A method, device, medium and equipment for predicting offshore low-permeability reservoir geology dessert

Abstract

The application discloses a kind of offshore low-permeability reservoir geology dessert prediction methods, comprising the following steps: low-permeability reservoir geology dessert prediction method includes the acquisition of low-permeability reservoir original well logging quality parameter and the determination of prestack inversion elastic parameter objective function, the construction of the isochronal inversion stratum framework of sedimentary mode guidance seismic recognizable scale, the construction of low-frequency model under paleogeomorphology constraint, prestack simultaneous inversion based on partial stack seismic data, reservoir quality parameter inversion based on bayesian discrimination, plane attribute extraction and characterization of three-dimensional RQI data body.The application is characterized by the distribution prediction of each period geology dessert of target interval by extracting the plane attribute of three-dimensional RQI data body, realizes the accurate characterization of offshore middle-deep low-permeability reservoir geology dessert, has better prediction ability, and then provides favorable support for development well site optimization deployment.

Description

Technical Field

[0001] This invention relates to the field of geological research technology for offshore oilfield development, specifically to a method, apparatus, medium, and equipment for predicting geological sweet spots in low-permeability offshore reservoirs. Background Technology

[0002] As production in shallow offshore oilfields declines year by year, low-permeability reserves in medium-deep formations are becoming increasingly important for future reserve replacement and production contribution, which is of great significance for increasing reserves and production in offshore oilfields. However, due to the low physical properties of low-permeability reservoirs, the recovery rate of developed medium-deep oilfields is usually low, even below 20%, resulting in poor development performance. Therefore, strengthening the prediction and characterization of favorable low-permeability reservoirs (geological sweet spots) can effectively support the optimization of injection-production well networks, improve water injection effects, and increase single-well production, which is the only way to efficiently develop medium-deep low-permeability offshore oil reservoirs.

[0003] Offshore low-permeability reservoirs are deeply buried, mostly below 2500 meters, with complex and varied geological characteristics and a lack of clear patterns in sweet spot distribution. Simultaneously, seismic data quality is low, with dominant frequencies mostly below 25 Hz, and well logging data is scarce and unevenly distributed, making effective comprehensive reservoir evaluation difficult. Current prediction methods mainly include seismic inversion techniques and seismic attribute analysis; however, for mid-to-deep offshore low-permeability oilfields, manually identifying the differences in seismic response between sweet and non-sweet spots is challenging, and traditional reservoir prediction methods are heavily influenced by human factors, resulting in low accuracy and multiple solutions. Summary of the Invention

[0004] The purpose of this invention is to provide a method, apparatus, medium, and equipment for predicting geological sweet spots in low-permeability offshore reservoirs, in order to solve the problem of low characterization accuracy of geological sweet spots in medium-deep low-permeability offshore reservoirs in the prior art.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] In a first aspect, the present invention provides a method for predicting geological sweet spots in low-permeability offshore reservoirs, the method comprising:

[0007] The original logging quality parameters of low-permeability reservoirs are calculated based on logging data, and the function of the target elastic parameters for pre-stack inversion is determined through rock physics analysis.

[0008] We constructed a seismically identifiable scale isochronous inversion stratigraphic framework guided by sedimentary models, and extracted small-layer paleogeomorphic features of each phase of the target segment after stratigraphic refinement interpretation.

[0009] By combining the paleogeography of each phase of the target segment with the logging curves of the pre-stack inversion elastic parameters, a low-frequency model constrained by paleogeography is constructed.

[0010] Seismic elastic parameter volume is obtained by inverting partially stacked seismic data volume based on seismic data. The seismic elastic parameter volume is substituted into the function of the pre-stack inversion elastic parameter objective to obtain the reservoir sensitive seismic elastic parameters. The frequency domain merging method is used to merge the frequency band range below the low frequency of the original seismic data in the low frequency model into this inversion result to obtain high-precision pre-stack inversion results.

[0011] Based on the Bayesian principle, a function for solving the three-dimensional RQI data volume is constructed by mapping multiple variables to the high-precision pre-stack inversion results.

[0012] By comparing the consistency between the 3D RQI data volume and the original well logging quality parameters, the planar attributes of the 3D RQI data volume are extracted to characterize the distribution prediction of geological sweet spots in each phase of the target interval.

[0013] The method for predicting geological sweet spots in low-permeability offshore reservoirs, preferably, uses the function of the objective of the pre-stack inversion elastic parameters as follows:

[0014]

[0015] In the formula, ρ is the density of the upper medium; V p V represents the longitudinal wave velocity of the upper medium. s λ represents the shear wave velocity of the upper medium; λ represents the reservoir's seismically sensitive elastic parameters.

[0016] The method for determining the function of the pre-stack inversion elastic parameter objective is as follows:

[0017] Based on the consistency processing of well logging data and the prediction of shear waves in rock physical modeling, rock physical analysis is performed on the well logging data, and high-permeability reservoir sensitive seismic elastic parameters are selected. These high-permeability reservoir sensitive seismic elastic parameters include the P-wave velocity V. p Shear wave velocity V s And density ρ, a function that determines the pre-stack inversion elastic parameter objective based on the selected high-permeability reservoir sensitive seismic elastic parameters.

[0018] The method for predicting geological sweet spots in low-permeability offshore reservoirs, preferably, involves obtaining the original well logging quality parameters of the low-permeability reservoir as follows:

[0019] Based on well logging data evaluation, quality control, and result interpretation, a function is constructed to solve for the original well logging quality parameters of low-permeability reservoirs. The formula for this function is as follows:

[0020]

[0021] In the formula, RQI is the reservoir logging quality parameter; md is the median grain size of the rock; sort is the sorting value of the rock grain size; corro is the content of soluble minerals in the rock; K1 and K2 are constant coefficients.

[0022] The method for predicting geological sweet spots in low-permeability marine reservoirs, preferably, involves constructing a seismically identifiable scale isochronous inversion stratigraphic framework guided by sedimentary models, and extracting the paleogeomorphic features of each phase of the target stratigraphic segment after refined interpretation, as follows:

[0023] The top and bottom interfaces of the target layer with obvious seismic reflections are interpreted. The paleogeography of the target layer is obtained by subtracting the top and bottom interfaces of the target layer, and two layers inside the target layer are extracted.

[0024] By combining well logging subdivision to further refine the interpretation of two layers within the target section, four layers within the target section are obtained. Subtracting between each pair of layers within the four layers within the target section yields the paleogeography of the early, middle, and late layers.

[0025] Based on the four layers within the target segment after refined interpretation, a seismic-identifiable scale isochronous inversion stratigraphic framework is constructed using a parallel bottom layer subdivision algorithm.

[0026] The method for predicting geological sweet spots in low-permeability offshore reservoirs, preferably, involves combining the paleogeomorphic features of each phase of the target formation with the logging curves of pre-stack inversion elastic parameters to construct a low-frequency model constrained by paleogeomorphic features, as follows:

[0027] Based on the extracted paleogeomorphology of each stratigraphic phase, the well point locations matching the paleogeomorphology of each stratigraphic phase are determined on the logging curves of the pre-stack inversion elastic parameters.

[0028] By interpolating / extrapolating the well curve values ​​at well point locations using paleogeographic constraints, a three-dimensional attribute data volume is created.

[0029] The Kriging method was used to eliminate the prediction error of well point location to obtain a low-frequency model.

[0030] The method for predicting geological sweet spots in low-permeability marine reservoirs, preferably, involves the following inversion method for partially stacked seismic data volumes:

[0031] The common reflection point gathers based on seismic data are optimized and stacked at different angles to obtain multiple stacked seismic data volumes at different angles;

[0032] The Aki-Richard approximation formula is used to simultaneously invert multiple stacked seismic data volumes to obtain the P-wave impedance volume and V... p / V s And the seismic elastic parameter body of ρ, the Aki-Richard approximation formula is:

[0033]

[0034] In the formula, R PP (θ) is the reflection coefficient of the longitudinal wave; Δv p and Δv s θ represents the difference in longitudinal and transverse wave velocities between the upper and lower media, respectively; θ is the incident angle of the seismic wave.

[0035] The method for predicting geological sweet spots in low-permeability offshore reservoirs, preferably, uses a three-dimensional RQI data volume with the following function:

[0036]

[0037] In the formula, P represents probability; RQI i P(RQI) represents the RQI value at point i. i ) is RQI i The prior probability; P(λ,V) p / V S |RQI i ) indicates that the RQI is known. i The probability of sensitive seismic elastic parameters under certain conditions; P(RQI) i |λ,V p / V S ) represents the probability of RQI given the elastic parameters of a sensitive earthquake.

[0038] Secondly, the present invention provides an analytical apparatus comprising:

[0039] The first processing unit is used to calculate and obtain the original logging quality parameters of low-permeability reservoirs based on logging data, and to determine the function of the target elastic parameters for pre-stack inversion through rock physics analysis.

[0040] The second processing unit is used to construct a seismically identifiable scale isochronous inversion stratigraphic framework guided by sedimentary models, and to extract paleogeomorphology of each period of the target stratigraphic segment after stratigraphic refinement interpretation.

[0041] The third processing unit is used to combine the small-layer paleogeography of each phase of the target segment with the well logging curves of the pre-stack inversion elastic parameters to construct a low-frequency model under paleogeographic constraints.

[0042] The fourth processing unit is used to invert and calculate the seismic elastic parameter volume based on the partially stacked seismic data volume, substitute the seismic elastic parameter volume into the function of the pre-stack inversion elastic parameter objective to obtain the reservoir sensitive seismic elastic parameters, and use the frequency domain merging method to merge the frequency band range below the original low frequency of the low frequency model into this inversion result to obtain high-precision pre-stack inversion results.

[0043] The fifth processing unit is a function used to construct a three-dimensional RQI data volume by mapping high-precision pre-stack inversion results using multiple variables based on Bayesian principles.

[0044] The sixth processing unit is used to compare the consistency between the three-dimensional RQI data volume and the original well logging quality parameters, and to extract the planar attributes of the three-dimensional RQI data volume to characterize the distribution prediction of geological sweet spots in each phase of the target interval.

[0045] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of the method for predicting geological sweet spots in low-permeability offshore reservoirs as described in the first aspect of the present invention.

[0046] Fourthly, the present invention provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the steps of the method for predicting geological sweet spots in low-permeability offshore reservoirs as described in the first aspect of the present invention.

[0047] The present invention, by adopting the above technical solution, has the following beneficial effects:

[0048] By optimizing the seismically sensitive elastic parameters characterizing high-permeability reservoirs through refined rock physics analysis, and constructing reservoir quality indicators (RQIs) reflecting sweet spots based on well logging data, a refined stratigraphic framework is built based on sedimentary models to ensure the accuracy of pre-stack inversion. Furthermore, the construction of a low-frequency model under paleogeographic constraints provides low-frequency components for high-precision inversion. Based on Bayesian discriminant multivariate mapping, the seismic elastic parameter volume obtained from pre-stack inversion is inverted to obtain a three-dimensional RQI data volume. By extracting the planar attributes of the three-dimensional RQI data volume, the distribution and prediction of geological sweet spots in different phases of the target stratigraphic segment are characterized, achieving accurate characterization of geological sweet spots in mid-to-deep low-permeability offshore reservoirs. Compared to traditional methods, this approach has a clearer objective and better predictive capabilities, thus providing favorable support for the optimized deployment of development wells. Attached Figure Description

[0049] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts. In the drawings:

[0050] Figure 1 This is a flowchart illustrating the method for predicting geological sweet spots in low-permeability offshore reservoirs provided in this embodiment of the invention.

[0051] Figure 2 This is a rock physical analysis diagram of the target work area of ​​the marine low-permeability reservoir geological sweet spot prediction method provided in this embodiment of the invention;

[0052] Figure 3(a) is the cross-plot analysis of the original logging quality parameters and λ, and (b) is the cross-plot analysis of the original logging quality parameters and Vp / Vs.

[0053] Figure 4 (a) is the paleogeographic map of the target layer, (b) is the root mean square amplitude attribute map of the earthquake in the target layer, and (c) is the overlay map of paleogeography and root mean square amplitude attribute of earthquake.

[0054] Figure 5 It contains the original seismic profile and the interpreted horizon map;

[0055] Figure 6 (a) is the early paleogeographic map of the target stratigraphic segment after stratigraphic refinement interpretation; (b) is the middle paleogeographic map of the target stratigraphic segment after stratigraphic refinement interpretation; and (c) is the late paleogeographic map of the target stratigraphic segment after stratigraphic refinement interpretation.

[0056] Figure 7 A well profile showing the connection between the three-dimensional RQI data volume of the target layer and the original logging quality parameters of the blind well;

[0057] Figure 8 (a) is the planar attribute distribution map of the early three-dimensional RQI data volume, (b) is the planar attribute distribution map of the mid-term three-dimensional RQI data volume, and (c) is the planar attribute distribution map of the late three-dimensional RQI data volume. Detailed Implementation

[0058] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the invention and to fully convey the scope of the invention to those skilled in the art.

[0059] Traditional reservoir prediction methods have low accuracy in characterizing geological sweet spots in mid-to-deep low-permeability offshore reservoirs. This invention provides a method, apparatus, medium, and equipment for predicting geological sweet spots in low-permeability reservoirs. The method includes acquiring the original well logging quality parameters of the low-permeability reservoir and determining the objective function of the pre-stack inversion elastic parameters; constructing a seismically identifiable scale isochronous inversion stratigraphic framework guided by sedimentary models; constructing a low-frequency model under paleogeographic constraints; performing simultaneous pre-stack inversion based on partially stacked seismic data; inverting reservoir quality parameters based on Bayesian discrimination; and extracting and characterizing the planar attributes of the 3D RQI data volume. This invention achieves accurate characterization of geological sweet spots in mid-to-deep low-permeability offshore reservoirs by extracting the planar attributes of the 3D RQI data volume to characterize and predict the distribution of geological sweet spots in different phases of the target formation, thus providing better predictive capabilities and offering favorable support for the optimized deployment of development wells.

[0060] The present invention will be described in detail below through embodiments.

[0061] Example

[0062] like Figure 1 As shown, this invention provides a method for predicting geological sweet spots in low-permeability offshore reservoirs, comprising:

[0063] Based on well logging data, the original well logging quality parameters of low-permeability reservoirs are calculated, and the function of the target pre-stack inversion elastic parameters is determined through rock physical analysis. The target function of the pre-stack inversion elastic parameters is:

[0064]

[0065] In the formula: ρ is the density of the upper medium; V p V represents the longitudinal wave velocity of the upper medium. s λ represents the shear wave velocity of the upper medium; λ represents the reservoir's seismically sensitive elastic parameters.

[0066] We constructed a seismically identifiable scale isochronous inversion stratigraphic framework guided by sedimentary models, and extracted paleogeomorphology of each period of the target stratigraphic segment after refined stratigraphic interpretation.

[0067] By combining the paleogeography of each phase of the target segment with the logging curves of the pre-stack inversion elastic parameters, a low-frequency model constrained by paleogeography is constructed.

[0068] Inversion calculations of partially stacked seismic data volumes based on seismic data yielded V... p / V s The seismic elastic parameter volume of ρ is used to obtain λ by substituting it into the function of the pre-stack inversion elastic parameter objective. Then, a frequency domain merging method is employed to incorporate the frequency bands below the low-frequency range of the original earthquake in the low-frequency model into this inversion result, yielding a high-precision pre-stack inversion result λ data volume and V. p / V s Data body;

[0069] Based on Bayesian principles, a function for solving the 3D RQI data volume is constructed by mapping high-precision pre-stack inversion results using multiple variables. The formula for the function is as follows:

[0070]

[0071] In the formula; P represents probability; RQI i P(RQI) represents the RQI value at point i. i ) is RQI i The prior probability; P(λ,V) p / V S |RQI i ) indicates that the RQI is known.i The probability of sensitive seismic elastic parameters under certain conditions; P(RQI) i |λ,V p / V S () represents the probability of RQI given the elastic parameters of a sensitive earthquake;

[0072] By comparing the consistency between the 3D RQI data volume and the original well logging quality parameters, the planar attributes of the 3D RQI data volume are extracted to characterize the distribution prediction of geological sweet spots in each phase of the target interval.

[0073] Furthermore, it also includes a method for determining the formulas for pre-stack inversion elastic parameters:

[0074] Based on the consistency processing of well logging data and the prediction of shear waves in rock physics modeling, rock physics analysis is performed on the well logging data, and seismically sensitive elastic parameters of high-permeability reservoirs are selected. These parameters include the P-wave velocity V0. p Shear wave velocity V s And density ρ, a function used to determine the pre-stack inversion elastic parameter objective based on the selected seismically sensitive elastic parameters of high-permeability reservoirs. Among these, rock physics cross-plot analysis, such as... Figure 2 As shown, it can be seen that by comprehensively utilizing V p / V s Furthermore, λ has a certain ability to distinguish between low-permeability and high-permeability reservoirs.

[0075] Furthermore, it also includes methods for obtaining raw logging quality parameters for low-permeability reservoirs:

[0076] Based on well logging data evaluation, quality control, and result interpretation, a function is constructed to solve for the original well logging quality parameters of low-permeability reservoirs. The formula for this function is as follows:

[0077]

[0078] In the formula, RQI is the reservoir logging quality parameter; md is the median grain size of the rock; sort is the sorting value of the rock grain size; corro is the content of soluble minerals in the rock; K1 and K2 are constant coefficients.

[0079] As mentioned above, the calculated original logging quality parameter RQI of the low-permeability reservoir is compared with V... p / V s And perform intersection analysis on λ, such as Figure 3 As shown, they all exhibit a certain correlation, therefore the seismic elastic parameter V can be used. p / V s And the pre-stack inversion of λ to predict the RQI (reservoir quality) parameter of low-permeability logging reservoirs.

[0080] Furthermore, it also includes a method for constructing a seismically identifiable scale isochronous inversion stratigraphic framework guided by sedimentary models, and extracting paleogeomorphology of each period of the target stratigraphic segment after refined stratigraphic interpretation:

[0081] refer to Figure 5 The top and bottom interfaces of the target layer with obvious seismic reflections are interpreted. The paleogeography of the target layer is obtained by subtracting the top and bottom interfaces of the target layer, and two layers inside the target layer are extracted.

[0082] By combining well logging subdivision to further refine the interpretation of two layers within the target section, four layers within the target section are obtained. Subtracting between each pair of layers within the four layers within the target section yields the paleogeography of the early, middle, and late layers.

[0083] Based on the four layers within the target segment after refined interpretation, a seismic-identifiable scale isochronous inversion stratigraphic framework is constructed using a parallel bottom layer subdivision algorithm.

[0084] As mentioned above, due to the low quality of seismic data in deep-sea, low-permeability oilfields, the complex characteristics of reservoir seismic structure changes, and the strong multiple interpretations of stratigraphic interpretation, it is necessary to construct an isochronous inversion stratigraphic framework with seismic identifiable scales, guided by sedimentary models, so as to lay the foundation for high-precision inversion.

[0085] Furthermore, it also includes precise methods for constructing seismically identifiable scale isochronous inversion stratigraphic frameworks:

[0086] The root mean square (RMS) amplitude properties of seismic data between the top and bottom interfaces of the target stratigraphic segment are extracted. These extracted RMS amplitude properties are then overlaid with the paleogeography of the target stratigraphic segment. The accuracy of the interpretation of the top and bottom interfaces of the target stratigraphic segment is determined by analyzing the match between the provenance and regional geological sediments displayed through this overlay. Figure 4 As shown, the superimposed data reveals that the overall source material originates from the west, consistent with regional geological sedimentation, indicating that the interpretation of the top and bottom interfaces of the target layer is reasonable.

[0087] Extracting the provenance and sedimentary patterns of paleogeomorphology from different stratigraphic periods, and determining the accuracy of stratigraphic refinement within the target section by matching these patterns with regional geological sediments. For example... Figure 6 As shown, the provenance sedimentary patterns are consistent with the regional geological sedimentation, thus confirming the accuracy of the detailed interpretation of the stratigraphic layers within the target section; otherwise, further modifications are required.

[0088] Furthermore, it also includes a method for constructing a low-frequency model constrained by paleogeography by combining the paleogeomorphic features of each phase of the target segment with the well logging curves of the pre-stack inversion elastic parameters:

[0089] Based on the extracted paleogeomorphology of each stratigraphic phase, the well point locations matching the paleogeomorphology of each stratigraphic phase are determined on the logging curves of the pre-stack inversion elastic parameters.

[0090] By interpolating / extrapolating the well curve values ​​at well point locations using paleogeographic constraints, a three-dimensional attribute data volume is created.

[0091] The Kriging method was used to eliminate the prediction error of well point location to obtain a low-frequency model.

[0092] As mentioned above, seismic data is band-limited, and the low-frequency components retrieved need to be compensated by establishing a low-frequency model. The construction of a low-frequency model under paleogeographic constraints provides low-frequency components for high-precision inversion.

[0093] Furthermore, it also includes inversion methods for partially stacked seismic data volumes:

[0094] The common reflection point gathers based on seismic data are optimized and stacked at different angles to obtain multiple stacked seismic data volumes at different angles;

[0095] The Aki-Richard approximation formula is used to simultaneously invert multiple stacked seismic data volumes to obtain the P-wave impedance volume and V... p / V s And the seismic elastic parameter body of ρ, the Aki-Richard approximation formula is:

[0096]

[0097] In the formula, R PP (θ) is the reflection coefficient of the longitudinal wave; Δv p and Δv s θ represents the difference in longitudinal and transverse wave velocities between the upper and lower media, respectively; θ is the incident angle of the seismic wave.

[0098] As mentioned above, the optimization processing of common reflection point gathers includes active correction, denoising, leveling, and other optimization processes to improve gather quality.

[0099] like Figure 7 As shown, a comparison of the relationship between the three-dimensional RQI data volume obtained by the low-permeability reservoir geological sweet spot prediction method of the present invention and the original logging quality parameter RQI of the blind well shows that the three-dimensional RQI data volume obtained by the present invention has high predictive power and high consistency with the well.

[0100] like Figure 8 As shown, the extracted three-dimensional RQI data volume plane attribute map reflects the temporal thickness plane distribution of geological sweet spots. It can be seen that the early geological sweet spots are mainly distributed in the southwest of the study area, the middle geological sweet spots gradually extend from the southwest to the northeast, and the late geological sweet spots are concentrated in the central part of the study area.

[0101] Based on the above-mentioned method for predicting geological sweet spots in low-permeability reservoirs, this invention also provides an analytical apparatus for the method, comprising:

[0102] The first processing unit is used to calculate and obtain the original logging quality parameters of low-permeability reservoirs based on logging data, and to determine the function of the target elastic parameters for pre-stack inversion through rock physics analysis.

[0103] The second processing unit is used to construct a seismically identifiable scale isochronous inversion stratigraphic framework guided by sedimentary models, and to extract paleogeomorphology of each period of the target stratigraphic segment after stratigraphic refinement interpretation.

[0104] The third processing unit is used to combine the small-layer paleogeography of each phase of the target segment with the well logging curves of the pre-stack inversion elastic parameters to construct a low-frequency model under paleogeographic constraints.

[0105] The fourth processing unit is used to perform inversion calculations on partially stacked seismic data volumes based on seismic data to obtain V... p / V s The seismic elastic parameter volume of ρ is used to obtain λ by substituting it into the function of the pre-stack inversion elastic parameter objective. Then, a frequency domain merging method is employed to incorporate the frequency bands below the low-frequency range of the original earthquake in the low-frequency model into this inversion result, yielding a high-precision pre-stack inversion result λ data volume and V. p / V s Data body;

[0106] The fifth processing unit is a function used to construct a three-dimensional RQI data volume by mapping high-precision pre-stack inversion results using multiple variables based on Bayesian principles.

[0107] The sixth processing unit is used to compare the consistency between the three-dimensional RQI data volume and the original well logging quality parameters, and to extract the planar attributes of the three-dimensional RQI data volume to characterize the distribution prediction of geological sweet spots in each phase of the target interval.

[0108] Based on the above-described method for predicting geological sweet spots in low-permeability reservoirs, the present invention also provides a computer-readable storage medium storing a computer program thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of the method for predicting geological sweet spots in low-permeability reservoirs as described in any one of claims 1-7.

[0109] Based on the above-described method for predicting geological sweet spots in low-permeability reservoirs, the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the computer program, implements the steps of the method for predicting geological sweet spots in low-permeability reservoirs as described in any one of claims 1-7.

[0110] This invention optimizes seismically sensitive elastic parameters of high-permeability reservoirs and constructs reservoir quality parameters (RQI) reflecting sweet spots. Based on sedimentary models, it constructs a stratigraphic framework to ensure the accuracy of pre-stack inversion. Furthermore, it provides low-frequency components for high-precision inversion through the construction of a low-frequency model constrained by paleogeography. Using Bayesian discriminant multivariate mapping, it inverts the seismic elastic parameter volume obtained from pre-stack inversion to obtain a three-dimensional RQI data volume. By extracting the planar attributes of the three-dimensional RQI data volume, it characterizes and predicts the distribution of geological sweet spots in different phases of the target stratigraphic segment, achieving accurate characterization of geological sweet spots in mid-to-deep low-permeability offshore reservoirs. Compared to traditional methods, this approach has a clearer objective and better predictive capabilities, thus providing favorable support for the optimized deployment of development wells.

[0111] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting geological sweet spots in low-permeability offshore reservoirs, characterized in that, The method for predicting geological sweet spots in low-permeability reservoirs includes: The original logging quality parameters of low-permeability reservoirs are calculated based on logging data, and the function of the target elastic parameters for pre-stack inversion is determined through rock physics analysis. We constructed a seismically identifiable scale isochronous inversion stratigraphic framework guided by sedimentary models, and extracted small-layer paleogeomorphic features of each phase of the target segment after stratigraphic refinement interpretation. By combining the paleogeography of each phase of the target segment with the logging curves of the pre-stack inversion elastic parameters, a low-frequency model constrained by paleogeography is constructed. Seismic elastic parameter volume is obtained by inverting partially stacked seismic data volume based on seismic data. The seismic elastic parameter volume is substituted into the function of the pre-stack inversion elastic parameter objective to obtain the reservoir sensitive seismic elastic parameters. The frequency domain merging method is used to merge the frequency band range below the low frequency of the original seismic data in the low frequency model into this inversion result to obtain high-precision pre-stack inversion results. Based on Bayesian principles, a function for solving the three-dimensional RQI data volume is constructed by mapping high-precision pre-stack inversion results using multiple variables. By comparing the consistency between the 3D RQI data volume and the original well logging quality parameters, the planar attributes of the 3D RQI data volume are extracted to characterize the distribution prediction of geological sweet spots in each phase of the target interval.

2. The method for predicting geological sweet spots in low-permeability offshore reservoirs according to claim 1, characterized in that, The function of the pre-stack inversion elastic parameter objective is: In the formula, ρ is the density of the upper medium; V p V represents the longitudinal wave velocity of the upper medium. s λ represents the shear wave velocity of the upper medium; λ is the reservoir's seismically sensitive elastic parameter. The method for determining the function of the pre-stack inversion elastic parameter objective is as follows: Based on the consistency processing of well logging data and the prediction of shear waves in rock physical modeling, rock physical analysis is performed on the well logging data, and high-permeability reservoir sensitive seismic elastic parameters are selected. These high-permeability reservoir sensitive seismic elastic parameters include the P-wave velocity V. p Shear wave velocity V s And density ρ, a function that determines the target of pre-stack inversion elastic parameters based on the selected high-permeability reservoir sensitive seismic elastic parameters.

3. The method for predicting geological sweet spots in low-permeability offshore reservoirs according to claim 1, characterized in that, The method for obtaining the original logging quality parameters of the low-permeability reservoir is as follows: Based on well logging data evaluation, quality control, and result interpretation, a function is constructed to solve for the original well logging quality parameters of low-permeability reservoirs. The formula for this function is as follows: In the formula, RQI is the reservoir logging quality parameter; md is the median grain size of the rock; sort is the sorting value of the rock grain size; corro is the content of soluble minerals in the rock; K1 and K2 are constant coefficients.

4. The method for predicting geological sweet spots in low-permeability offshore reservoirs according to claim 1, characterized in that, The method for constructing a seismically identifiable scale isochronous inversion stratigraphic framework guided by sedimentary models, and extracting the small-layer paleogeomorphology of each period of the target segment after stratigraphic refinement interpretation, is as follows: The top and bottom interfaces of the target layer with obvious seismic reflections are interpreted. The paleogeography of the target layer is obtained by subtracting the top and bottom interfaces of the target layer, and two layers inside the target layer are extracted. By combining well logging subdivision to further refine the interpretation of two layers within the target section, four layers within the target section are obtained. Subtracting between each pair of layers within the four layers within the target section yields the paleogeography of the early, middle, and late layers. Based on the four layers within the target segment after refined interpretation, a seismic-identifiable scale isochronous inversion stratigraphic framework is constructed using a parallel bottom layer subdivision algorithm.

5. The method for predicting geological sweet spots in low-permeability offshore reservoirs according to claim 4, characterized in that, The method for constructing a low-frequency model constrained by paleogeography by combining the paleogeomorphic features of each phase of the target stratigraphic segment with the well logging curves of the pre-stack inversion elastic parameters is as follows: Based on the extracted paleogeomorphology of each stratigraphic phase, the well point locations matching the paleogeomorphology of each stratigraphic phase are determined on the logging curves of the pre-stack inversion elastic parameters. By interpolating / extrapolating the well curve values ​​at well point locations using paleogeographic constraints, a three-dimensional attribute data volume is created. The Kriging method was used to eliminate the prediction error of well point location to obtain a low-frequency model.

6. The method for predicting geological sweet spots in low-permeability offshore reservoirs according to claim 2, characterized in that, The inversion method for the partially stacked seismic data volume is as follows: The common reflection point gathers based on seismic data are optimized and stacked at different angles to obtain multiple stacked seismic data volumes at different angles; The Aki-Richard approximation formula is used to simultaneously invert multiple stacked seismic data volumes to obtain the P-wave impedance volume and V... p / V s And the seismic elastic parameter body of ρ, the Aki-Richard approximation formula is: In the formula, R PP (θ) is the reflection coefficient of the longitudinal wave; Δv p and Δv s θ represents the difference in longitudinal and transverse wave velocities between the upper and lower media, respectively; θ is the incident angle of the seismic wave.

7. The method for predicting geological sweet spots in low-permeability offshore reservoirs according to claim 6, characterized in that, The function of the three-dimensional RQI data volume is: In the formula, P represents probability; RQI i P(RQI) represents the RQI value at point i. i ) is RQI i The prior probability; P(λ,V) p / V S |RQI i ) indicates that the RQI is known. i The probability of sensitive seismic elastic parameters under certain conditions; P(RQI) i |λ,V p / V S ) represents the probability of RQI given the elastic parameters of a sensitive earthquake.

8. An analytical apparatus, characterized in that, The analytical apparatus includes: The first processing unit is used to calculate and obtain the original logging quality parameters of low-permeability reservoirs based on logging data, and to determine the function of the target elastic parameters for pre-stack inversion through rock physics analysis. The second processing unit is used to construct a seismically identifiable scale isochronous inversion stratigraphic framework guided by sedimentary models, and to extract paleogeomorphology of each period of the target stratigraphic segment after stratigraphic refinement interpretation. The third processing unit is used to combine the small-layer paleogeography of each phase of the target segment with the well logging curves of the pre-stack inversion elastic parameters to construct a low-frequency model under paleogeographic constraints. The fourth processing unit is used to invert and calculate the seismic elastic parameter volume based on the partially stacked seismic data volume, substitute the seismic elastic parameter volume into the function of the pre-stack inversion elastic parameter objective to obtain the reservoir sensitive seismic elastic parameters, and use the frequency domain merging method to merge the frequency band range below the original low frequency of the low frequency model into this inversion result to obtain high-precision pre-stack inversion results. The fifth processing unit is a function used to construct a three-dimensional RQI data volume by mapping high-precision pre-stack inversion results using multiple variables based on Bayesian principles. The sixth processing unit is used to compare the consistency between the three-dimensional RQI data volume and the original well logging quality parameters, and to extract the planar attributes of the three-dimensional RQI data volume to characterize the distribution prediction of geological sweet spots in each phase of the target interval.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for predicting geological sweet spots in marine low-permeability reservoirs as described in any one of claims 1-7.

10. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method for predicting geological sweet spots in marine low-permeability reservoirs as described in any one of claims 1-7.

Images