A method, device and equipment for determining the cause of formation overpressure and a storage medium

Abstract

The application discloses a stratum overpressure cause determining method, device and equipment and a storage medium. The method comprises the following steps: acquiring multi-source geological data corresponding to each stratum region in a target salt rock stratum to be subjected to overpressure cause determination; performing feature fusion processing on the multi-source geological data to construct a multi-dimensional feature vector set of each stratum region; performing unsupervised clustering processing on the multi-dimensional feature vector set corresponding to each stratum region based on a preset neighborhood adjustment mechanism to obtain each overpressure mechanism classification cluster; and determining an overpressure cause result corresponding to each stratum region in the target salt rock stratum according to the overpressure mechanism classification cluster and a pre-constructed overpressure cause mapping relationship. By adopting a lithology and stratum feature self-adaptive dynamic clustering radius, the application significantly improves the recognition accuracy and generalization ability of the heterogeneous carbonate rock overpressure mechanism, and provides an intelligent solution for drilling safety, reservoir evaluation and productivity prediction in oil and gas exploration and development.

Description

Technical Field

[0001] This invention relates to the field of geological exploration technology, and in particular to a method, apparatus, equipment and storage medium for determining the cause of formation overpressure. Background Technology

[0002] As important global oil and gas reservoirs, acid and salt rock formations present significant challenges to exploration and development due to their complex pore structures and high heterogeneity. Accurate identification and prediction of overpressure mechanisms in acid and salt rock formations have become crucial geological prerequisites for ensuring drilling safety, optimizing reservoir evaluation, and improving the accuracy of production prediction.

[0003] Formation overpressure refers to the geological phenomenon where the pressure of pore fluids in a formation exceeds the hydrostatic pressure. It is primarily caused by various mechanisms, including undercompaction, fluid expansion, and tectonic compression. In carbonate rocks, overpressure of different origins has drastically different controlling effects on hydrocarbon migration, accumulation, and preservation. Accurately determining formation overpressure has a significant impact on engineering safety and economic benefits.

[0004] Current overpressure identification methods primarily rely on empirical models from single data sources (such as the Eaton method for well logging curves) and subjective expert judgment, which have significant limitations: First, the data is isolated, failing to integrate multi-source information such as well logging, seismic data, and drilling data, leading to the omission of key signals and confusion of mechanisms; second, the models have poor generalization ability, and traditional methods based on fixed thresholds are difficult to adapt to strong heterogeneity, resulting in a high misjudgment rate in complex diagenetic areas; third, there is a high degree of reliance on human intervention, lacking standardized intelligent methods. These problems severely restrict the high-precision and high-efficiency identification of overpressure mechanisms in deep carbonate rocks. Summary of the Invention

[0005] This invention provides a method, apparatus, equipment, and storage medium for determining the genesis of stratigraphic overpressure. It employs a dynamic clustering radius that is adaptive to lithology and stratigraphic characteristics, which significantly improves the identification accuracy and generalization ability of overpressure mechanisms in heterogeneous carbonate rocks.

[0006] According to one aspect of the present invention, a method for determining the origin of formation overpressure is provided. The method includes:

[0007] Acquire multi-source geological data corresponding to various stratigraphic regions in the target salt rock strata whose overcompression genesis is to be investigated;

[0008] The multi-source geological data are subjected to feature fusion processing to construct a multi-dimensional feature vector set for each of the stratigraphic regions.

[0009] Based on a preset neighborhood adjustment mechanism, the multidimensional feature vector sets corresponding to each stratigraphic region are subjected to unsupervised clustering to obtain each overpressure mechanism classification cluster. The preset neighborhood adjustment mechanism dynamically adjusts the clustering radius according to the burial depth and seismic velocity of the target salt rock stratum.

[0010] Based on the overpressure mechanism classification cluster and the pre-constructed overpressure genesis mapping relationship, the overpressure genesis result corresponding to each stratigraphic region in the target salt rock formation is determined.

[0011] According to another aspect of the present invention, an apparatus for determining the origin of formation overpressure is provided. The apparatus includes:

[0012] The geological data acquisition module is used to acquire multi-source geological data corresponding to various stratigraphic regions in the target salt rock strata to be studied for overpressure genesis.

[0013] The feature vector determination module is used to perform feature fusion processing on the multi-source geological data to construct a multi-dimensional feature vector set for each of the stratigraphic regions.

[0014] The feature vector clustering module is used to perform unsupervised clustering processing on the multidimensional feature vector sets corresponding to each of the stratigraphic regions based on a preset neighborhood adjustment mechanism to obtain each overpressure mechanism classification cluster. The preset neighborhood adjustment mechanism dynamically adjusts the clustering radius according to the burial depth and seismic velocity of the target salt rock strata.

[0015] The overpressure origin determination module is used to determine the overpressure origin result corresponding to each stratigraphic region in the target salt rock formation based on the overpressure mechanism classification cluster and the pre-constructed overpressure origin mapping relationship.

[0016] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0017] At least one processor; and

[0018] A memory communicatively connected to the at least one processor; wherein,

[0019] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the formation overpressure origin determination method according to any embodiment of the present invention.

[0020] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the formation overpressure origin determination method according to any embodiment of the present invention.

[0021] The technical solution of this invention involves acquiring multi-source geological data corresponding to various stratigraphic regions in the target salt rock formation for overpressure genesis analysis; performing feature fusion processing on the multi-source geological data to construct multi-dimensional feature vector sets for each stratigraphic region; performing unsupervised clustering processing on the multi-dimensional feature vector sets corresponding to each stratigraphic region based on a preset neighborhood adjustment mechanism to obtain various overpressure mechanism classification clusters; and determining the overpressure genesis result corresponding to each stratigraphic region in the target salt rock formation based on the overpressure mechanism classification clusters and the pre-constructed overpressure genesis mapping relationship. This invention overcomes the defect of missing signals from a single data source; it employs a dynamic clustering radius that adapts to lithology and stratigraphic characteristics, significantly improving the identification accuracy and generalization ability of overpressure mechanisms in strongly heterogeneous carbonate rocks. It greatly reduces reliance on expert experience, providing a reliable basis for reservoir evaluation and production prediction of carbonate rocks while ensuring drilling safety.

[0022] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart of a method for determining the genesis of formation overpressure according to an embodiment of the present invention;

[0025] Figure 2 This is a flowchart of a method for determining the genesis of formation overpressure according to an embodiment of the present invention;

[0026] Figure 3 This is a structural diagram of a formation overpressure origin determination device provided according to an embodiment of the present invention;

[0027] Figure 4 This is a schematic diagram of the structure of an electronic device for implementing the method for determining the formation overpressure origin in this embodiment of the invention. Detailed Implementation

[0028] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0030] Figure 1 This is a flowchart illustrating a method for determining the genesis of formation overpressure according to an embodiment of the present invention. This embodiment is applicable to analyzing and determining the causes of overpressure in carbonate rock formations. The method can be executed by a formation overpressure genesis determination device, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes:

[0031] S101. Obtain multi-source geological data corresponding to each stratigraphic region in the target salt rock strata whose overpressure genesis is to be investigated.

[0032] The target salt rock strata can refer to the carbonate rock strata to be studied for overpressure genetic analysis. The stratigraphic region can refer to various areas within the target salt rock strata. Multi-source geological data can refer to the geological data of the target salt rock strata.

[0033] For example, multi-source geological data may include, but is not limited to, well logging data, well logging engineering data, and 3D seismic data of the target salt rock formation.

[0034] Specifically, based on the internal database, we collect multi-dimensional geological data, including original logging curves, logging data, drilling data, and 3D seismic data from all drilled wells related to the target salt rock formation, for analysis and research on the cause of overpressure in the target salt rock formation.

[0035] S102. Perform feature fusion processing on the multi-source geological data to construct a multi-dimensional feature vector set for each of the stratigraphic regions.

[0036] Specifically, for multi-source geological data such as well logging, seismic data, and engineering data, features sensitive to overpressure response are extracted. These features are arranged and concatenated in a predefined order to form a fixed-dimensional multi-dimensional feature vector set, thereby mapping all geological regions into a unified feature space and providing structured data for subsequent overpressure cause analysis.

[0037] For example, the step of performing feature fusion processing on the multi-source geological data to construct a multi-dimensional feature vector set includes: performing feature extraction processing on the multi-source geological data to obtain multiple initial features; aligning and integrating the multiple initial features to obtain an initial fused feature vector; and performing dimensionality reduction fusion processing on the initial fused feature vector based on a preset joint algorithm to obtain a multi-dimensional feature vector set.

[0038] Specifically, multi-source geological data undergoes feature extraction processing. The extracted features are then stitched together along the well depth direction to obtain multiple initial features. These initial features are aligned and integrated to obtain an initial fused feature vector. Based on a preset joint algorithm (such as a wavelet transform-principal component analysis (WT-PCA) joint algorithm), the initial fused feature vector is subjected to dimensionality reduction and fusion processing to form a multi-dimensional feature vector set characterizing the cause of overpressure.

[0039] For example, the step of performing feature extraction processing on the multi-source geological data to obtain multiple initial features includes:

[0040] Pore ​​pressure parameters, sonic time difference constants, and resistivity abrupt change characteristics are extracted from the well logging data in the multi-source geological data. Velocity spectrum inversion and overpressure body and fluid activity traces are extracted from the well logging data in the multi-source geological data.

[0041] Specifically, from the original well logging curves in multi-source geological data, pore pressure parameters, sonic time difference constants, and resistivity abrupt change characteristics directly related to pressure are calculated. From the original seismic data, velocity spectrum inversion overpressure bodies and fluid activity traces that can macroscopically reflect pressure anomalies are obtained, thus acquiring multiple initial features.

[0042] For example, before performing feature fusion processing on the multi-source geological data, the method further includes: performing data preprocessing on the multi-source geological data, wherein the preprocessing includes, but is not limited to, data cleaning, normalization, missing value imputation, and noise filtering.

[0043] Specifically, before performing feature fusion processing on multi-source geological data, it is necessary to perform data preprocessing, such as data cleaning, normalization, missing value imputation, and noise filtering, to eliminate errors, dimensional differences, information gaps, and random interference in the data, and ensure that data from different sources and at different scales can be effectively fused under the same benchmark, thereby improving the reliability, consistency, and stability of the subsequent feature vector set and modeling effect.

[0044] S103. Based on the preset neighborhood adjustment mechanism, the multi-dimensional feature vector sets corresponding to each of the strata regions are subjected to unsupervised clustering to obtain each overpressure mechanism classification cluster.

[0045] The preset neighborhood adjustment mechanism can adapt to the distribution of overpressure characteristics under different lithological backgrounds, and is used to solve the applicability problem of traditional clustering in heterogeneous carbonate strata. The preset neighborhood adjustment mechanism dynamically adjusts the clustering radius according to the burial depth and seismic velocity of the target salt rock strata.

[0046] Specifically, the clustering radius is determined based on a preset neighborhood adjustment mechanism. Then, based on the clustering algorithm and the clustering radius, the multi-dimensional feature vector sets corresponding to each stratum region are subjected to unsupervised clustering, thereby obtaining various overpressure mechanism classification clusters.

[0047] S104. Based on the overpressure mechanism classification cluster and the pre-constructed overpressure origin mapping relationship, determine the overpressure origin result corresponding to each stratigraphic region in the target salt rock formation.

[0048] The overpressure origin mapping relationship refers to the mapping relationship between overpressure origin results and data characteristics. This mapping relationship can be pre-constructed based on actual cases. The overpressure origin types include at least fluid expansion type, tectonic stress type, undercompaction type, and composite type.

[0049] Specifically, by analyzing the intra-cluster data characteristics of the overpressure mechanism classification clusters and matching the intra-cluster data characteristics with the overpressure origin mapping relationship, the overpressure origin results of each overpressure mechanism classification cluster can be obtained, and thus the overpressure origin results corresponding to each of the aforementioned stratigraphic regions can be obtained.

[0050] For example, the process of constructing the overpressure cause mapping relationship includes: acquiring sample overpressure cases and the sample geological data and sample overpressure causes corresponding to the sample overpressure cases; performing clustering processing on the sample overpressure cases according to the sample clustering radius corresponding to the sample overpressure cases to obtain sample cluster classification clusters; and performing relationship mapping processing on the cluster center features of the sample cluster classification clusters and the sample overpressure causes to construct the overpressure cause mapping relationship.

[0051] The sample geological data can refer to the geological data of sample overpressure cases. Specifically, a batch of historical case data whose overpressure causes have been authoritatively confirmed through drilling, testing, and other means are collected as sample overpressure cases. The method of this invention is used to perform cluster analysis on these cases to obtain sample cluster classifications. Geological experts, based on the known causes of sample overpressure, label each sample cluster classification with its underlying cause (e.g., designating a cluster as "undercompacted type"), and map the feature combinations of the cluster centers to the causes of sample overpressure, creating a mapping relationship for overpressure causes. This mapping relationship is then stored in a knowledge base for future use, thereby enabling the transfer and reuse of experience.

[0052] For example, determining the overpressure genetic result corresponding to each stratigraphic region in the target salt rock formation based on the overpressure mechanism classification cluster and the pre-constructed overpressure genetic mapping relationship includes:

[0053] For each overpressure mechanism classification cluster, cluster center analysis is performed on the overpressure mechanism classification cluster to determine the combination of characteristic parameters of the cluster center;

[0054] The combination of the characteristic parameters is compared with the mapping relationship of overpressure genesis to determine the overpressure genesis result corresponding to each overpressure mechanism classification cluster. Based on the overpressure genesis result corresponding to each overpressure mechanism classification cluster, the overpressure genesis result corresponding to each of the stratigraphic regions is determined.

[0055] Among them, the combination of feature parameters can refer to the combination of features corresponding to the cluster centers of the overpressure mechanism classification cluster.

[0056] In this invention, the center point of each overpressure mechanism cluster is a specific feature vector. By analyzing the combination of feature parameters of the cluster center, it can be geologically identified. Specifically, the feature parameter combination is compared with the overpressure genesis mapping relationship to determine the overpressure genesis result corresponding to each overpressure mechanism cluster. For example, if the feature parameter combination of a cluster center is characterized by "extremely high fluid activity trace intensity and very low tectonic stress index," then this feature parameter combination uniquely identifies the cluster as a "fluid expansion type" overpressure mechanism. Furthermore, based on the overpressure genesis result corresponding to each overpressure mechanism cluster, the overpressure genesis result corresponding to the overpressure mechanism cluster can be determined as the overpressure genesis result corresponding to each stratigraphic region within that overpressure mechanism cluster.

[0057] The technical solution of this invention involves acquiring multi-source geological data corresponding to various stratigraphic regions in the target salt rock formation for overpressure genesis analysis; performing feature fusion processing on the multi-source geological data to construct multi-dimensional feature vector sets for each stratigraphic region; performing unsupervised clustering processing on the multi-dimensional feature vector sets corresponding to each stratigraphic region based on a preset neighborhood adjustment mechanism to obtain various overpressure mechanism classification clusters; and determining the overpressure genesis result corresponding to each stratigraphic region in the target salt rock formation based on the overpressure mechanism classification clusters and the pre-constructed overpressure genesis mapping relationship. This invention overcomes the defect of missing signals from a single data source; it employs a dynamic clustering radius that adapts to lithology and stratigraphic characteristics, significantly improving the identification accuracy and generalization ability of overpressure mechanisms in strongly heterogeneous carbonate rocks. It greatly reduces reliance on expert experience, providing a reliable basis for reservoir evaluation and production prediction of carbonate rocks while ensuring drilling safety.

[0058] Figure 2 This is a flowchart illustrating a method for determining the origin of formation overpressure according to an embodiment of the present invention. Based on the aforementioned embodiments, this embodiment further refines the description by performing unsupervised clustering on the multidimensional feature vector sets corresponding to each formation region. For example... Figure 2 As shown, the method includes:

[0059] S201. Obtain multi-source geological data corresponding to each stratigraphic region in the target salt rock strata whose overpressure genesis is to be investigated.

[0060] S202. Perform feature fusion processing on the multi-source geological data to construct a multi-dimensional feature vector set for each of the stratigraphic regions.

[0061] S203. Based on the seismic velocity gradient and burial depth of the target salt rock formation, determine the clustering neighborhood radius for unsupervised clustering of the target salt rock formation.

[0062] Specifically, based on multi-source geological data of the target salt rock strata, the seismic velocity gradient and burial depth corresponding to the target salt rock strata are determined. Based on the seismic velocity gradient and burial depth, the clustering neighborhood radius for unsupervised clustering of the target salt rock strata is calculated.

[0063] For example, determining the clustering neighborhood radius for unsupervised clustering of the target salt rock formation based on the seismic velocity gradient and burial depth of the target salt rock formation includes:

[0064] ;

[0065] in, α refers to the clustering neighborhood radius, A refers to the seismic velocity gradient, B refers to the stratum burial depth, and α is the lithology fit coefficient, which is dynamically calibrated through the lithology database.

[0066] S204. Based on the clustering neighborhood radius, perform unsupervised clustering on the multidimensional feature vector sets corresponding to each of the stratigraphic regions to obtain each overpressure mechanism classification cluster.

[0067] Specifically, this invention employs the OPTICS clustering algorithm framework, introducing a clustering neighborhood radius to perform unsupervised clustering of the multidimensional feature vector sets corresponding to each stratigraphic region, thereby obtaining various overpressure mechanism classification clusters. By using the clustering neighborhood radius for clustering, unsupervised clustering is transformed into intelligent identification of spatially dispersed but genetically unified geological bodies, solving the long-standing technical problem that traditional methods cannot effectively handle stratigraphic heterogeneity.

[0068] For example, after obtaining each overpressure mechanism classification cluster, the method further includes: using the silhouette coefficient and the Calinski-Harabasz index to evaluate the clustering effect of each overpressure mechanism classification cluster, and determining the clustering evaluation score corresponding to each overpressure mechanism classification cluster; when the clustering evaluation score is lower than a preset score threshold, the overpressure mechanism classification cluster is subjected to a verification process.

[0069] Specifically, after obtaining the overpressure mechanism classification clusters, the silhouette coefficient and Calinski-Harabasz index can be used to evaluate the clustering effect of the overpressure mechanism classification clusters and obtain a clustering evaluation score. The clustering evaluation score is compared with a preset score threshold (which can be set according to the actual situation). If the clustering evaluation score is lower than the preset score threshold, a manual verification process is triggered to review and verify the overpressure mechanism classification clusters.

[0070] S205. Based on the overpressure mechanism classification cluster and the pre-constructed overpressure origin mapping relationship, determine the overpressure origin result corresponding to each stratigraphic region in the target salt rock stratum.

[0071] For example, after determining the overpressure genesis result corresponding to each of the said formation regions, the method further includes:

[0072] Based on the results of the overpressure genesis, a spatial distribution thermogram and probability model of the overpressure mechanism of the target salt rock formation are generated.

[0073] The technical solution of this invention can further spatialize and quantify the results of overpressure formation. By using the Kriging interpolation algorithm to integrate the overpressure formation types and their clustering probabilities of discrete well points with three-dimensional seismic attribute constraints, a heat map of overpressure mechanism types continuously distributed across the entire work area and a corresponding type probability model are generated. This model not only intuitively reveals the distribution patterns of different overpressure mechanisms in the plane and vertical directions, but also quantifies the reliability of the discrimination results through probability values. This provides a data-driven scientific decision-making basis for well location deployment, drilling risk zone division, and pressure prediction model construction, realizing a closed loop from mechanism discrimination to visual decision support.

[0074] For example, after determining the overpressure origin results corresponding to each of the formation regions, the method further includes: optimizing the drilling fluid density window based on the overpressure origin results during the drilling engineering design stage; and implementing pressure control and production enhancement measures for fluid expansion type overpressure zones based on the overpressure origin results during the reservoir stimulation stage.

[0075] After determining the causes of overpressure in each stratigraphic region, a closed-loop decision-making chain is established from geological diagnosis to engineering implementation. During the drilling engineering design phase, formation pressure and fracture pressure models are dynamically coupled based on the specific overpressure type, and the drilling fluid density safety window for each well section is optimized differentially to precisely balance well control safety and reservoir protection. During the reservoir stimulation phase, for identified high-potential fluid-expanding overpressure zones, targeted production enhancement processes such as controlled-pressure acidizing or fracture-network fracturing are intelligently recommended and designed. Real-time pressure control ensures effective stimulation while preventing fracture runaway. This process directly translates mechanistic understanding into executable engineering parameters, significantly improving drilling safety and reservoir stimulation effectiveness.

[0076] Figure 3 This is a schematic diagram of a device for determining the origin of formation overpressure, provided in an embodiment of the present invention. Figure 3 As shown, the device includes:

[0077] Geological data acquisition module 301 is used to acquire multi-source geological data corresponding to various stratigraphic regions in the target salt rock strata to be subjected to overpressure genesis;

[0078] The feature vector determination module 302 is used to perform feature fusion processing on the multi-source geological data to construct a multi-dimensional feature vector set for each of the stratigraphic regions.

[0079] The feature vector clustering module 303 is used to perform unsupervised clustering processing on the multidimensional feature vector sets corresponding to each of the stratigraphic regions based on a preset neighborhood adjustment mechanism to obtain each overpressure mechanism classification cluster. The preset neighborhood adjustment mechanism dynamically adjusts the clustering radius according to the burial depth and seismic velocity of the target salt rock stratum.

[0080] The overpressure origin determination module 304 is used to determine the overpressure origin result corresponding to each stratigraphic region in the target salt rock stratum based on the overpressure mechanism classification cluster and the pre-constructed overpressure origin mapping relationship.

[0081] The technical solution of this invention involves acquiring multi-source geological data corresponding to various stratigraphic regions in the target salt rock formation for overpressure genesis analysis; performing feature fusion processing on the multi-source geological data to construct multi-dimensional feature vector sets for each stratigraphic region; performing unsupervised clustering processing on the multi-dimensional feature vector sets corresponding to each stratigraphic region based on a preset neighborhood adjustment mechanism to obtain various overpressure mechanism classification clusters; and determining the overpressure genesis result corresponding to each stratigraphic region in the target salt rock formation based on the overpressure mechanism classification clusters and the pre-constructed overpressure genesis mapping relationship. This invention overcomes the defect of missing signals from a single data source; it employs a dynamic clustering radius that adapts to lithology and stratigraphic characteristics, significantly improving the identification accuracy and generalization ability of overpressure mechanisms in strongly heterogeneous carbonate rocks. It greatly reduces reliance on expert experience, providing a reliable basis for reservoir evaluation and production prediction of carbonate rocks while ensuring drilling safety.

[0082] Optionally, the feature vector determination module 302 is also used for:

[0083] Before performing feature fusion processing on the multi-source geological data, the multi-source geological data is preprocessed, including but not limited to data cleaning, normalization, missing value imputation, and noise filtering.

[0084] Optionally, the feature vector determination module 302 further includes:

[0085] An initial feature determination unit is used to perform feature extraction processing on the multi-source geological data to obtain multiple initial features;

[0086] A fusion feature construction unit is used to align and integrate multiple initial features to obtain an initial fusion feature vector;

[0087] The multidimensional vector construction unit is used to perform dimensionality reduction and fusion processing on the initial fused feature vector based on a preset joint algorithm to obtain a multidimensional feature vector set.

[0088] Optional, initial feature determination unit, specifically used for:

[0089] Pore ​​pressure parameters, sonic time difference constants, and resistivity abrupt change characteristics are extracted from the well logging data in the multi-source geological data. Velocity spectrum inversion and overpressure body and fluid activity traces are extracted from the well logging data in the multi-source geological data.

[0090] Optionally, the feature vector clustering module 303 includes:

[0091] The clustering radius determination unit is used to determine the clustering neighborhood radius of the target salt rock formation for unsupervised clustering based on the seismic velocity gradient and burial depth of the target salt rock formation.

[0092] The feature vector clustering unit is used to perform unsupervised clustering of the multidimensional feature vector sets corresponding to each of the stratigraphic regions based on the clustering neighborhood radius, so as to obtain each overpressure mechanism classification cluster.

[0093] Optionally, the cluster radius determination unit is specifically used for:

[0094] ;

[0095] in, α refers to the clustering neighborhood radius, A refers to the seismic velocity gradient, B refers to the stratum burial depth, and α is the lithology fit coefficient, which is dynamically calibrated through the lithology database.

[0096] Optionally, the overpressure cause determination module 304 is specifically used for:

[0097] For each overpressure mechanism classification cluster, cluster center analysis is performed on the overpressure mechanism classification cluster to determine the combination of characteristic parameters of the cluster center;

[0098] The combination of the characteristic parameters is compared with the mapping relationship of overpressure genesis to determine the overpressure genesis result corresponding to each overpressure mechanism classification cluster. Based on the overpressure genesis result corresponding to each overpressure mechanism classification cluster, the overpressure genesis result corresponding to each of the stratigraphic regions is determined.

[0099] Optionally, the overpressure cause types include at least fluid expansion type, tectonic stress type, undercompaction type and composite type.

[0100] Optionally, the overpressure cause determination module 304 further includes a mapping relationship construction unit. The mapping relationship construction unit is used for:

[0101] Obtain sample overpressure cases, along with the corresponding sample geological data and the causes of sample overpressure;

[0102] Based on the sample cluster radius corresponding to the sample overpressure case, the sample overpressure cases are clustered to obtain sample cluster classification clusters;

[0103] The overpressure cause mapping relationship is constructed by performing a relation mapping process based on the cluster center characteristics of the sample clusters and the causes of overpressure in the samples.

[0104] Optionally, the feature vector clustering module 303 is also used for:

[0105] After obtaining each overpressure mechanism classification cluster, the silhouette coefficient and Calinski-Harabasz index are used to evaluate the clustering effect of each overpressure mechanism classification cluster and determine the clustering evaluation score corresponding to each overpressure mechanism classification cluster.

[0106] If the cluster evaluation score is lower than the preset score threshold, the overpressure mechanism classification cluster will be subject to verification.

[0107] Optionally, the overpressure cause determination module 304 is also used for:

[0108] After determining the overpressure genesis results corresponding to each of the aforementioned stratigraphic regions, the process also includes:

[0109] Based on the results of the overpressure genesis, a spatial distribution thermogram and probability model of the overpressure mechanism of the target salt rock formation are generated.

[0110] Optionally, the overpressure cause determination module 304 is also used for:

[0111] After determining the overpressure genesis results for each of the aforementioned formation regions, the drilling fluid density window is optimized based on the overpressure genesis results during the drilling engineering design phase.

[0112] During the reservoir stimulation stage, pressure control and production enhancement measures are implemented for fluid expansion-type overpressured reservoirs based on the results of the overpressure formation.

[0113] The formation overpressure origin determination device provided in the embodiments of the present invention can execute the formation overpressure origin determination method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0114] Figure 4 A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0115] like Figure 4As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0116] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0117] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as methods for determining the genesis of formation overpressure.

[0118] In some embodiments, the formation overpressure origin determination method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the formation overpressure origin determination method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the formation overpressure origin determination method by any other suitable means (e.g., by means of firmware).

[0119] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0120] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0121] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0122] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0123] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0124] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through a communication network. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0125] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0126] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for determining the genesis of formation overpressure, characterized in that, include: Acquire multi-source geological data corresponding to various stratigraphic regions in the target salt rock strata whose overcompression genesis is to be investigated; The multi-source geological data are subjected to feature fusion processing to construct a multi-dimensional feature vector set for each of the stratigraphic regions. Based on a preset neighborhood adjustment mechanism, the multidimensional feature vector sets corresponding to each stratigraphic region are subjected to unsupervised clustering to obtain each overpressure mechanism classification cluster. The preset neighborhood adjustment mechanism dynamically adjusts the clustering radius according to the burial depth and seismic velocity of the target salt rock stratum. Based on the overpressure mechanism classification cluster and the pre-constructed overpressure genesis mapping relationship, the overpressure genesis result corresponding to each stratigraphic region in the target salt rock formation is determined.

2. The method according to claim 1, characterized in that, Before performing feature fusion processing on the multi-source geological data, the method further includes: performing data preprocessing on the multi-source geological data, wherein the preprocessing includes, but is not limited to, data cleaning, normalization, missing value imputation, and noise filtering.

3. The method according to claim 1, characterized in that, The step of performing feature fusion processing on the multi-source geological data to construct a multi-dimensional feature vector set includes: The multi-source geological data is subjected to feature extraction processing to obtain multiple initial features; The initial features are aligned and integrated to obtain an initial fused feature vector; Based on a preset joint algorithm, the initial fused feature vector is subjected to dimensionality reduction and fusion processing to obtain a multidimensional feature vector set.

4. The method according to claim 3, characterized in that, The process of extracting features from the multi-source geological data yields multiple initial features, including: Pore ​​pressure parameters, sonic time difference constants, and resistivity abrupt change characteristics are extracted from the well logging data in the multi-source geological data. Velocity spectrum inversion and overpressure body and fluid activity traces are extracted from the well logging data in the multi-source geological data.

5. The method according to claim 1, characterized in that, The method, based on a preset neighborhood adjustment mechanism, performs unsupervised clustering on the multidimensional feature vector sets corresponding to each of the aforementioned stratigraphic regions to obtain various overpressure mechanism classification clusters, including: Based on the seismic velocity gradient and burial depth of the target salt rock formation, the clustering neighborhood radius for unsupervised clustering of the target salt rock formation is determined. Based on the clustering neighborhood radius, the multidimensional feature vector sets corresponding to each of the stratigraphic regions are subjected to unsupervised clustering to obtain each overpressure mechanism classification cluster.

6. The method according to claim 5, characterized in that, The step of determining the clustering neighborhood radius for unsupervised clustering of the target salt rock strata based on the seismic velocity gradient and burial depth of the target salt rock strata includes: ； in, α refers to the clustering neighborhood radius, A refers to the seismic velocity gradient, B refers to the stratum burial depth, and α is the lithology fit coefficient, which is dynamically calibrated through the lithology database.

7. The method according to claim 1, characterized in that, The step of determining the overpressure origin of each stratigraphic region in the target salt rock formation based on the overpressure mechanism classification cluster and the pre-constructed overpressure origin mapping relationship includes: For each overpressure mechanism classification cluster, cluster center analysis is performed on the overpressure mechanism classification cluster to determine the combination of characteristic parameters of the cluster center; The combination of the characteristic parameters is compared with the mapping relationship of overpressure genesis to determine the overpressure genesis result corresponding to each overpressure mechanism classification cluster. Based on the overpressure genesis result corresponding to each overpressure mechanism classification cluster, the overpressure genesis result corresponding to each of the stratigraphic regions is determined.

8. The method according to claim 1, characterized in that, The types of overpressure formation include at least fluid expansion type, tectonic stress type, undercompaction type, and composite type.

9. The method according to claim 1, characterized in that, The process of constructing the mapping relationship of overpressure causes includes: Obtain sample overpressure cases, along with the corresponding sample geological data and the causes of sample overpressure; Based on the sample cluster radius corresponding to the sample overpressure case, the sample overpressure cases are clustered to obtain sample cluster classification clusters; The overpressure cause mapping relationship is constructed by performing a relation mapping process based on the cluster center characteristics of the sample clusters and the causes of overpressure in the samples.

10. The method according to claim 1, characterized in that, After obtaining the various overpressure mechanism classification clusters, the following are also included: The silhouette coefficient and the Calinski-Harabasz index were used to evaluate the clustering effect of each overpressure mechanism classification cluster, and the clustering evaluation score corresponding to each overpressure mechanism classification cluster was determined. If the cluster evaluation score is lower than the preset score threshold, the overpressure mechanism classification cluster will be subject to verification.

11. The method according to claim 1, characterized in that, After determining the overpressure genesis results corresponding to each of the aforementioned stratigraphic regions, the process also includes: Based on the results of the overpressure genesis, a spatial distribution thermogram and probability model of the overpressure mechanism of the target salt rock formation are generated.

12. The method according to claim 1, characterized in that, After determining the overpressure genesis results corresponding to each of the aforementioned stratigraphic regions, the process also includes: During the drilling engineering design phase, the drilling fluid density window is optimized based on the results of the overpressure causes. During the reservoir stimulation stage, pressure control and production enhancement measures are implemented for fluid expansion-type overpressured reservoirs based on the results of the overpressure formation.

13. A device for determining the origin of formation overpressure, characterized in that, include: The geological data acquisition module is used to acquire multi-source geological data corresponding to various stratigraphic regions in the target salt rock strata to be studied for overpressure genesis. The feature vector determination module is used to perform feature fusion processing on the multi-source geological data to construct a multi-dimensional feature vector set for each of the stratigraphic regions. The feature vector clustering module is used to perform unsupervised clustering processing on the multidimensional feature vector sets corresponding to each of the stratigraphic regions based on a preset neighborhood adjustment mechanism to obtain each overpressure mechanism classification cluster. The preset neighborhood adjustment mechanism dynamically adjusts the clustering radius according to the burial depth and seismic velocity of the target salt rock strata. The overpressure origin determination module is used to determine the overpressure origin result corresponding to each stratigraphic region in the target salt rock formation based on the overpressure mechanism classification cluster and the pre-constructed overpressure origin mapping relationship.

14. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, which is executed by the at least one processor to enable the at least one processor to perform the method for determining the origin of formation overpressure as described in any one of claims 1-12.

15. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the method for determining the origin of formation overpressure as described in any one of claims 1-12.

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