a i2 (x)+…+b3(x)a i3 (x).

[0105] According to an embodiment of this application, optionally, in the above formation pressure prediction method, the relative weights of each pressure prediction model are:

[0106]

[0107] Among them, w i (x) represents the relative weight of the i-th pressure prediction model at spatial location x.

[0108] According to an embodiment of this application, optionally, in the above formation pressure prediction method, the pressure prediction result of the target formation is:

[0109]

[0110] Wherein, P(x) is the predicted pressure of the target formation at spatial location x, P i (x) is the pressure prediction model at spatial location x.

[0111] Secondly, this application provides a formation pressure prediction device, the device comprising:

[0112] The acquisition module is used to acquire well logging data of the target formation;

[0113] The inversion module is used to perform seismic inversion based on the well logging data of the target formation to obtain the inversion results;

[0114] The model building module is used to construct, based on the inversion results, a first pressure prediction model for undercompaction, a second pressure prediction model for hydrocarbon generation and pressurization, and a third pressure prediction model for tectonic compression corresponding to the target strata.

[0115] The fusion module is used to fuse the first pressure prediction model, the second pressure prediction model, and the third pressure prediction model using a fuzzy logic algorithm to obtain the pressure prediction result of the target formation.

[0116] Thirdly, this application provides an electronic device including a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, performs the formation pressure prediction method as described in any one of the first aspects.

[0117] Fourthly, this application provides a storage medium storing a computer program that can be executed by one or more processors and can be used to implement the formation pressure prediction method as described in any of the first aspects.

[0118] Compared with the prior art, one or more embodiments of the above solutions may have the following advantages or beneficial effects:

[0119] This application provides a formation pressure prediction method, apparatus, electronic device, and storage medium. The method includes acquiring well logging data of a target formation; performing seismic inversion based on the well logging data of the target formation to obtain inversion results; constructing a first pressure prediction model for undercompacted formation, a second pressure prediction model for hydrocarbon generation and pressurization, and a third pressure prediction model for tectonic compression corresponding to the target formation based on the inversion results; and fusing the first, second, and third pressure prediction models using a fuzzy logic algorithm to obtain the pressure prediction result of the target formation. This method achieves complex formation pressure prediction based on multi-information fusion, improving the accuracy of pre-drilling formation pressure prediction under complex geological conditions. Attached Figure Description

[0120] The present application will be described in more detail below based on embodiments and with reference to the accompanying drawings:

[0121] Figure 1A schematic flowchart illustrating a formation pressure prediction method provided in this application embodiment;

[0122] Figure 2 A high-precision seismic layer velocity profile (geologically guided constrained grid tomography) of a target stratum provided for an embodiment of this application;

[0123] Figure 3 The above-mentioned target formation pressure coefficient plan view (undercompaction cause) provided for the embodiments of this application;

[0124] Figure 4 The above-mentioned target formation pressure coefficient profile (hydrocarbon generation and pressurization cause) provided for the embodiments of this application;

[0125] Figure 5 The above-mentioned target formation pressure coefficient planar diagram (hydrocarbon generation and pressurization origin) provided for the embodiments of this application;

[0126] Figure 6 The above-mentioned target formation pressure coefficient profile (compressional tectonic origin) provided for the embodiments of this application;

[0127] Figure 7 Plan view of the target formation pressure coefficient (compressional tectonic origin) provided for the embodiments of this application;

[0128] Figure 8 The target formation pressure coefficient planar diagram (multi-genetic fuzzy logic fusion) provided for the embodiments of this application;

[0129] Figure 9 A schematic diagram of a formation pressure prediction device provided in an embodiment of this application;

[0130] In the accompanying drawings, the same parts are referred to by the same reference numerals, and the drawings are not drawn to scale. Detailed Implementation

[0131] The following detailed description of the embodiments of this application, in conjunction with the accompanying drawings, will provide a thorough understanding of how this application uses technical means to solve technical problems and achieve corresponding technical effects, enabling its implementation. The embodiments of this application and the various features within them can be combined with each other without conflict, and all resulting technical solutions are within the protection scope of this application.

[0132] Furthermore, numerous specific details are set forth in the following description for purposes of explanation, in order to provide a thorough understanding of the embodiments of the invention. However, it will be apparent to those skilled in the art that the invention may be practiced without the specific details herein or the particular methods described.

[0133] Example 1

[0134] Figure 1 Please refer to the flowchart of a formation pressure prediction method provided in this application embodiment. Figure 1 This embodiment provides a method for predicting formation pressure, including:

[0135] Step S110: Obtain well logging data for the target formation.

[0136] Step S120: Perform seismic inversion based on the well logging data of the target formation to obtain the inversion results.

[0137] In some embodiments, the inversion results include a pre-stack depth migration velocity model that describes the correspondence between pre-stack depth migration velocity and spatial location.

[0138] Correspondingly, step S120 includes the following steps:

[0139] S121: Based on the well logging data of the target formation, construct a pre-stack time migration velocity model, and use the pre-stack time migration velocity model as the initial velocity model for pre-stack depth migration.

[0140] S122: Generate the CIP gather of the target strata using the Kirchhoff pre-stack depth migration method;

[0141] S123: Based on the CIP gather, the initial velocity model is optimized and the velocity perturbation in the depth domain is obtained by solving the mesh tomography equation, thus obtaining the final pre-stack depth migration velocity model.

[0142] Overpressure in the formation can lead to a decrease in seismic wave velocity. Under normal compaction conditions, formation velocity increases with depth. When underpressure occurs, the formation velocity is lower than normal. When there is overpressure in the formation, the seismic wave propagation velocity of the formation rocks often decreases significantly, resulting in a low-velocity anomaly. Therefore, the primary method for predicting formation pressure is currently seismic velocity prediction. The key issue in pressure prediction is determining the formation velocity. Only by obtaining a high-precision, high-resolution velocity field can a relatively accurate pressure field be obtained.

[0143] The establishment of a high-precision velocity model includes structural modeling, near-surface velocity model establishment, initial velocity modeling, and model optimization, iteration, and updating. Among these, DWT-reflection mesh tomography velocity inversion and model optimization iteration are the key and core steps. First, a pre-stack time-migrating velocity model is used as the initial velocity model for pre-stack depth migration, which significantly improves the efficiency of pre-stack depth migration velocity modeling and ensures a certain level of accuracy for the initial velocity model. Second, the Kirchhoff pre-stack depth migration method is used to generate CIP gathers for the target line, providing a relatively stable basis for picking up the residual delay axis of the reflected wave. The optimization and iteration of the velocity model involves solving the mesh tomography equations to obtain the velocity perturbation in the depth domain, and introducing geologically guided constrained mesh tomography technology to improve the accuracy of velocity inversion. After multiple iterations, a high-precision velocity model is finally obtained.

[0144] In some embodiments, the inversion results include the actual P-wave velocity of the target formation and the formation P-wave velocity under normal compaction conditions.

[0145] Correspondingly, step S120 includes the following steps:

[0146] S124: Based on the well logging data of the target formation, perform seismic inversion to obtain the actual P-wave velocity of the target formation;

[0147] S125: Based on the well logging data of the target formation, calculate the elastic tensor of the wet clay and the elastic tensor of the sandy mixture in the target formation;

[0148] S126: Calculate the elastic tensor of the equivalent shale in the target stratum based on the elastic tensor of the wet clay and the elastic tensor of the sandy mixture in the target stratum.

[0149] S127: Convert the elastic tensor of the equivalent shale in the target formation into acoustic transit time to obtain the longitudinal wave velocity of the target formation under normal compaction conditions.

[0150] The well logging data includes the total porosity of the rock, the volume content of clay and the porosity of wet clay in the target formation, as well as the types of each sandy mixture component, the volume content of each sandy mixture component and the elastic modulus of each sandy mixture component in the target formation.

[0151] Step S125 includes the following steps:

[0152] S125a: Based on the total porosity of the rock, the volume content of clay, and the porosity of wet clay in the target stratum, the elastic tensor of the wet clay in the target stratum is calculated using the following formula:

[0153]

[0154]

[0155] Where κ is the porosity of the wet clay in the target stratum, and φ and f c These represent the total porosity of the rock and the volumetric content of clay in the target stratum, respectively. Let C be the elastic tensor of pure clay. ij Let be the elastic tensor of the wet clay in the target stratum. Assuming the clay has transverse isotropic symmetry, the elastic tensor of the clay can be characterized by five independent parameters using the following formula:

[0156]

[0157] Among them, C 11 C 13 C 33 C 44 and C 66 For the five independent parameters, The corresponding five independent parameters and They are respectively

[0158] S125b: Based on the types, volume fractions, and elastic moduli of each sandy mixture component in the target formation, the elastic modulus of the sandy mixture in the target formation is calculated using the following formula, based on the Voight-Reuss-Hill model:

[0159]

[0160]

[0161]

[0162] Among them, f i M is the volume content of the i-th sandy mixture component, N is the number of sandy mixture components, and M is the volume content of the i-th sandy mixture component. i It is the elastic modulus of the i-th sandy mixture component, including bulk modulus K or shear modulus μ, M V M is the upper limit of the elastic modulus of the equivalent rock provided by the Voigt model. R M is the upper limit of the elastic modulus of the equivalent rock provided by the Reuss model. H It is the arithmetic mean of the upper and lower limits of the elastic modulus of the equivalent rock;

[0163] S125c: Using the elastic modulus of the sandy mixture in the target formation, calculate the elastic tensor of the sandy mixture in the target formation using the following formula:

[0164]

[0165] Among them, C xy Let be the elastic tensor of the sandy mixture in the target formation.

[0166] Step S126 includes the following steps:

[0167] Based on the elastic tensors of the clay and sand mixture in the target formation, the elastic tensor of the equivalent shale in the target formation is calculated using the following formula:

[0168]

[0169]

[0170]

[0171]

[0172]

[0173] C' 66 = <c 66 >;

[0174] C' 12 =C' 11 - <c 11 >+ <c 12 >;

[0175] Among them, C mn C' is the elastic tensor of the equivalent shale in the target formation. ij C mn The elastic stiffness component in the i-th row and j-th column, c ij Let represent the elastic stiffness components of the corresponding elastic tensors of wet clay, sandy mixtures, and organic matter. The elastic stiffness component of the elastic tensor of organic matter is a constant. Angle brackets <·> represent the volume-weighted average of the elastic stiffness components of the corresponding elastic tensors of wet clay, sandy mixtures, and organic matter within the brackets.

[0176] Bulk modulus K of organic matter O =2.9 GPa, shear modulus μ O =2.7 GPa, the elastic tensor matrix of organic matter is:

[0177]

[0178] Among them, C O Let be the elastic tensor of organic matter.

[0179] In addition, step S127 includes the following steps:

[0180] The elastic stiffness component of the elastic tensor of the equivalent shale in the target formation is converted into the formation P-wave velocity under normal compaction conditions using the following formula:

[0181]

[0182] Among them, V nct Let ρ be the longitudinal wave velocity of the target stratum at spatial location x under normal compaction conditions, and ρ be the rock density.

[0183] In some cases, the formation P-wave velocity V nct The value was obtained by fitting the P-wave velocity of the pure mudstone section. The fitted V value was obtained by... nc The t-curve is usually relatively smooth and can reflect a certain trend, but it cannot reflect more detailed changes, let alone express the increase in pore pressure caused by hydrocarbon generation in the organic matter enrichment zone.

[0184] In some embodiments, the inversion results include the influence of distance from faults on the target formation pressure and the influence of burial depth on the target formation pressure. Correspondingly, the well logging data includes the distances between the target formation and each fault, as well as the elapsed time of the target formation.

[0185] Correspondingly, step S120 includes the following steps:

[0186] S128: Based on the distances between the target formation and each fault, calculate the influence of the distance between each fault and the pressure in the target formation using the following formula:

[0187]

[0188]

[0189] Where, ΔP Dxi The influence of the i-th fault on the formation pressure of the target stratum at spatial location x is represented by dis_DC. xi Let ci be the distance between the target stratum at spatial location x and the i-th fault, where ci is a constant and ci > 1, and ΔP Dx The combined effect of the distance between the target stratum and m faults at spatial location x on the formation pressure at spatial location x;

[0190] S129: Based on the time elapsed in the target stratum, calculate the influence of burial depth on the pressure in the target stratum using the following formula:

[0191] ΔP Tx =β*time x+ε;

[0192] Where, ΔP Tx The effect of burial depth at spatial location x on formation pressure, time x Let ε be the time elapsed at the target stratum at spatial location x, where β and ε are constants.

[0193] This can be understood as follows: based on the actual conditions of the target strata, the study area is divided into n different pressure systems according to structural units, taking into account the influence of distance and depth from faults on formation pressure. Within each pressure system, the distance and depth of each point on the plane from the fault are considered, and combined with the corresponding correction formula, formation pressure correction in complex structural areas is achieved.

[0194] The effect of fault distance on formation pressure can be expressed as a power function. The curve of the power function shows that the closer to the fault, the more significant the effect of the fault, which is consistent with geological laws.

[0195] Step S130: Based on the inversion results, construct a first pressure prediction model for undercompactment, a second pressure prediction model for hydrocarbon generation and pressurization, and a third pressure prediction model for tectonic compression corresponding to the target stratum.

[0196] Corresponding to the first pressure prediction model for undercompaction, the inversion results include a pre-stack depth migration velocity model for describing the correspondence between pre-stack depth migration velocity and spatial location.

[0197] At this point, correspondingly, based on the inversion results, a first pressure prediction model for the undercompaction cause of the target stratum is constructed, including the following steps:

[0198] Based on the pre-stack depth migration velocity model, the first pressure prediction model for the undercompacted formation corresponding to the target stratum is constructed using the following formula:

[0199]

[0200] Where P1(x) is the formation pressure at spatial location x due to undercompaction, FC(v) is the correction factor, v is Poisson's ratio, FC(v) is the positive function of Poisson's ratio v, Vmax and Vmin are the seismic wave velocities when porosity is close to zero and rigidity is close to zero, respectively. x P represents the pre-stack depth migration velocity at spatial location x. OV This refers to the pressure of the overlying strata.

[0201] Wherein, FC(v) is the factor of the improved Philippians pressure prediction model, considering that the stress and velocity sensitivity is related to lithology, and the Poisson's ratio v parameter is a comprehensive parameter for inverting the P-wave and S-wave moduli of lithology. Therefore, a parameter correction function FC(v) with the Poisson's ratio parameter is constructed to adjust the overlying formation pressure P. OV The stress effect on the rock skeleton. Typically, FC(v) is defined as a linear positive function of Poisson's ratio v; however, depending on the situation, it can also be defined as a nonlinear positive function. Overburden pressure P. OV It can be calculated based on the density and depth of the rock mass.

[0202] Vmax and Vmin are the seismic wave velocities when porosity is close to zero and rigidity is close to zero, respectively. The former approximates the matrix velocity, and the latter approximates the pore fluid velocity. x The pre-stack depth migration velocity at spatial location x can be directly obtained using the pre-stack depth migration velocity model described above.

[0203] Corresponding to the second pressure prediction model for hydrocarbon generation and pressurization, the inversion results include the actual P-wave velocity of the target formation and the formation P-wave velocity under normal compaction conditions.

[0204] Correspondingly, based on the inversion results, a second pressure prediction model for the hydrocarbon generation and pressurization genesis corresponding to the target formation is constructed, including the following steps:

[0205] Based on the P-wave velocity of the target formation and the P-wave velocity of the formation under normal compaction conditions, a second pressure prediction model for the hydrocarbon generation and pressurization genesis corresponding to the target formation is constructed as follows:

[0206] P2(x)=P OV -(P OV -P h (V) x / V nct ) c ;

[0207] Where P2(x) is the formation pressure at spatial location x due to hydrocarbon generation and pressurization, P OV For the overlying formation pressure, P h For hydrostatic pressure, V x V represents the actual P-wave velocity at spatial location x of the target formation. nct Let c be the longitudinal wave velocity of the target formation at spatial location x under normal compaction conditions, where c is a constant.

[0208] Corresponding to the third pressure prediction model based on tectonic compression, the inversion results include the influence of the distance from the fault on the target formation pressure and the influence of the burial depth on the target formation pressure;

[0209] Correspondingly, based on the inversion results, a third pressure prediction model of tectonic compressional origin corresponding to the target stratum is constructed, including the following steps:

[0210] Based on the influence of distance from the fault on the pressure of the target formation and the influence of burial depth on the pressure of the target formation, a third pressure prediction model corresponding to the tectonic compression of the target formation is constructed using the following formula:

[0211] P3(x)=a d *ΔP Dx +a t *ΔP Tx ;

[0212] Where P3(x) is the formation pressure caused by tectonic compression at spatial location x, ΔP Dx Let ΔP be the combined effect of the distance between point x (the point in space) and the fault on formation pressure. Tx To illustrate the effect of burial depth at spatial location x on formation pressure, a d and a t These are the correction coefficients for the distance between spatial location x and the fault, and the burial depth at spatial location x, respectively.

[0213] Step S140: The first pressure prediction model, the second pressure prediction model and the third pressure prediction model are fused using a fuzzy logic algorithm to obtain the pressure prediction result of the target formation.

[0214] Specifically, step S140 includes the following steps:

[0215] S141: Normalize the first pressure prediction model, the second pressure prediction model and the third pressure prediction model to obtain the normalized results of the first pressure prediction model, the second pressure prediction model and the third pressure prediction model.

[0216] S142: Based on the normalization result, construct the membership functions corresponding to the first pressure prediction model, the second pressure prediction model, and the third pressure prediction model;

[0217] S143: Based on the membership function, calculate the relative distance between the membership degrees of any two pressure prediction models among the first pressure prediction model, the second pressure prediction model, and the third pressure prediction model at any spatial location.

[0218] S144: Define a fuzzy proximity function such that at any spatial location, the greater the relative distance between the membership degrees of two pressure prediction models, the smaller their fuzzy proximity; conversely, the smaller the relative distance, the greater their fuzzy proximity.

[0219] S145: Using the fuzzy proximity function, obtain the comprehensive fuzzy proximity of the membership degree of each pressure prediction model to the membership degree of other pressure prediction models.

[0220] S146: Normalize the comprehensive fuzzy proximity of the membership degrees of each pressure prediction model to other pressure prediction models to obtain the relative weights of each pressure prediction model.

[0221] S147: Based on the relative weights of each pressure prediction model, the first pressure prediction model, the second pressure prediction model, and the third pressure prediction model are fused to obtain the pressure prediction result of the target formation.

[0222] Step S141 includes the following steps:

[0223] The first pressure prediction model, the second pressure prediction model, and the third pressure prediction model are normalized using the following formula to obtain the normalized results of the first pressure prediction model, the second pressure prediction model, and the third pressure prediction model:

[0224]

[0225] Among them, w i (x) represents the normalized result of the pressure prediction model at spatial location x.

[0226] Correspondingly, the membership function is:

[0227]

[0228] Where, μ i (x) represents the membership degree of the i-th pressure prediction model at spatial location x.

[0229] The relative distance between the membership degrees of any two pressure prediction models among the first, second, and third pressure prediction models is:

[0230] d ij (x)=|μ i (x)-μ j (x)|;

[0231] Where, d ij (x) represents the relative distance between the membership degrees of the i-th pressure prediction model and the j-th pressure prediction model at spatial location x, μ i (x) represents the membership degree of the i-th pressure prediction model at spatial location x, μ j (x) represents the membership degree of the j-th pressure prediction model at spatial location x. ijThe larger the value of d, the greater the difference between the i-th and j-th pressure prediction models at spatial location x, meaning the less mutual support exists between the two data points; conversely, the smaller the value of d, the less support exists between the two data points. ij The smaller the value, the greater the mutual support between the i-th pressure prediction model and the j-th pressure prediction model at spatial location x.

[0232] The fuzzy proximity function is:

[0233]

[0234] Among them, a ij (x) is the fuzzy proximity function between the i-th and j-th pressure prediction models at spatial location x, where max{d ij} represents the maximum relative distance between the membership degrees of the i-th pressure prediction model and the j-th pressure prediction model at each spatial location.

[0235] Correspondingly, step S145 includes the following steps:

[0236] S145a: Using the fuzzy proximity function, the fuzzy proximity between any two pressure prediction models at any spatial location is calculated, thus obtaining the fuzzy proximity matrix as follows:

[0237]

[0238] Where A(x) is the fuzzy proximity matrix;

[0239] S145b: Based on the fuzzy proximity matrix, calculate the comprehensive fuzzy proximity u between the membership degrees of each pressure prediction model and the membership degrees of other pressure prediction models. i Based on the probability source merging theory, a set of nonnegative numbers b1(x), b2(x), and b3(x) are required to satisfy the following conditions:

[0240] u i (x)=b1(x)a i1 (x)+b1(x)a i2 (x)+…+b3(x)a i3 (x), i = 1, 2, 3;

[0241] S145c: Introduce two eigenvectors U(x) and B(x), and let U(x) = [u1(x), u2(x), u3(x)]. T B(x) = [b1(x), b2(x), b3(x)] T Thus, the following matrix form is obtained:

[0242] U(x) = A(x)B(x);

[0243] S145d: Define a maximum fuzzy eigenvalue λ such that λB(x)=A(x)B(x), obtain the eigenvector B(x), and use the corresponding fuzzy proximity matrix as the comprehensive fuzzy proximity of the membership degree of each pressure prediction model to the membership degree of other pressure prediction models; where, the comprehensive fuzzy proximity of the membership degree of each pressure prediction model to the membership degree of other pressure prediction models is:

[0244] u i (x)=λb i (x)=b1(x)a i1 (x)+b1(x)a i2 (x)+…+b3(x)a i3 (x).

[0245] During the fusion process, different attributes need to be assigned different weights. Attributes with better stability and higher reliability have greater weights in the fusion. As discussed earlier, attributes with higher fuzzy proximity have higher reliability and stability, and therefore greater weights; conversely, attributes with lower fuzzy proximity have lower weights. Therefore, fuzzy proximity can be used to characterize the weights of each attribute.

[0246] Correspondingly, the relative weights of each pressure prediction model are:

[0247]

[0248] Among them, w i (x) represents the relative weight of the i-th pressure prediction model at spatial location x.

[0249] The pressure prediction result for the target formation is as follows:

[0250]

[0251] Wherein, P(x) is the predicted pressure of the target formation at spatial location x, P i (x) is the pressure prediction model at spatial location x.

[0252] This can be understood as treating the pressure prediction results under undercompaction, hydrocarbon generation-induced pressurization, and compressional tectonic activity as different attributes of the same geological target (pressure anomaly). The pressure prediction problem under complex genesis then transforms into a multi-attribute fusion problem. In the process of predicting pressure anomalies using different attributes, there is often no clear-cut boundary as to whether a single extracted attribute can indicate a pressure anomaly; that is, fuzziness exists. To describe and quantify this fuzzy problem, we propose a multi-attribute fusion method based on fuzzy logic. This method applies fuzzy theory to attribute fusion to obtain a comprehensive quantitative attribute that more accurately describes subsurface reservoir information than a single attribute. It uses the concept of "membership degree" to precisely characterize the relationship between elements and fuzzy sets, simulating rule-based reasoning by the human brain and resolving various uncertainties caused by the logical failure of the "law of excluded middle." When fusing the membership degrees obtained from various attributes, the fusion algorithm is entirely based on existing data, fully mining the implicit information within the data. The calculation of various attribute weights is entirely data-driven, reducing the subjectivity of human manipulation. By combining fuzzy logic with information fusion, problems with imprecise descriptions can be handled, and information can be adaptively merged, thereby improving the accuracy of stress prediction.

[0253] According to the above method, although the fusion result itself has lost the original clear physical meaning of each attribute parameter, it represents the common changes of multiple attribute parameters and has the advantages of various attributes, thereby reducing the ambiguity of single attribute prediction and finally realizing the prediction of complex genetic formation pressure based on the combination of multiple information.

[0254] This application provides a formation pressure prediction method, which includes acquiring well logging data of a target formation; performing seismic inversion based on the well logging data of the target formation to obtain inversion results; constructing a first pressure prediction model for undercompacted formation, a second pressure prediction model for hydrocarbon generation and pressurization, and a third pressure prediction model for tectonic compression corresponding to the target formation based on the inversion results; and fusing the first, second, and third pressure prediction models using a fuzzy logic algorithm to obtain the pressure prediction result of the target formation. This method achieves pressure prediction of complex formations based on multi-information fusion, improving the accuracy of pre-drilling formation pressure prediction under complex geological conditions.

[0255] Example 2

[0256] Based on Example 1 and Example 2, this example illustrates the method described in Example 1 through specific implementation cases.

[0257] In this embodiment, an unconventional shale gas reservoir under complex geological conditions in southern China was selected as the target fault.

[0258] First, according to the method in step S120 of Embodiment 1, the seismic layer velocity profile corresponding to the pre-stack depth migration velocity model is as follows: Figure 2 As shown.

[0259] Subsequently, according to the method in step S130 of Example 1, a first pressure prediction model for the undercompactment cause corresponding to the target stratum is obtained, and the pressure coefficient plane diagram corresponding to the pressure prediction model is as follows. Figure 3 As shown.

[0260] Subsequently, following the method for constructing the second pressure prediction model for hydrocarbon generation and pressurization in step S130 of Example 1, the velocity trends of multiple wells in the target area under normal compaction conditions were calculated, and a normal compaction velocity model was constructed based on the normal compaction trend velocities of Jiaoye 5, Jiaoye 7, and Jiaoye 8 wells. Then, the formation pressure considering hydrocarbon generation and pressurization conditions was calculated. Figure 4 The formation pressure coefficient profile of Well Jiaoye 8 shows that it is greater than that of Well Jiaoye 7, and the formation pressure coefficient of the entire Pingqiao anticline is higher than that of the Baima syncline. Furthermore, the vertical prediction accuracy is higher than that based solely on seismic layer velocities. Figure 5 This is a planar diagram of the formation pressure coefficient in the high-quality shale section of the Longmaxi-Wufeng Formation. The overall trend of higher pressure in the west and lower pressure in the east is basically consistent with the actual drilling results. However, it can also be seen from the diagram that the formation pressure difference does not show significant variation with structural features due to the interpolation algorithm. The influence of tectonic activity on formation pressure needs to be further considered.

[0261] The Jiaoshiba South Structural Zone is characterized by steep, banded faults, fractured structures, significant tectonic stress, and a complex pressure system. Considering the influence of tectonic structures on formation pressure, relative compressional tectonic stress and tectonic pressure factors are introduced. A formation pressure prediction model under compressional structures is established based on the method used in step S130 of Example 1 to construct the third pressure prediction model based on tectonic compression. The study area is divided into three different pressure systems in the plane, based on the Shimen No. 2 fault zone and the Heshunchang fault. Furthermore, the prediction results are structurally corrected by comprehensively considering factors such as distance from faults, burial depth, and structural morphology.

[0262] Figure 6 The results show the predicted formation pressure coefficient of the Guojiaoye 8 well. It can be seen that the formation pressure at the bottom of the Longmaxi Formation is significantly higher, reaching a maximum of 1.6, indicating an overpressured shale gas reservoir. The pressure coefficient decreases on both flanks of the anticline.

[0263] The pore pressure in layers 1-5 of the high-quality shale section is affected by burial depth, with relatively lower pressure in the uplifted area. Spatially, the pore pressure is vertically influenced by burial depth, with relatively lower pressure in the uplifted area; horizontally, it is also controlled by fault zones. Overall, the pressure is higher in the west and lower in the east, lower in the uplifted area and higher in the depression. The predicted pressure coefficients of layers 1-5 of the high-quality shale section after structural correction are shown in the figure. Figure 7 As shown, the pressure coefficient of the Pingqiao anticline is higher than that of the Baima syncline, and the trend of being higher in the west and lower in the east is highly consistent with the actual drilling results. The distribution of formation pressure coefficients with structural characteristics is also more reasonable.

[0264] by Figure 3 , Figure 5 and Figure 7 Based on the different methods and formation pressure prediction results represented by these methods, and according to the method in step S140 of Example 1, a multi-attribute fusion method based on fuzzy logic is used to achieve a comprehensive prediction of complex formation pressure in dual-complex tectonic zones. The prediction results are as follows: Figure 8 As shown, the error between the predicted results and the measured pressure data is less than 10%.

[0265] Example 3

[0266] Figure 9 Please refer to the structural schematic diagram of a formation pressure prediction device provided in this application embodiment. Figure 9 This embodiment provides a formation pressure prediction device 100, including an acquisition module 110, an inversion module 120, a model building module 130, and a fusion module 140.

[0267] Module 110 is used to acquire well logging data of the target formation;

[0268] The inversion module 120 is used to perform seismic inversion based on the well logging data of the target formation to obtain the inversion result;

[0269] The model building module 130 is used to build, based on the inversion results, a first pressure prediction model for undercompaction, a second pressure prediction model for hydrocarbon generation and pressurization, and a third pressure prediction model for tectonic compression corresponding to the target stratum.

[0270] The fusion module 140 is used to fuse the first pressure prediction model, the second pressure prediction model and the third pressure prediction model using a fuzzy logic algorithm to obtain the pressure prediction result of the target formation.

[0271] The acquisition module 110 acquires well logging data of the target formation; the inversion module 120 performs seismic inversion based on the well logging data of the target formation to obtain the inversion results; the model building module 130 constructs a first pressure prediction model for undercompactment, a second pressure prediction model for hydrocarbon generation and pressurization, and a third pressure prediction model for tectonic compression corresponding to the target formation based on the inversion results; the fusion module 140 uses a fuzzy logic algorithm to fuse the first pressure prediction model, the second pressure prediction model, and the third pressure prediction model to obtain the pressure prediction results of the target formation.

[0272] Specific implementation examples of the formation pressure prediction method based on the above modules have been detailed in Example 1 and will not be repeated here.

[0273] Example 4

[0274] This embodiment provides an electronic device, which may be a mobile phone, computer, or tablet computer, etc., including a memory and a processor. The memory stores a calculator program, which, when executed by the processor, implements the formation pressure prediction method as described in Embodiment 1. It is understood that the electronic device may also include an input / output (I / O) interface and communication components.

[0275] The processor is used to execute all or part of the steps in the formation pressure prediction method as described in Embodiment 1. The memory is used to store various types of data, which may include, for example, instructions for any application or method in the electronic device, as well as application-related data.

[0276] The processor may be implemented as an Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor, or other electronic components, and is used to execute the formation pressure prediction method in Embodiment 1 above.

[0277] The memory can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0278] Example 5

[0279] This embodiment also provides a computer-readable storage medium, such as flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, server, app store, etc., which stores a computer program. When the computer program is executed by a processor, it can implement the following method steps:

[0280] Step S110: Obtain well logging data for the target formation;

[0281] Step S120: Based on the well logging data of the target formation, perform seismic inversion to obtain the inversion results;

[0282] Step S130: Based on the inversion results, construct a first pressure prediction model for undercompaction, a second pressure prediction model for hydrocarbon generation and pressurization, and a third pressure prediction model for tectonic compression corresponding to the target stratum.

[0283] Step S140: The first pressure prediction model, the second pressure prediction model and the third pressure prediction model are fused using a fuzzy logic algorithm to obtain the pressure prediction result of the target formation.

[0284] For a detailed description of the above method steps, please refer to Example 1. This example will not be repeated here.

[0285] In summary, this application provides a formation pressure prediction method, apparatus, electronic device, and storage medium. The method includes acquiring well logging data of a target formation; performing seismic inversion based on the well logging data of the target formation to obtain inversion results; constructing a first pressure prediction model for undercompacted formation, a second pressure prediction model for hydrocarbon generation and pressurization, and a third pressure prediction model for tectonic compression corresponding to the target formation based on the inversion results; and fusing the first, second, and third pressure prediction models using a fuzzy logic algorithm to obtain the pressure prediction result of the target formation. This method achieves complex formation pressure prediction based on multi-information fusion, improving the accuracy of pre-drilling formation pressure prediction under complex geological conditions.

[0286] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0287] Although the embodiments disclosed in this application are as described above, the content is merely for the purpose of facilitating understanding of this application and is not intended to limit this application. Any person skilled in the art to which this application pertains may make any modifications and changes in the form and details of the implementation without departing from the spirit and scope disclosed in this application; however, the scope of patent protection of this application shall still be determined by the scope defined in the appended claims.

Claims

1. A method of predicting formation pressure, characterized by, The method comprises: obtaining well logging data of a target formation; performing seismic inversion according to the well logging data of the target formation to obtain an inversion result; constructing a first pressure prediction model of the target formation corresponding to an undercompaction cause, a second pressure prediction model of the target formation corresponding to a hydrocarbon generation and pressure increase cause, and a third pressure prediction model of the target formation corresponding to a tectonic extrusion cause according to the inversion result; fusing the first pressure prediction model, the second pressure prediction model, and the third pressure prediction model by using a fuzzy logic algorithm to obtain a pressure prediction result of the target formation; wherein the fusing of the first pressure prediction model, the second pressure prediction model, and the third pressure prediction model by using the fuzzy logic algorithm to obtain the pressure prediction result of the target formation comprises the following steps: normalizing the first pressure prediction model, the second pressure prediction model, and the third pressure prediction model to obtain normalized results of the first pressure prediction model, the second pressure prediction model, and the third pressure prediction model; constructing membership functions corresponding to the first pressure prediction model, the second pressure prediction model, and the third pressure prediction model according to the normalized results; calculating relative distances between membership degrees of any two pressure prediction models of the first pressure prediction model, the second pressure prediction model, and the third pressure prediction model at any spatial position according to the membership functions; defining a fuzzy closeness function such that the greater the relative distance between the membership degrees of any two pressure prediction models is, the smaller the fuzzy closeness between the two is; otherwise, the greater the fuzzy closeness between the two is; obtaining comprehensive fuzzy closeness between the membership degree of each pressure prediction model and the membership degrees of other pressure prediction models through the fuzzy closeness function; normalizing the comprehensive fuzzy closeness between the membership degree of each pressure prediction model and the membership degrees of other pressure prediction models to obtain relative weights of the pressure prediction models; fusing the first pressure prediction model, the second pressure prediction model, and the third pressure prediction model according to the relative weights of the pressure prediction models to obtain the pressure prediction result of the target formation; the inversion result comprises an influence of a distance from a fault on the pressure of the target formation and an influence of a burial depth on the pressure of the target formation; constructing the third pressure prediction model of the target formation corresponding to the tectonic extrusion cause according to the inversion result comprises the following steps: constructing the third pressure prediction model of the target formation corresponding to the tectonic extrusion cause according to the comprehensive influence of the distance from the fault on the pressure of the target formation and the influence of the burial depth on the pressure of the target formation by the following formula: ； wherein, the formation pressure caused by the tectonic extrusion at the spatial position x, the combined effect of the distance from the fault at the spatial position x and the burial depth at the spatial position x on the formation pressure, the effect of the burial depth at the spatial position x on the formation pressure, and are correction factors for the distance from the fault at the spatial position x and the burial depth at the spatial position x, respectively.

2. The method of claim 1, wherein, the inversion result comprises a prestack depth migration velocity model used for describing a corresponding relationship between a prestack depth migration velocity and a spatial position; constructing the first pressure prediction model of the target formation corresponding to the undercompaction cause according to the inversion result comprises the following steps: constructing the first pressure prediction model of the target formation corresponding to the undercompaction cause according to the prestack depth migration velocity model by the following formula: wherein, P(x) is the undercompacted formation pressure at spatial location x, is the correction factor, is the Poisson's ratio, , are the seismic wave velocities for porosity approaching zero and rigidity approaching zero, respectively, is the prestack depth-migrated velocity at spatial location x, is the overburden pressure.

3. The method of claim 2, wherein, performing seismic inversion according to the well logging data of the target formation to obtain an inversion result comprises the following steps: According to the well logging data of the target formation, a pre-stack time migration velocity model is constructed, and the pre-stack time migration velocity model is used as an initial velocity model of pre-stack depth migration; A CIP gather of the target formation is generated by using a Kirchhoff pre-stack depth migration method; Based on the CIP gather, a depth domain velocity disturbance is obtained by optimizing and iteratively solving the initial velocity model through a grid tomography equation, so as to obtain a final pre-stack depth migration velocity model.

4. The method of claim 1, wherein, The inversion result includes an actual P-wave velocity of the target formation and a P-wave velocity of the target formation under normal compaction; According to the inversion result, a second pressure prediction model corresponding to the hydrocarbon generation and pressure increase of the target formation is constructed, including the following steps: According to the P-wave velocity of the target formation and the P-wave velocity of the target formation under normal compaction, the second pressure prediction model corresponding to the hydrocarbon generation and pressure increase of the target formation is constructed as follows: ； wherein, P(x) is the formation pressure at spatial location x due to hydrocarbon generation pressurization, P(x) is the overburden pressure at spatial location x, P(x) is the hydrostatic pressure at spatial location x, V(x) is the actual P-wave velocity of the target formation at spatial location x, V(x) is the formation P-wave velocity of the target formation at spatial location x under normal compaction, and c is a constant.

5. The method of claim 4, wherein, According to the well logging data of the target formation, seismic inversion is performed to obtain an inversion result, including the following steps: According to the well logging data of the target formation, seismic inversion is performed to obtain an actual P-wave velocity of the target formation; According to the well logging data of the target formation, an elastic tensor of wet clay and an elastic tensor of a sandy mixture in the target formation are calculated; According to the elastic tensor of wet clay and the elastic tensor of the sandy mixture, an elastic tensor of equivalent shale in the target formation is calculated; The elastic tensor of equivalent shale in the target formation is converted into acoustic travel time to obtain a P-wave velocity of the target formation under normal compaction.

6. The method of claim 5, wherein, The well logging data includes total porosity of rocks, volume content of clay and wet clay porosity in the target formation, and types, volume content and elastic modulus of each sandy mixture component in the target formation; According to the well logging data of the target formation, an elastic tensor of wet clay and an elastic tensor of a sandy mixture in the target formation are calculated, including the following steps: According to the total porosity of rocks, the volume content of clay and the wet clay porosity in the target formation, the elastic tensor of wet clay in the target formation is calculated by the following formula: ； ； wherein, is the wet clay porosity of the target formation, and are the total porosity of the rock and the volume content of clay in the target formation, respectively, is the elastic tensor of pure clay, is the elastic tensor of the wet clay in the target formation, assuming that the clay has transversely isotropic symmetry, which can be characterized by five independent parameters, characterized by wherein , , , and are the five independent parameters, corresponding to the five independent parameters , , , and are respectively , , , , ; According to the types, volume content and elastic modulus of each sandy mixture component in the target formation, based on a Voight-Reuss-Hill model, the elastic modulus of the sandy mixture in the target formation is calculated by the following formula: ； ； ； wherein, is the volume content of the nth sand mixture component, N is the number of sand mixture components, is the elastic modulus of the nth sand mixture component, including the bulk modulus or shear modulus , is the upper limit of the equivalent rock's elastic modulus provided by the Voigt model, is the upper limit of the equivalent rock's elastic modulus provided by the Reuss model, is the arithmetic mean of the upper and lower limits of the equivalent rock's elastic modulus; According to the elastic modulus of the sandy mixture in the target formation, the elastic tensor of the sandy mixture in the target formation is calculated by the following formula: ； wherein, is the elastic tensor of the sandy mixture in the target formation.

7. The method of claim 5, wherein, According to the elastic tensor of wet clay and the elastic tensor of the sandy mixture in the target formation, an elastic tensor of equivalent shale in the target formation is calculated, including the following steps: According to the elastic tensor of wet clay and the elastic tensor of the sandy mixture in the target formation, the elastic tensor of equivalent shale in the target formation is calculated by the following formula: ； ； ； ； ； ； ； wherein, is the elastic tensor of the equivalent shale in the target formation, is the elastic tensor of the equivalent shale in the target formation, is the elastic stiffness component of the elastic tensor of the equivalent shale in the target formation in the i-th row and j-th column, is the elastic stiffness component of the elastic tensor of the corresponding wet clay, the elastic tensor of the sandy mixture, and the elastic tensor of the organic matter, the elastic stiffness component of the elastic tensor of the organic matter being a constant, and the angle brackets is the elastic stiffness component of the elastic tensor of the corresponding wet clay, the elastic tensor of the sandy mixture, and the elastic tensor of the organic matter, the elastic stiffness component of the elastic tensor of the organic matter being a constant, and the angle brackets 8. The method of claim 7, wherein, The elastic tensor of equivalent shale in the target formation is converted into acoustic travel time to obtain a P-wave velocity of the target formation under normal compaction, including the following steps: The elastic stiffness component of the elastic tensor of the equivalent shale in the target formation is converted into the formation P-wave velocity under normal compaction by the following formula: ； wherein, Vn(x) is the normal compaction velocity of the target formation at the spatial location x, and p is the rock density.

9. The method of claim 1, wherein, The logging data includes the distance of the target formation from each fault and the elapsed time of the target formation; According to the logging data of the target formation, seismic inversion is performed to obtain an inversion result, including the following steps: The distance of the target formation from each fault is used to calculate the influence of the distance of each fault on the pressure of the target formation by the following formula: ； ； wherein, is the distance between the target formation at spatial position x and the i-th fault, is the influence of the i-th fault on the formation pressure of the target formation at spatial position x, is the distance between the target formation at spatial position x and the i-th fault, is a constant, ci>1, is the comprehensive influence of the distance between the target formation at spatial position x and the i-th fault on the formation pressure. The elapsed time of the target formation is used to calculate the influence of the burial depth on the pressure of the target formation by the following formula: ； wherein, is the transit time of the target formation at spatial location x, and β and are constants.

10. The method of claim 1, wherein, The first pressure prediction model, the second pressure prediction model, and the third pressure prediction model are normalized to obtain normalized results of the first pressure prediction model, the second pressure prediction model, and the third pressure prediction model, including the following steps: The first pressure prediction model, the second pressure prediction model, and the third pressure prediction model are normalized by the following formula to obtain normalized results of the first pressure prediction model, the second pressure prediction model, and the third pressure prediction model: ； wherein, is the first derivative of the pressure prediction model at spatial position x at time t. is the normalized result of the pressure prediction model.

11. The method of claim 10, wherein, The membership function is: wherein, is the pressure prediction model's membership at spatial location xpoint. is the pressure prediction model's membership at spatial location xpoint.

12. The method of claim 11, wherein, The relative distance between the membership degrees of any two of the first pressure prediction model, the second pressure prediction model, and the third pressure prediction model is: ； wherein, is the membership of the pressure prediction model at spatial location xpoint for the is the membership of the pressure prediction model at spatial location xpoint for the is the relative distance between the membership of the pressure prediction model and the is the membership of the pressure prediction model at spatial location xpoint for the is the membership of the pressure prediction model at spatial location xpoint for the is the membership of the pressure prediction model at spatial location xpoint for the is the membership of the pressure prediction model at spatial location xpoint for the 13. The method of claim 12, wherein, The fuzzy closeness function is: ； wherein, is the pressure prediction model at the spatial location xpoint, is the pressure prediction model at the spatial location xpoint, is the fuzzy closeness function corresponding to the pressure prediction model, is the membership of the pressure prediction model at the spatial location xpoint, is the membership of the pressure prediction model at the spatial location xpoint, is the maximum value of the relative distance between the membership of the pressure prediction model and the membership of the pressure prediction model.

14. The method of claim 13, wherein, The comprehensive fuzzy closeness between the membership degree of each pressure prediction model and the membership degrees of other pressure prediction models is obtained by the fuzzy closeness function, including the following steps: The fuzzy closeness between any two pressure prediction models at any spatial position is obtained by the fuzzy closeness function, so as to obtain a fuzzy closeness matrix: ； wherein, is the fuzzy closeness matrix; According to the fuzzy closeness matrix, a membership degree of each pressure prediction model and a comprehensive fuzzy closeness degree of the membership degrees of other pressure prediction models are obtained , and based on the probability source combination theory, a set of non-negative numbers , , satisfy the following conditions: Introducing two eigenvectors and , let , , so that the following matrix form is obtained: ； define a maximum fuzzy feature value λ, so that , find the eigenvector , and the corresponding fuzzy closeness matrix is taken as the comprehensive fuzzy closeness degree of the membership degree of each pressure prediction model and the membership degree of other pressure prediction models; wherein the comprehensive fuzzy closeness degree of the membership degree of each pressure prediction model and the membership degree of other pressure prediction models is 。 15. The method of claim 14, wherein, The relative weight of each pressure prediction model is: wherein, is the relative weight of the pressure prediction model at spatial location xpoint. is the relative weight of the pressure prediction model at spatial location xpoint.

16. The method of claim 15, wherein, The pressure prediction result of the target formation is: ； wherein, is a pressure prediction result for the target formation at spatial location x, is a pressure prediction result for the target formation at spatial location x, is a pressure prediction result for the target formation at spatial location x, 17. A formation pressure prediction apparatus for implementing the method of any one of claims 1 to 16, characterized by, The device comprises: An acquisition module configured to acquire logging data of a target formation; An inversion module configured to perform seismic inversion according to the logging data of the target formation to obtain an inversion result; A model construction module configured to construct a first pressure prediction model of the target formation corresponding to an undercompaction cause, a second pressure prediction model of the target formation corresponding to a hydrocarbon generation and pressure increase cause, and a third pressure prediction model of the target formation corresponding to a tectonic extrusion cause according to the inversion result; A fusion module configured to fuse the first pressure prediction model, the second pressure prediction model, and the third pressure prediction model by using a fuzzy logic algorithm to obtain a pressure prediction result of the target formation.

18. An electronic device, comprising: A memory and a processor, wherein the memory stores a computer program, and the computer program is executed by the processor to perform the formation pressure prediction method according to any one of claims 1 to 16.

19. A storage medium, characterized by The computer program stored in the storage medium can be executed by one or more processors, and can be used to implement the formation pressure prediction method according to any one of claims 1 to 16.

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