A method and apparatus for predicting seismic activity in shale reservoirs with a double sweet spot

Abstract

This invention discloses a seismic prediction method and apparatus for shale reservoir double-sweet spots. The method includes: obtaining first data of a first parameter indicating geological conditions and second data of a second parameter indicating engineering conditions for the target stratum based on pre-stack and post-stack inversion of seismic data from the study area; obtaining double-sweet spot evaluation parameter data based on the weighting coefficients of the first and second parameters and the first and second data; and performing double-sweet spot prediction for the target stratum based on the double-sweet spot evaluation parameter data and a pre-determined double-sweet spot evaluation standard. This method effectively utilizes multi-parameter data from both pre-stack and post-stack layers to conduct seismic prediction and comprehensive evaluation of shale gas double-sweet spots, effectively improving the prediction accuracy of deep shale gas reservoirs, providing a basis for well location deployment in shale gas exploration and development, and thus significantly reducing the risks of unconventional reservoir drilling.

Description

Technical Field

[0001] This invention relates to the field of petroleum geophysical exploration technology, and in particular to a method and apparatus for seismic prediction of double sweet spots in shale reservoirs. Background Technology

[0002] Shale and mudstone possess significant oil and gas exploration potential. However, in shale gas exploration and development, the low initial production capacity in many areas hinders economic efficiency. Therefore, the application and breakthroughs in shale gas "sweet spot" exploration technology are crucial for achieving economical extraction and further improving shale gas production capacity. In the early stages of shale gas exploration and development, limited geological data and low drilling levels make seismic prediction and comprehensive evaluation of shale gas "sweet spots" using pre- and post-stack seismic data critical. Currently, shale gas exploration and development in my country is still in its initial stages, with insufficient theoretical research, weak extraction technology, and a lack of systematic seismic evaluation and prediction systems for shale gas "sweet spots." Summary of the Invention

[0003] In view of the above problems, the present invention is proposed to provide a method and apparatus for seismic prediction of double sweet spots in shale reservoirs that overcomes or at least partially solves the above problems, and can effectively carry out seismic prediction and comprehensive evaluation of shale gas "double sweet spots".

[0004] In a first aspect, embodiments of the present invention provide a method for seismic prediction of shale reservoirs with a double sweet spot, comprising:

[0005] Based on pre-stack and post-stack inversion of seismic data in the study area, the first data of the first parameter indicating the geological conditions of the target segment and the second data of the second parameter indicating the engineering conditions are obtained. The geological conditions reflect the hydrocarbon generation capacity and reservoir conditions, and the engineering conditions reflect the mining difficulty.

[0006] Based on the weight coefficients of the first parameter and the second parameter, as well as the first data and the second data, the double dessert evaluation parameter data is obtained;

[0007] Based on the double sweet spot evaluation parameter data and the predetermined double sweet spot evaluation criteria, double sweet spot prediction is performed for the target layer.

[0008] Secondly, embodiments of the present invention provide a shale reservoir double sweet spot seismic prediction device, comprising:

[0009] The sensitive parameter data acquisition module is used to obtain the first data of the first parameter indicating the geological conditions of the target segment and the second data of the second parameter indicating the engineering conditions based on the pre-stack and post-stack inversion of the seismic data of the study area. The geological conditions reflect the hydrocarbon generation capacity and reservoir conditions, and the engineering conditions reflect the mining difficulty.

[0010] The double dessert evaluation parameter data acquisition module is used to obtain double dessert evaluation parameter data based on the weight coefficients of the first parameter and the second parameter, as well as the first data and the second data.

[0011] The double sweet spot prediction module is used to predict the double sweet spot of the target layer based on the double sweet spot evaluation parameter data and the predetermined double sweet spot evaluation criteria.

[0012] Thirdly, embodiments of the present invention provide a computer program product, including a computer program / instruction, wherein the computer program / instruction, when executed by a processor, implements the above-mentioned shale reservoir double sweet spot seismic prediction method.

[0013] Fourthly, this disclosure provides a server, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described shale reservoir double-sweet spot seismic prediction method.

[0014] The beneficial effects of the above-described technical solutions provided in the embodiments of the present invention include at least the following:

[0015] The shale reservoir double sweet spot seismic prediction method provided in this invention can effectively utilize post-stack and pre-stack multi-parameter data to carry out shale gas "double sweet spot" seismic prediction and comprehensive evaluation, effectively improve the prediction accuracy of deep shale gas reservoirs, provide a basis for shale gas exploration and development well location deployment, and thus significantly reduce the risk of unconventional reservoir drilling.

[0016] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings.

[0017] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0018] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0019] Figure 1 This is a flowchart of the shale reservoir double sweet spot seismic prediction method in Embodiment 1 of the present invention;

[0020] Figure 2 This is a flowchart illustrating the specific implementation of the shale gas double-sweet spot earthquake prediction method in Embodiment 2 of the present invention;

[0021] Figure 3This is a plan view of the total organic carbon content in Embodiment 2 of the present invention;

[0022] Figure 4 This is a porosity planar diagram in Embodiment 2 of the present invention;

[0023] Figure 5 This is a plan view of the mudstone and shale thickness in Embodiment 2 of the present invention;

[0024] Figure 6 This is a planar diagram of the brittleness index in Embodiment 2 of the present invention;

[0025] Figure 7 This is a pore pressure plan view in Embodiment 2 of the present invention;

[0026] Figure 8 This is a planar diagram of crack density in Embodiment 2 of the present invention;

[0027] Figure 9 This is a comprehensive evaluation diagram of the two desserts in Embodiment 2 of the present invention;

[0028] Figure 10 This is a schematic diagram of the structure of the shale reservoir double sweet spot seismic prediction device in an embodiment of the present invention. Detailed Implementation

[0029] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0030] It should be understood that the terminology used herein is merely for describing particular embodiments and is not intended to limit the invention. Unless otherwise stated, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. While only preferred methods and materials have been described herein, any methods and materials similar or equivalent to those described herein may be used in the implementation or testing of this invention. All references to this specification are incorporated by way of citation to disclose and describe methods and / or materials associated with those references. In the event of any conflict with any incorporated reference, the content of this specification shall prevail.

[0031] To address the problem of existing technologies being unable to perform seismic prediction of shale gas "sweet spots," this invention provides a method and apparatus for seismic prediction of shale reservoirs with double sweet spots. This method effectively utilizes post-stack and pre-stack multi-parameter data to conduct seismic prediction and comprehensive evaluation of shale gas "double sweet spots."

[0032] Based on rock physical analysis, this invention explores and establishes a method for predicting and comprehensively evaluating the "double sweet spot" seismic activity of shale, which is applicable to the double sweet spot prediction of shale gas and shale oil.

[0033] Example 1

[0034] Embodiment 1 of the present invention provides a method for seismic prediction of shale reservoirs with double sweet spots, the process of which is as follows: Figure 1 As shown, it includes the following steps:

[0035] Step S11: Based on the pre-stack and post-stack inversion of seismic data in the study area, obtain the first data of the first parameter indicating the geological conditions of the target segment and the second data of the second parameter indicating the engineering conditions.

[0036] Geological conditions reflect hydrocarbon generation capacity and storage conditions, so the first parameter is used to evaluate the hydrocarbon generation capacity and storage capacity of the reservoir, and is the geological sweet spot evaluation parameter; engineering conditions reflect the difficulty of extraction, so the second parameter is used to evaluate the ease of oil and gas extraction from the reservoir, and is the engineering sweet spot evaluation parameter.

[0037] In some embodiments, before performing step S11, multiple sensitive parameters of shale reservoir oil and gas resources can be determined through rock physics analysis; post-stack and pre-stack inversion of seismic data in the study area can be carried out to obtain the data volume corresponding to each sensitive parameter.

[0038] Specifically, it could be at least one of the following sensitive parameters for determining shale reservoir oil and gas resources:

[0039] Longitudinal wave velocity, transverse wave velocity, longitudinal wave impedance, transverse wave impedance, density, Poisson's ratio, Young's modulus, Lamé impedance, and shear modulus.

[0040] The aforementioned sensitive elastic parameters can effectively reflect lithology and / or hydrocarbon content. Therefore, through pre-stack and post-stack seismic data inversion, the P-wave velocity volume, S-wave velocity volume, P-wave impedance volume, S-wave impedance volume, density volume, Poisson's ratio volume, Young's modulus volume, Lamé impedance volume, and shear modulus volume of the study area were obtained, providing a data foundation for subsequent steps.

[0041] In some embodiments, obtaining first data of a first parameter indicating the geological conditions of a target stratum may include obtaining at least one of the following first data of the first parameter indicating the geological conditions of the target stratum:

[0042] Data on total organic carbon content, porosity, and shale thickness.

[0043] Obtaining second data for the second parameter indicating the engineering conditions of the target layer may include obtaining at least one of the following second data for the second parameter indicating the engineering conditions of the target layer:

[0044] Brittleness index data, pore pressure data, and crack density data.

[0045] The first and second data mentioned above can specifically be the planar distribution data of the target layer segment. The specific method for determining them will be described in detail later.

[0046] Step S12: Obtain the double dessert evaluation parameter data based on the weight coefficients of the first parameter and the second parameter, as well as the first data and the second data.

[0047] Taking the first parameter, which includes total organic carbon content T, porosity Ф, and shale thickness H, and the second parameter, which includes brittleness index B, pore pressure P, and fracture density F, as an example, the evaluation parameters for the double sweet spot can be determined by the following formula (1):

[0048] COMP=c1T+c2φ+c3H+c4B+c5P+c6F (1)

[0049] In formula (1), COMP is the double sweet spot evaluation parameter, i.e., the comprehensive evaluation index; the weight coefficients of T, Ф, H, B, P and F are c1, c2, c3, c4, c5 and c6 respectively, and c1, c2, c3, c4, c5 and c6 are regional experience values, and their sum is 1.

[0050] Furthermore, T, Ф, H, B, P, and F can be normalized values.

[0051] Step S13: Based on the double sweet spot evaluation parameter data and the predetermined double sweet spot evaluation criteria, predict the double sweet spot of the target layer.

[0052] For example, the double sweet spot level corresponding to the range of double sweet spot evaluation parameter data in the study area can be determined in advance, and the distribution of double sweet spot level in the target layer can be determined according to the double sweet spot evaluation parameter data.

[0053] The shale reservoir double sweet spot seismic prediction method provided in Embodiment 1 of this invention can effectively utilize post-stack and pre-stack multi-parameter data to carry out shale gas "double sweet spot" seismic prediction and comprehensive evaluation, effectively improve the prediction accuracy of deep shale gas reservoirs, provide a basis for shale gas exploration and development well location deployment, and thus significantly reduce the risk of unconventional reservoir drilling.

[0054] The determination of the first and second data of the aforementioned target layer segment can be performed according to the following steps:

[0055] 1. Total organic carbon content data

[0056] (1) Using the superposition method of resistivity and sonic transit time curves, the total organic carbon curve of wells in the study area was obtained by calibrating the total organic carbon samples.

[0057] The total organic carbon sample point can be a sample point with the measured total organic carbon content.

[0058] By overlaying resistivity and sonic transit time curves, the total organic carbon curve of the wells in the study area can be obtained using the following formulas (2) and (3):

[0059] ΔLgR=Lg(R / R 基线 )+k1×(Δt-Δt 基线 (2)

[0060] TOC = ΔLgR × 10 (2.297-0.1688LOM) (3)

[0061] In formulas (2) and (3), R is resistivity, Δt is the sound wave transit time, and R 基线 , Δt 基线 These are resistivity, acoustic transit time baseline (based on relatively pure lithology other than shale, such as sandstone, carbonate rock, etc.), LOM is an index reflecting the maturity of organic matter, k1 is the proportional coefficient of the miscibility scale, and the specific values ​​of LOM and k1 can be determined empirically, and TOC is the total organic carbon content.

[0062] (2) Using the total organic carbon curve as a constraint, the total organic carbon content is obtained by inverting the seismic waveform indication of the post-stack seismic data.

[0063] The total organic carbon content was obtained as a high-resolution total organic carbon content.

[0064] (3) Obtain the total organic carbon content data of the target layer to indicate the geological conditions through the total organic carbon content volume.

[0065] This may include determining the time window range of the target layer, extracting the data volume within that time window from the total organic carbon content volume, and determining the planar distribution data of the total organic carbon content of the target layer using an averaging method.

[0066] 2. Porosity data

[0067] (1) Using the porosity curves of wells in the study area as constraints, the porosity volume is obtained by inverting the seismic waveform indicators of the post-stack seismic data.

[0068] The porosity curve can be obtained from well logging interpretation, and the interpretation process can be constrained by measured porosity samples.

[0069] (2) Porosity data indicating the geological conditions of the target layer are obtained through porosity volume.

[0070] This may include determining the time window range of the target layer, extracting the data volume within that time window from the porosity volume, and determining the porosity planar distribution data of the target layer using an averaging method.

[0071] 3. Shale thickness data

[0072] (1) Based on rock physics analysis, the shear wave impedance threshold value of shale in the study area was obtained.

[0073] (2) The time thickness data of the shale in the target layer is obtained by using the transverse wave impedance body of the study area and the threshold value.

[0074] Specifically, by using the shale shear wave impedance threshold value, the number of sample points in the target layer that are below this threshold value is counted, and the shale time thickness can be obtained by multiplying the number of sample points by the sampling time interval.

[0075] (3) Based on the shale time thickness data and P-wave velocity volume, obtain the shale thickness data indicating the geological conditions of the target layer.

[0076] One approach is to determine the time window range of the target layer, extract the data volume within that time window from the P-wave velocity volume, and determine the P-wave velocity plane distribution data of the target layer using the averaging method; then, multiply the P-wave velocity plane distribution data with the shale time thickness data to determine the shale thickness data.

[0077] Furthermore, by using the total organic carbon content data, porosity data, and shale thickness data determined above, geological sweet spot evaluation parameters can be determined, and geological sweet spots in the target stratum can be predicted and evaluated.

[0078] 4. Brittleness index data

[0079] (1) Based on the brittleness index samples, Young's modulus volume, shear resistance volume, Lamé resistance volume and Poisson's ratio volume in the study area, determine the fitting relationship between the brittleness index and Young's modulus, shear resistance, Lamé resistance and Poisson's ratio.

[0080] Obtain the measured brittleness index samples.

[0081] A linear relationship is established between the measured brittleness index and Young's modulus, shear resistance, Lamé resistance, and Poisson's ratio, specifically as formula (4):

[0082] BI=a1ΔE+a2Δμρ+a3Δσ+a4Δλρ+a5 (4)

[0083] In formula (4), BI is the brittleness index, ΔE is Young's modulus, Δμρ is the shear resistance, Δλρ is the Lamé resistance, Δσ is Poisson's ratio, and a1, a2, a3, a4, and a5 are the linear coefficients to be fitted.

[0084] After determining the specific values ​​of a1, a2, a3, a4, and a5, substituting them into the above formula (4) will yield the fitting relationship between the brittleness index and Young's modulus, shear resistance, Lamé resistance, and Poisson's ratio.

[0085] (2) Based on the fitting relationship and Young's modulus body, shear resistance body, Lamé resistance body and Poisson's ratio body, the brittleness index body is obtained.

[0086] (3) Obtain the brittleness index data of the target layer indicating the engineering conditions through the brittleness index body.

[0087] This may include determining the time window range of the target layer, extracting the data volume within that time window from the brittleness index volume, and determining the planar distribution data of the brittleness index of the target layer using an averaging method.

[0088] 5. Pore pressure data

[0089] (1) Based on the longitudinal wave impedance volume, transverse wave impedance volume, and density volume of the study area, the pore pressure volume is obtained through the following formula (5):

[0090]

[0091] In formula (5), P p P is the pore pressure. ov For overlying pressure, P imp For the longitudinal wave impedance, S imp Let ρ be the transverse wave impedance, ρ be the density, H be the depth, and b1, b2, b3, and b4 be empirical constants for the study area.

[0092] Overhead pressure P ov It can be obtained based on the depth and the density of the overlying rock layers.

[0093] (2) Obtain pore pressure data indicating the engineering conditions of the target section through pore pressure body.

[0094] This may include determining the time window range of the target layer, extracting the data volume within that time window from the pore pressure volume, and determining the pore pressure plane distribution data of the target layer using an averaging method.

[0095] 6. Crack density data

[0096] (1) Crack density volume is obtained based on the azimuth data volume of the study area.

[0097] Based on azimuth-stacked seismic data, anisotropic inversion can be performed to obtain fracture density volume and azimuth volume.

[0098] (2) Crack density data indicating the engineering conditions of the target layer are obtained through crack density volume.

[0099] This may include determining the time window range of the target layer, extracting the data volume within that time window from the fracture density volume, and determining the plane distribution data of the fracture density of the target layer using an averaging method.

[0100] Furthermore, by using the brittleness index data, pore pressure data, and crack density data determined above, the engineering sweet spot evaluation parameters can be determined, and the engineering sweet spot of the target layer can be predicted and evaluated.

[0101] Example 2

[0102] The shale reservoir double-sweet spot seismic prediction method provided in Embodiment 1 of this invention is applicable to double-sweet spot seismic prediction of shale gas and shale oil. Embodiment 2 of this invention provides a specific implementation process for the shale gas double-sweet spot seismic prediction method. Taking the Ordovician shale reservoir in the xx area as an example, the Ordovician reservoir in the study area is mainly marine sedimentary. Based on paleontological characteristics, sedimentary cycles, and regional marker beds, its strata can be divided into five lithological sections from bottom to top: the Sandaokan Formation, the Zhuozishan Formation, the Kelimoli Formation, the Wulalik Formation, and the Lashizhong Formation. Among them, the Wulalik Formation argillaceous carbonate reservoir is highly heterogeneous with very complex pores, making seismic prediction and comprehensive evaluation of the shale "double sweet spot" difficult. Embodiment 2 of this invention provides a shale gas "double sweet spot" seismic prediction and comprehensive evaluation method based on post-stack and pre-stack multi-parameter data, obtaining shale "double sweet spot" seismic prediction results and comprehensive evaluation results. See [link to documentation]. Figure 2 As shown, the main steps are as follows:

[0103] Step S21: Perform pre-stack and post-stack seismic inversions to obtain P-wave velocity, S-wave velocity, P-wave impedance, S-wave impedance, density, Poisson's ratio, Young's modulus, Lamé impedance, and shear modulus data volumes.

[0104] Step S22: Using post-stack seismic data and shear wave impedance data obtained from pre-stack and post-stack inversions, obtain data on total organic carbon content, porosity, and mudstone and shale thickness of the Uralik Formation.

[0105] like Figure 3 Plan view of total organic carbon content in the target section, Uralik Formation. Figure 4 Porosity planar diagram Figure 5 This is a plan view of the thickness of mudstone and shale.

[0106] Step S23: Using the azimuth seismic data and the Young's modulus, shear impedance, Lamé impedance, Poisson's ratio, P-wave impedance, S-wave impedance and density volume data obtained from pre-stack and post-stack inversions, the brittleness index, pore pressure and fracture density data are obtained.

[0107] Among them, the coefficients in formula (4) for calculating the brittleness index are a1 = 122.5, a2 = 17.85, a3 = 47.63, a4 = 25.4, and a5 = -13.66.

[0108] like Figure 6 A brittleness index planar diagram of the target stratigraphic section, the Uralik Formation. Figure 7Pore ​​pressure plan view Figure 8 This is a planar diagram showing the crack density.

[0109] Step S24: Based on the total organic carbon content data, porosity data, shale thickness data, brittleness index data, pore pressure data, and fracture density data, obtain the double sweet spot evaluation parameter data.

[0110] In the above formula (1), we take c1 = 0.5, c2 = 0.1, c3 = 0.1, c4 = 0.1, c5 = 0.1, and c6 = 0.1 respectively. We use formula (1) to obtain the evaluation parameter data of the double dessert by weighting with variable coefficients.

[0111] Step S25: Obtain a comprehensive evaluation chart of the two desserts from the evaluation parameter data of the two desserts and the pre-determined evaluation criteria of the two desserts.

[0112] like Figure 9 As shown, the target segment, the Uralik Formation, is divided into two double sweet spot zones: Zone I and Zone II.

[0113] Based on the inventive concept of this invention, embodiments of this invention also provide a shale reservoir double sweet spot seismic prediction device, the structure of which is as follows: Figure 10 As shown, it includes:

[0114] Sensitive parameter data acquisition module 101 is used to obtain first data of first parameters indicating geological conditions of the target segment and second data of second parameters indicating engineering conditions based on pre-stack and post-stack inversion of seismic data of the study area. The geological conditions reflect hydrocarbon generation capacity and reservoir conditions, and the engineering conditions reflect mining difficulty.

[0115] The double dessert evaluation parameter data acquisition module 102 is used to obtain double dessert evaluation parameter data based on the weight coefficients of the first parameter and the second parameter, as well as the first data and the second data.

[0116] The double sweet spot prediction module 103 is used to predict the double sweet spot of the target layer based on the double sweet spot evaluation parameter data and the predetermined double sweet spot evaluation criteria.

[0117] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0118] Based on the inventive concept of the present invention, embodiments of the present invention also provide a computer program product, including a computer program / instruction, wherein the computer program / instruction, when executed by a processor, implements the above-mentioned shale reservoir double sweet spot seismic prediction method.

[0119] Based on the inventive concept of this invention, this disclosure also provides a server, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-mentioned shale reservoir double sweet spot seismic prediction method.

[0120] Unless otherwise specifically stated, terms such as processing, calculation, operation, determination, display, etc., may refer to the actions and / or processes of one or more processing or computing systems or similar devices that represent the manipulation and conversion of data representing physical (e.g., electronic) quantities within the registers or memory of the processing system into other data similarly representing physical quantities within the memory, registers, or other such information storage, transmission, or display devices of the processing system. Information and signals can be represented using any of a variety of different techniques and methods. For example, data, instructions, commands, information, signals, bits, symbols, and chips mentioned throughout the above description can be represented by voltage, current, electromagnetic waves, magnetic fields or particles, light fields or particles, or any combination thereof.

[0121] It should be understood that the specific order or hierarchy of steps in the disclosed process is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in the process may be rearranged without departing from the scope of this disclosure. The appended method claims provide elements of various steps in an exemplary order and are not intended to limit the scope to the specific order or hierarchy described.

[0122] In the detailed description above, various features are combined together in a single embodiment to simplify this disclosure. This approach to disclosure should not be construed as reflecting an intention that embodiments of the claimed subject matter require more features than are explicitly stated in each claim. Rather, as reflected in the appended claims, the invention is presented with fewer features than all of the features in a single disclosed embodiment. Therefore, the appended claims are hereby explicitly incorporated into the detailed description, with each claim representing a separate preferred embodiment of the invention.

[0123] Those skilled in the art will also understand that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments herein can be implemented as electronic hardware, computer software, or a combination thereof. To clearly illustrate the interchangeability between hardware and software, the various illustrative components, blocks, modules, circuits, and steps described above are generally described in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. Those skilled in the art can implement the described functionality in alternative ways for each specific application; however, such implementation decisions should not be construed as departing from the scope of this disclosure.

[0124] The steps of the methods or algorithms described in conjunction with the embodiments herein can be directly embodied in hardware, software modules executed by a processor, or a combination thereof. The software modules can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium well known in the art. An exemplary storage medium is connected to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. The ASIC can reside in a user terminal. Alternatively, the processor and storage medium can exist as discrete components in the user terminal.

[0125] For software implementation, the techniques described in this application can be implemented using modules (e.g., procedures, functions, etc.) that perform the functions described in this application. This software code can be stored in memory units and executed by a processor. The memory units can be implemented within the processor or outside the processor; in the latter case, they are communicatively coupled to the processor via various means, as is well known in the art.

[0126] The foregoing description includes examples of one or more embodiments. It is certainly impossible to describe all possible combinations of components or methods in order to describe the above embodiments, but those skilled in the art will recognize that further combinations and arrangements of the various embodiments are possible. Therefore, the embodiments described herein are intended to cover all such changes, modifications, and variations that fall within the scope of the appended claims. Furthermore, the term “comprising” as used in the specification or claims is interpreted in a manner similar to the term “including,” as it is understood when used as a conjunction in the claims. Additionally, the use of any term “or” in the specification of the claims is intended to mean “non-exclusive or.” The terms “first” and “second” are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

Claims

1. A method for seismic prediction of shale reservoirs with a double sweet spot, characterized in that, include: Based on pre-stack and post-stack inversions of seismic data from the study area, P-wave velocity, S-wave velocity, P-wave impedance, S-wave impedance, density, Poisson's ratio, Young's modulus, Lamé impedance, and shear modulus data volumes are obtained. Using post-stack seismic data and the S-wave impedance data volumes obtained from pre-stack and post-stack inversions, total organic carbon content, porosity, and shale thickness data are obtained. Using azimuth-seismic data and the Young's modulus, shear impedance, Lamé impedance, Poisson's ratio, P-wave impedance, S-wave impedance, and density data volumes obtained from pre-stack and post-stack inversions, brittleness index, pore pressure, and fracture density data are obtained. At least one of the following first data points indicating the geological conditions of the target section is obtained: total organic carbon content, porosity, and shale thickness data. At least one of the following second data points indicating the engineering conditions is obtained: brittleness index, pore pressure, and fracture density data. The geological conditions reflect hydrocarbon generation capacity and reservoir conditions, and the engineering conditions reflect the difficulty of extraction. Based on the weighting coefficients of the first parameter and the second parameter, and the first data and the second data, the double sweet spot evaluation parameter data are determined by the following formula: COMP=c1T+c2Ф+c3H+c4B+c5P+c6F, where COMP is the double sweet spot evaluation parameter, T is the total organic carbon content, Ф is the porosity, H is the mudstone and shale thickness, B is the brittleness index, P is the pore pressure, F is the fracture density, and the weighting coefficients of T, Ф, H, B, P and F are c1, c2, c3, c4, c5 and c6, respectively, and the sum of c1, c2, c3, c4, c5 and c6 is 1; Based on the double sweet spot evaluation parameter data and the predetermined double sweet spot evaluation criteria, double sweet spot prediction is performed for the target layer segment; Specifically, the pore pressure data indicating the engineering conditions of the target layer are obtained, including: based on the longitudinal wave impedance volume, transverse wave impedance volume, and density volume of the study area, the pore pressure volume is obtained through the following formula: ； in, Pore ​​pressure, For the pressure of the overlying, For longitudinal wave impedance, For transverse wave impedance, Let h be the density, b1 be the depth, and b2 be the empirical constants of the study area. The pore pressure data indicating the engineering conditions of the target layer are obtained through the pore pressure body.

2. The method as described in claim 1, characterized in that, The total organic carbon content data indicating the geological conditions of the target stratigraphic section were obtained, specifically including: Using the superposition method of resistivity and sonic transit time curves, the total organic carbon curve of the wells in the study area was obtained by calibrating the total organic carbon samples. Using the total organic carbon curve as a constraint, the total organic carbon content volume is obtained by inverting the seismic waveform indication from the post-stack seismic data. The total organic carbon content data indicating the geological conditions of the target stratum are obtained through the total organic carbon content volume.

3. The method as described in claim 1, characterized in that, The porosity data indicating the geological conditions of the target stratum were obtained, specifically including: Using the porosity curves of wells in the study area as constraints, porosity volumes are obtained by seismic waveform indices from post-stack seismic data. The porosity data indicating the geological conditions of the target stratum are obtained through the porosity volume.

4. The method as described in claim 1, characterized in that, Obtain shale thickness data indicating geological conditions of the target stratigraphic unit, specifically including: Based on rock physics analysis, the shear wave impedance threshold value of the shale in the study area was obtained. The shale time thickness data of the target layer is obtained by using the transverse wave impedance volume of the study area and the threshold value. Based on the shale time thickness data and P-wave velocity volume, shale thickness data indicating the geological conditions of the target layer are obtained.

5. The method as described in claim 1, characterized in that, Obtain the brittleness index data indicating the engineering conditions of the target layer, specifically including: Based on the brittleness index samples, Young's modulus volume, shear resistance volume, Lamé resistance volume, and Poisson's ratio volume in the study area, the fitting relationship between the brittleness index and Young's modulus, shear resistance, Lamé resistance, and Poisson's ratio was determined. Based on the fitting relationship and the Young's modulus body, shear resistance body, Lamé resistance body and Poisson's ratio body, the brittleness index body is obtained; The brittleness index data indicating the engineering conditions of the target layer are obtained through the brittleness index body.

6. The method as described in claim 1, characterized in that, Obtain crack density data indicating engineering conditions for the target layer, specifically including: Crack density volume was obtained based on the azimuth data volume of the study area; The crack density data indicating the engineering conditions of the target layer are obtained through the crack density volume.

7. The method according to any one of claims 1 to 6, characterized in that, Before obtaining the first data of the first parameter indicating the geological conditions of the target segment and the second data of the second parameter indicating the engineering conditions based on the pre-stack and post-stack inversion of seismic data of the study area, the method further includes: Through rock physics analysis, several sensitive parameters of shale reservoir oil and gas resources were determined; Post-stack and pre-stack inversions of seismic data in the study area were conducted to obtain the data volume corresponding to each sensitive parameter.

8. The method as described in claim 7, characterized in that, The specific parameters for determining shale reservoir oil and gas resources include: Determine at least one of the following sensitive parameters for shale reservoir oil and gas resources: Longitudinal wave velocity, transverse wave velocity, longitudinal wave impedance, transverse wave impedance, density, Poisson's ratio, Young's modulus, Lamé impedance, and shear modulus.

9. A shale reservoir double sweet spot seismic prediction device, characterized in that, include: The sensitive parameter data acquisition module is used to obtain P-wave velocity, S-wave velocity, P-wave impedance, S-wave impedance, density, Poisson's ratio, Young's modulus, Lamé impedance, and shear modulus data volumes based on pre-stack and post-stack inversions of seismic data in the study area; using post-stack seismic data and the S-wave impedance data volumes obtained from pre-stack and post-stack inversions, it obtains total organic carbon content, porosity, and shale thickness data; using azimuthal seismic data and the Young's modulus, shear impedance, Lamé impedance, Poisson's ratio, P-wave impedance, S-wave impedance, and density data volumes obtained from pre-stack and post-stack inversions, it obtains brittleness index, pore pressure, and fracture density data; it obtains at least one of the following first data as a first parameter indicating geological conditions of the target section: total organic carbon content data, porosity data, and shale thickness data; and at least one of the following second data as a second parameter indicating engineering conditions: brittleness index data, pore pressure data, and fracture density data; the geological conditions reflect hydrocarbon generation capacity and reservoir conditions, and the engineering conditions reflect mining difficulty; The double sweet spot evaluation parameter data acquisition module is used to determine the double sweet spot evaluation parameter data COMP=c1T+c2Ф+c3H+c4B+c5P+c6F according to the weight coefficients of the first parameter and the second parameter, as well as the first data and the second data, through the following formula, where COMP is the double sweet spot evaluation parameter, T is the total organic carbon content, Ф is the porosity, H is the mudstone and shale thickness, B is the brittleness index, P is the pore pressure, F is the fracture density, and the weight coefficients of T, Ф, H, B, P and F are c1, c2, c3, c4, c5 and c6, respectively, and the sum of c1, c2, c3, c4, c5 and c6 is 1; The double sweet spot prediction module is used to predict the double sweet spot of the target segment based on the double sweet spot evaluation parameter data and the predetermined double sweet spot evaluation criteria. The sensitive parameter data acquisition module obtains pore pressure data indicating engineering conditions of the target layer, specifically used to: based on the longitudinal wave impedance volume, transverse wave impedance volume, and density volume of the study area, obtain the pore pressure volume using the following formula: ； in, Pore ​​pressure, For the pressure of the overlying, For longitudinal wave impedance, For transverse wave impedance, Let h be the density, b1 be the depth, and b2 be the empirical constants of the study area. The pore pressure data indicating the engineering conditions of the target layer are obtained through the pore pressure body.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the shale reservoir double sweet spot seismic prediction method according to any one of claims 1 to 8.

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