Reservoir fluid identification method, device and equipment and storage medium

Abstract

The invention provides a reservoir fluid identification method, device and equipment and a storage medium, and the method comprises the steps: obtaining geological information data of a research area, building an initial geoelectric model, and determining the top and bottom depths of each target stratum; acquiring wide-area apparent resistivity data of all stratums to obtain an overall wide-area apparent resistivity profile, and deleting data of each target stratum in the overall wide-area apparent resistivity profile to obtain an original wide-area apparent resistivity profile; based on a frequency spectrum encryption technology and a wide area electromagnetic method, obtaining wide area apparent resistivity data of each target layer; according to the initial geoelectric model, the undisturbed wide area apparent resistivity profile and the wide area apparent resistivity data of each target stratum, taking the initial geoelectric model as a current geoelectric model, executing a multi-target stratum progressive inversion strategy, and obtaining a target inversion apparent resistivity profile containing all target stratum information; and fluid identification and division are carried out according to the target inversion apparent resistivity profile. According to the method, the speed and the precision of fluid identification can be improved through multi-objective layer progressive inversion.

Description

Technical Field

[0001] This application relates to the field of oil and gas exploration and development technology, and in particular to a reservoir fluid identification method, apparatus, equipment and storage medium. Background Technology

[0002] Currently, as my country's oil and gas exploration and development expands into deeper and more complex lithological areas, traditional reservoir fluid identification technologies face severe challenges. Existing methods mostly rely on geoelectric models constructed from single target layers for inversion interpretation. While these methods have achieved some success in fluid identification in reservoirs with simple structural zones, they exhibit significant limitations when dealing with reservoirs in complex geological conditions where multiple oil and gas-bearing strata are superimposed. Insufficient vertical resolution in the stratigraphic framework constructed from seismic data can lead to stratigraphic calibration errors, and the inherent ambiguity of wide-area electromagnetic inversion can cause error accumulation in multi-strata environments, significantly reducing the reliability of deep fluid identification results. Summary of the Invention

[0003] This application aims to propose a reservoir fluid identification method, apparatus, device, and storage medium, which can improve the speed and accuracy of fluid identification through multi-target layer progressive inversion.

[0004] The reservoir fluid identification method according to a first aspect embodiment of this application includes: Obtain geological information data for the study area, including seismic data and well logging data; Based on the seismic data and the well logging data, an initial geoelectric model is established; Based on the seismic data and the well logging data, determine the top and bottom depths of each target layer in the study area; there are n target layers; Based on the wide-area electromagnetic method, the wide-area apparent resistivity data of all strata in the study area are obtained to obtain the overall wide-area apparent resistivity profile. Based on the top and bottom depths of each target layer, the data of each target layer in the overall wide-area apparent resistivity profile are deleted to obtain the original wide-area apparent resistivity profile. Based on spectrum encryption technology and the wide-area electromagnetic method, the wide-area apparent resistivity data of each of the target layers is obtained; Based on the initial geoelectric model, the original wide-area apparent resistivity profile, and the wide-area apparent resistivity data of each of the target layers, the initial geoelectric model is used as the current geoelectric model, and a multi-target layer progressive inversion strategy is executed to obtain a target inversion apparent resistivity profile containing information of all target layers. Based on the target inverted apparent resistivity profile, fluid identification and classification are performed; The multi-purpose layer progressive inversion strategy includes: Select the i-th target layer as the current target layer; Based on the wide-area apparent resistivity data of the current target layer and the current geoelectric model, a constrained inversion is performed to obtain the inverted apparent resistivity profile corresponding to the current target layer. Fill the original wide-area apparent resistivity profile with the inverted apparent resistivity profile corresponding to the current target layer to obtain an intermediate inverted apparent resistivity profile containing information about the current target layer. Based on the intermediate inverted apparent resistivity profile, stratigraphic correction is performed according to the seismic data and the well logging data to obtain an intermediate geoelectric model containing the current target layer information, and the intermediate geoelectric model is used as the current geoelectric model. If i is less than or equal to n-1, assign i+1 to i and return to the step of selecting the i-th target layer as the current target layer. With all the target layers selected, the intermediate inverted apparent resistivity profile is determined as the target inverted apparent resistivity profile.

[0005] According to some embodiments of this application, establishing an initial geoelectric model based on the seismic data and the well logging data includes: Based on the earthquake data, obtain the stratigraphic information of the study area; Based on the well logging data, the well logging resistivity of each formation was obtained; Based on the stratigraphic layering information and the well logging resistivity of each stratigraphic layer, a stratigraphic framework is constructed, and the initial geoelectric model is established.

[0006] According to some embodiments of this application, determining the top and bottom depths of each target layer based on the seismic data and the well logging data includes: Based on the seismic data, stratigraphic information of the study area is obtained, and the stratigraphic information is corrected and realigned based on the well logging data to obtain the top and bottom depths of each stratum in the study area. The top and bottom depths of each target layer are obtained based on the top and bottom depths of each stratum within the study area.

[0007] According to some embodiments of this application, obtaining wide-area apparent resistivity data for each of the target layers based on spectrum encryption technology and the wide-area electromagnetic method includes: Based on the skin depth formula, the wide-area emission frequency range of each target layer is obtained according to the top and bottom depths of each target layer; Based on spectrum encryption technology, frequency point encryption is performed on each of the target layers within the corresponding wide-area transmission frequency range; Based on the wide-area electromagnetic method, the wide-area apparent resistivity data of each target layer are obtained by encrypted acquisition of wide-area electromagnetic devices.

[0008] According to some embodiments of this application, the constrained inversion based on the wide-area apparent resistivity data of the current target layer and the current geoelectric model includes: Based on the constrained inversion algorithm, constrained inversion is performed according to the wide-area apparent resistivity data of the current target layer and the current geoelectric model.

[0009] According to some embodiments of this application, the constraint inversion algorithm includes the nonlinear conjugate gradient method, the lateral constraint inversion method, or the longitudinal constraint inversion method.

[0010] According to some embodiments of this application, the step of performing fluid identification and segmentation based on the target inverted apparent resistivity profile includes: Based on the geological information, a wide-area fluid identification standard is obtained, wherein the wide-area fluid identification standard identifies and classifies the fluids within the target layer based on the wide-area apparent resistivity; Based on the wide-area fluid identification standard, the target inverted apparent resistivity profile is divided into fluid identification sections to obtain the fluid identification results.

[0011] A reservoir fluid identification device according to a second aspect embodiment of this application includes: The data acquisition module is used to acquire geological information data of the study area, including seismic data and well logging data; The initial geoelectric model establishment module is used to establish an initial geoelectric model based on the seismic data and the well logging data; The top and bottom depth determination module is used to determine the top and bottom depths of each target layer in the study area based on the seismic data and the well logging data; there are n target layers; The first module is used to obtain wide-area apparent resistivity data of all strata in the study area based on the wide-area electromagnetic method, and to obtain an overall wide-area apparent resistivity profile. The second obtaining module is used to delete the data of each of the target layers in the overall wide-area apparent resistivity profile according to the top and bottom depths of each of the target layers, so as to obtain the original wide-area apparent resistivity profile. The acquisition module is used to acquire wide-area apparent resistivity data of each of the target layers based on spectrum encryption technology and the wide-area electromagnetic method. The progressive inversion module is used to take the initial geoelectric model as the current geoelectric model, execute a multi-target-layer progressive inversion strategy, and obtain a target inversion apparent resistivity profile containing information of all target layers, based on the initial geoelectric model, the original wide-area apparent resistivity profile, and the wide-area apparent resistivity data of each target layer. The fluid identification module is used to identify and classify fluids based on the apparent resistivity profile retrieved from the target. The multi-purpose layer progressive inversion strategy includes: Select the i-th target layer as the current target layer; Based on the wide-area apparent resistivity data of the current target layer and the current geoelectric model, a constrained inversion is performed to obtain the inverted apparent resistivity profile corresponding to the current target layer. Fill the original wide-area apparent resistivity profile with the inverted apparent resistivity profile corresponding to the current target layer to obtain an intermediate inverted apparent resistivity profile containing information about the current target layer. Based on the intermediate inverted apparent resistivity profile, stratigraphic correction is performed according to the seismic data and the well logging data to obtain an intermediate geoelectric model containing the current target layer information, and the intermediate geoelectric model is used as the current geoelectric model. If i is less than or equal to n-1, assign i+1 to i and return to the step of selecting the i-th target layer as the current target layer. With all the target layers selected, the intermediate inverted apparent resistivity profile is determined as the target inverted apparent resistivity profile.

[0012] An electronic device according to a third aspect of this application includes a processor and a memory, the memory storing a program or instructions executable on the processor, the program or instructions being executed by the processor to implement the steps of the reservoir fluid identification method as described in any of the first aspects of the present application.

[0013] A computer-readable storage medium according to a fourth aspect embodiment of the present application stores computer-executable instructions for performing the reservoir fluid identification method as described in the first aspect embodiment above.

[0014] In this embodiment of the application, when faced with complex geological conditions where multiple sets of oil and gas-bearing strata are superimposed and developed, i.e. when fluid identification is required for multiple target layers, the inversion of each layer is not independent. Instead, the geoelectric model is dynamically optimized through progressive correction of multiple target layers. The model of the next layer is corrected by the inversion results of the previous target layer, and a cascade feedback mechanism is established. Finally, a closed-loop optimization process for joint inversion of all target layers is realized, which effectively improves the inversion accuracy and speed, significantly enhances the vertical resolution, and thus improves the speed and accuracy of fluid identification.

[0015] Other features and advantages of this application will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing this application. Attached Figure Description

[0016] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which: Figure 1 This is a schematic flowchart of an embodiment of the reservoir fluid identification method of this application; Figure 2 This is a schematic diagram of the initial geoelectric model of an embodiment of the reservoir fluid identification method of this application; Figure 3 This is a schematic diagram of the original wide-area apparent resistivity profile of an embodiment of the reservoir fluid identification method of this application; Figure 4 This is a schematic diagram of the target inversion apparent resistivity profile of an embodiment of the reservoir fluid identification method of this application; Figure 5 This is a schematic diagram of an embodiment of the reservoir fluid identification device of this application; Figure 6 This is a schematic diagram of the hardware structure of an embodiment of the electronic device of this application. Detailed Implementation

[0017] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0018] In the description of this application, the use of terms such as "first," "second," etc., is for the purpose of distinguishing technical features only and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated or the order of the technical features indicated.

[0019] In the description of this application, it should be understood that the orientation descriptions, such as up, down, etc., are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application.

[0020] In the description of this application, it should be noted that, unless otherwise explicitly defined, terms such as "setup," "installation," and "connection" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this application in conjunction with the specific content of the technical solution.

[0021] The technical solution of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are some embodiments of this application, not all embodiments.

[0022] To facilitate a better understanding of the solutions in the embodiments of this application, the relevant technologies will be introduced first below.

[0023] In the field of oil and gas exploration and development, target reservoirs often contain fluids of different properties, such as oil, natural gas, formation water, and artificially injected fracturing fluids. These multiple fluids coexist in the reservoir and, due to gravity or artificial development measures, tend to accumulate in vertical stratification and horizontal block formations. When deploying oil and gas exploration and production wells, it is necessary to select sites in oil and gas-rich areas as much as possible to improve drilling success rates.

[0024] The resistivity of oil, gas, and water in reservoirs exhibits significant differences, which forms the theoretical basis for this method of reservoir fluid identification. Generally, oil and natural gas are essentially non-conductive, exhibiting high to extremely high resistivity; formation water and artificially injected water-based fracturing fluids are conductors, exhibiting low to extremely low resistivity; while the resistivity of formations without oil, gas, or water typically falls between these two.

[0025] Figure 1 This is a schematic flowchart of an embodiment of the reservoir fluid identification method of this application; Figure 2 This is a schematic diagram of the initial geoelectric model of an embodiment of the reservoir fluid identification method of this application; Figure 3 This is a schematic diagram of the original wide-area apparent resistivity profile of an embodiment of the reservoir fluid identification method of this application; Figure 4 This is a schematic diagram of the target inversion apparent resistivity profile of an embodiment of the reservoir fluid identification method of this application; Figure 5 This is a schematic diagram of an embodiment of the reservoir fluid identification device of this application; Figure 6 This is a schematic diagram of the hardware structure of an embodiment of the electronic device of this application.

[0026] See below. Figure 1 The embodiments of this application will be further described below. This application proposes a reservoir fluid identification method, which includes the following steps: Obtain geological information data for the study area, including seismic data and well logging data; An initial geoelectric model was established based on seismic and well logging data; Based on seismic and well logging data, the top and bottom depths of each target layer in the study area are determined; there are n target layers. Based on the wide-area electromagnetic method, wide-area apparent resistivity data of all strata in the study area were obtained, and an overall wide-area apparent resistivity profile was obtained. Based on the top and bottom depths of each target layer, the data of each target layer in the overall wide-area apparent resistivity profile are deleted to obtain the original wide-area apparent resistivity profile. Based on spectrum encryption technology and wide-area electromagnetic method, wide-area apparent resistivity data of each target layer are obtained; Based on the initial geoelectric model, the original wide-area apparent resistivity profile, and the wide-area apparent resistivity data of each target layer, the initial geoelectric model is used as the current geoelectric model. A multi-target layer progressive inversion strategy is executed to obtain the target inversion apparent resistivity profile containing information of all target layers. Fluid identification and classification are performed based on the target inverted apparent resistivity profile; Among them, the multi-purpose layered progressive inversion strategy includes: Select the i-th target layer as the current target layer; Based on the wide-area apparent resistivity data of the current target layer and the current geoelectric model, a constrained inversion is performed to obtain the inverted apparent resistivity profile corresponding to the current target layer. Fill the original wide-area apparent resistivity profile with the inverted apparent resistivity profile corresponding to the current target layer to obtain the intermediate inverted apparent resistivity profile containing the information of the current target layer. Based on the intermediate inverted apparent resistivity profile, stratigraphic correction is performed according to seismic and well logging data to obtain an intermediate geoelectric model containing information of the current target layer. The intermediate geoelectric model is then used as the current geoelectric model. If i is less than or equal to n-1, assign i+1 to i and return to select the i-th destination layer as the current destination layer. With all target layers selected, the intermediate inverted apparent resistivity profile is determined as the target inverted apparent resistivity profile.

[0027] In this embodiment of the application, when faced with complex geological conditions where multiple sets of oil and gas-bearing strata are superimposed and developed, i.e. when fluid identification is required for multiple target layers, the inversion of each layer is not independent. Instead, the geoelectric model is dynamically optimized through progressive correction of multiple target layers. The model of the next layer is corrected by the inversion results of the previous target layer, and a cascade feedback mechanism is established. Finally, a closed-loop optimization process for joint inversion of all target layers is realized, which effectively improves the inversion accuracy and speed, significantly enhances the vertical resolution, and thus improves the speed and accuracy of fluid identification.

[0028] The aforementioned geological information includes seismic data and well logging data. It is understandable that detailed seismic and well logging data are available in the more developed study areas. Seismic data provides information on the structural characteristics of the study area and the stratigraphic stratification from deep to shallow. Well logging data primarily includes logging measurements of formation resistivity, containing at least one of the following types: conventional resistivity logging, lateral logging, and induction logging. Conventional resistivity logging information is the primary source, with Rt representing formation resistivity.

[0029] In some implementations, an initial geoelectric model is established based on seismic and well logging data, including: Based on seismic data, obtain stratigraphic information for the study area; Based on well logging data, the well logging resistivity of each formation is obtained; Based on the stratigraphic information and the well logging resistivity of each stratigraphic layer, a stratigraphic framework is constructed, and an initial geoelectric model is established.

[0030] In this embodiment, by combining seismic and well logging data, a simple, albeit low-resolution, initial geoelectric model can be established. Seismic data clearly presents the stratigraphic layering of the study area, revealing the distribution and structure of different strata; well logging data accurately obtains the resistivity information of each stratum. Using the stratigraphic layering information as a framework, a stratigraphic grid is constructed, and the corresponding well logging resistivity data for each stratum is filled in to establish the initial geoelectric model, which simply displays the resistivity distribution of each stratum within the study area. This initial geoelectric model serves as the basis for subsequent multi-objective layer-by-layer progressive inversion, providing an initial reference for the subsequent inversion process, allowing the inversion to proceed on a relatively reasonable foundation.

[0031] The above-mentioned acquisition of stratigraphic information in the study area based on seismic data can be used to obtain the vertical stratigraphic results of each stratum, i.e., the top and bottom depths of each set of strata.

[0032] Specifically, based on the stratigraphic information, the well logging resistivity information of each stratigraphic unit is filled in to obtain the initial geoelectric model K1, as referenced. Figure 2 As shown. It is important to note that the well logging resistivity of each formation is often substituted as an average value. Within the study area, the average resistivity of each formation is determined by its lithology and the fluids it contains, and generally does not vary significantly. If local thin-layer anomalies exist, they should be discarded for overall consideration, or targeted modeling should be performed for local studies. Understandably, the initial geoelectric model K1 is structurally simple, i.e., a horizontally homogeneous layered model. The resistivity of each formation is averaged within the model, with no local high or low values. The formation is horizontally layered, without local uplifts, depressions, faults, or torsion.

[0033] In some implementations, the top and bottom depths of each target layer are determined based on seismic and well logging data, including: Based on seismic data, stratigraphic information of the study area is obtained, and the stratigraphic information is corrected and realigned based on well logging data to obtain the top and bottom depths of each stratum in the study area. The top and bottom depths of each target layer are obtained based on the top and bottom depths of each stratum within the study area.

[0034] In this embodiment, since the stratigraphic information obtained from seismic data often contains certain errors, while well logging data can provide more accurate stratigraphic information, using well logging data to correct the stratigraphic information can yield more accurate top and bottom depths of each stratum within the study area. Based on these accurate top and bottom depths, the top and bottom depths of each target layer can be further determined.

[0035] Understandably, stratigraphic information obtained from seismic data is a preliminary result. Artificial interpretation of seismic profiles reflects reasonable structural development and stratigraphic characteristics, roughly showing the stratigraphic layering and determining the top and bottom depths of strata. However, it may contain some inaccuracies. Well logging data, on the other hand, is obtained by directly measuring various physical parameters of the strata in the well, resulting in higher accuracy. By combining well logging data with seismic data, the top and bottom depths of each stratum interpreted seismically can be obtained. Correction using well logging data can eliminate errors in the seismic data, making the determination of top and bottom depths of strata more accurate.

[0036] In addition, it should be noted that the target layer in this application is only roughly determined based on the lithology, and the specific top and bottom depths cannot be known in advance. Therefore, it is necessary to obtain the specific top and bottom depths of each stratum in the current study area in order to determine the specific top and bottom depths of the target layer.

[0037] There are n target layers, where n is an integer greater than or equal to 2. Specifically, on the seismic profile, each target layer is marked from shallowest to deepest, and is denoted as F1, F2, F3, ..., Fn. Each target layer has its own top depth and bottom depth, i.e., top and bottom depths.

[0038] refer to Figure 3 As shown, in some implementations, wide-area apparent resistivity data of all strata in the study area are obtained based on the wide-area electromagnetic method to obtain an overall wide-area apparent resistivity profile. Based on the top and bottom depths of each target layer, the data of each target layer in the overall wide-area apparent resistivity profile are deleted to obtain the original wide-area apparent resistivity profile.

[0039] In this embodiment, wide-area electromagnetic methods are used to measure all strata within the study area, obtaining wide-area apparent resistivity data for all strata. This yields a comprehensive wide-area apparent resistivity profile, which contains wide-area apparent resistivity information for all strata within the study area, providing a simple yet comprehensive dataset. Furthermore, by removing the data for each target layer from the comprehensive wide-area apparent resistivity profile based on the top and bottom depths of each target layer, the original wide-area apparent resistivity profile is obtained. (Refer to...) Figure 3 As shown in the figure, the blank areas represent the depths of each target layer. This profile is used to gradually fill in the detailed apparent resistivity profiles of the target layers in subsequent multi-target layer progressive inversion strategies.

[0040] In some implementations, wide-area apparent resistivity data for each target layer is obtained based on spectrum encryption technology and wide-area electromagnetic methods, including: Based on the skin depth formula, the wide-area transmission frequency range of each target layer is obtained according to the top and bottom depths of each target layer; Based on spectrum encryption technology, frequency point encryption is performed on each target layer within the corresponding wide-area transmission frequency range; Based on the wide-area electromagnetic method, wide-area apparent resistivity data of each target layer are obtained from encrypted acquisition by a wide-area electromagnetic device.

[0041] In this embodiment, by combining spectrum encryption technology and wide-area electromagnetic method, the amount of data obtained is increased by encrypting the acquisition of the target layer, and more reliable and accurate wide-area apparent resistivity data of each target layer can be obtained. Its vertical resolution is significantly improved compared with unencrypted data, and it is more sensitive to resistivity changes caused by fluid changes.

[0042] It should be noted that, after knowing the top and bottom depths of each target layer, the extrapolated top and bottom depths are usually extended by 50m to obtain the design range for the densification frequency. For example, if the top depth of the F1 stratum is -1500m, extending it outward by 50m means extending it to a shallower depth of 50m, which is -1450m; if the bottom depth of the F1 stratum is -1700m, extending it outward by 50m means extending it to a deeper depth of 50m, which is -1750m.

[0043] Therefore, it can be understood that for the aforementioned original wide-area apparent resistivity profile—that is, the wide-area apparent resistivity profile formed after obtaining the wide-area apparent resistivity including all strata, and then deleting the data of each target layer from this profile—it is important to note that the deletion range for each target layer's data is defined by extrapolating the top and bottom depths of each target layer by 50 meters. It can be understood that this extrapolation depth, i.e., 50 meters, can be adjusted according to different actual geological conditions, and can be reduced or increased.

[0044] The aforementioned spectrum encryption technology can encrypt the transmission frequency and increase the number of collected frequency points from 1-3 (unencrypted) to 10-30, thus increasing the data volume tenfold and effectively improving the wide-area electromagnetic longitudinal resolution of the target layer. This spectrum encryption technology is based on the skin depth formula, which is shown below: ; in, The coefficient is 355-357, and D is the detection depth in meters. Apparent resistivity, in Ω·m, represents the combined resistivity of multiple strata within a certain depth range from the Earth's surface. f represents the transmitted signal frequency, in Hz.

[0045] Based on the top and bottom depths determined by extrapolation of the above target layers, the frequency range of wide-area electromagnetic emission can be approximately estimated.

[0046] It should be noted that the skin depth formula here is an approximation. Typically, k is taken as 356, but it can be adjusted according to the actual situation. Generally, the transmission frequency range of wide-area electromagnetic devices can be from 0.0020Hz to 15728.6Hz. The transmission frequency can be adjusted according to the actual situation. Generally, the higher the frequency, the shallower the detection depth; conversely, the higher the frequency, the deeper the detection depth.

[0047] The aforementioned wide-area electromagnetic method involves collecting wide-area electromagnetic wave data of a specified area using wide-area electromagnetic equipment. This data is then processed through noise reduction, signal-to-noise ratio enhancement, Fourier transform, and calculation to obtain wide-area apparent resistivity data for each layer within the specified area.

[0048] The aforementioned wide-area electromagnetic equipment may include wide-area high-power transmitters and wide-area high-precision receivers. The aforementioned apparent resistivity data... It can be calculated using the following formula: ; ; in, For the electric field of wide-area electromagnetic devices Quantity, The distance between adjacent receiving points of a wide-area electromagnetic device. The magnitude of the harmonic current transmitted by the wide-area electromagnetic device. For the observation device of the wide-area electromagnetic equipment, Let be the electromagnetic effect coefficient, dL be the distance of the electric dipole source of the wide-area electromagnetic device, and r be the transmit / receive distance of the wide-area electromagnetic device. θ is the azimuth angle, k is the wave number, and i is the imaginary unit.

[0049] In some implementations, constrained inversion is performed based on wide-area apparent resistivity data of the current target layer and the current geoelectric model, including: Based on the constrained inversion algorithm, constrained inversion is performed using the wide-area apparent resistivity data of the current target layer and the current geoelectric model.

[0050] In this embodiment, by using a constrained inversion algorithm, appropriate constraints can be added during the inversion process, making the inversion results more consistent with the actual geological conditions. In practical applications, the selection of the constrained inversion algorithm and the setting of its parameters need to be determined based on the specific geological conditions and characteristics to achieve the best inversion results. In some implementations, the constraint inversion algorithm includes the nonlinear conjugate gradient method, the lateral constraint inversion method, or the longitudinal constraint inversion method.

[0051] The aforementioned Nonlinear Conjugate Gradient Method (NLCG) is an efficient inversion algorithm suitable for large-scale 3D inversion problems. It finds the minimum of the objective function iteratively, eliminating the need to explicitly store and compute a large Jacobian (sensitivity) matrix, thus saving computational resources. It's important to note that the inversion problem itself is ill-posed and has strong multiple solutions, requiring regularization to obtain stable and reasonable solutions. Tikhonov regularization is the most commonly used method, and its objective function can be defined as: ; Among them, the model constraint terms Typically includes smoothing constraints (such as the Laplace operator): ; here, They are The second-order difference matrix in the direction, It is the smoothing coefficient in each direction. Gradient calculation is the core of NLCG, and the objective function The gradient is: ; here, It is the Jacobian matrix (sensitivity matrix), representing the forward response. For model parameters The partial derivatives. Implicit or adjoint forward modeling techniques are often used to efficiently compute the product of a Jacobian matrix and a vector. without needing to explicitly store large amounts of data. The matrix itself.

[0052] It should be noted that a "cooling" strategy is often used, dynamically adjusting the regularization parameters, i.e. Initially, model constraints were emphasized to stabilize the inversion; later, the constraints were reduced. Let the data fit take precedence to obtain details.

[0053] In summary, the nonlinear conjugate gradient method can significantly improve the accuracy and precision of inversion interpretation when the geoelectric model is known. After the target layer spectrum is densified, the vertical resolution of the corresponding wide-area apparent resistivity profile is also significantly improved due to the significant increase in the amount of data in the vertical direction.

[0054] In some implementations, based on the initial geoelectric model, the original wide-area apparent resistivity profile, and the wide-area apparent resistivity data of each target layer, the initial geoelectric model is used as the current geoelectric model, and a multi-target layer progressive inversion strategy is executed to obtain a target inversion apparent resistivity profile containing information of all target layers. Among them, the multi-purpose layered progressive inversion strategy includes: Select the i-th target layer as the current target layer; Based on the wide-area apparent resistivity data of the current target layer and the current geoelectric model, a constrained inversion is performed to obtain the inverted apparent resistivity profile corresponding to the current target layer. Fill the original wide-area apparent resistivity profile with the inverted apparent resistivity profile corresponding to the current target layer to obtain the intermediate inverted apparent resistivity profile containing the information of the current target layer. Based on the intermediate inverted apparent resistivity profile, stratigraphic correction is performed according to seismic and well logging data to obtain an intermediate geoelectric model containing information of the current target layer. The intermediate geoelectric model is then used as the current geoelectric model. If i is less than or equal to n-1, assign i+1 to i and return to select the i-th destination layer as the current destination layer. With all target layers selected, the intermediate inverted apparent resistivity profile is determined as the target inverted apparent resistivity profile.

[0055] This embodiment describes the specific process of multi-purpose layer progressive inversion. The initial value of i is 1.

[0056] Specifically, firstly, the initial geoelectric model K1 is used as the current geoelectric model. The first target layer, i.e., the shallowest target layer F1, is selected as the current target layer. Constrained inversion is performed on the wide-area apparent resistivity of F1 obtained from the densely acquired data under the K1 model to obtain the inverted apparent resistivity profile corresponding to F1. This profile is then filled into the original wide-area apparent resistivity profile to obtain an intermediate inverted apparent resistivity profile containing information about the target layer F1, denoted as A1. It can be understood that a single constrained inversion only targets a single layer, improving the accuracy and efficiency of the inversion. Using the A1 result as a benchmark, depth correction is performed on each electrical layer using seismic and well logging data to form an intermediate geoelectric model containing information about the current target layer, denoted as geoelectric model K2. It should be noted that geoelectric model K2 does not contain the wide-area apparent resistivity data for target layers F2, F3, ..., Fn; that is, the values ​​within the top and bottom depth ranges corresponding to these target layers are empty. The wide-area apparent resistivity information of the target layers in geoelectric model K2 only includes information about layer F1.

[0057] Subsequently, using geoelectric model K2 as the current geoelectric model, and selecting the second target layer, namely the deeper target layer F2 than F1, as the current target layer, a constrained inversion under the K2 model is performed on the wide-area apparent resistivity of F2 obtained from the encrypted acquisition. This yields the inverted apparent resistivity profile corresponding to F2. It can be understood that since the K2 model already contains F1 information, the actual result is an inverted apparent resistivity profile containing information from both F1 and F2. This profile is then filled into the original wide-area apparent resistivity profile to obtain A2. Using the A1 result as a benchmark, layer correction is performed to obtain geoelectric model K3, which only contains information from layers F1 and F2.

[0058] Understandably, the same progressive inversion steps are then performed to obtain the wide-area apparent resistivity profiles A3, A4, ..., An corresponding to the target layers F3, F4, ..., Fn. Finally, when i equals n-1, i+1 is assigned to i, and the process returns to select the i-th target layer as the current target layer, i.e., the n-th target layer, which is the target layer with the greatest burial depth Fn. After performing a constrained inversion under the Kn model on the wide-area apparent resistivity of Fn obtained from the encrypted acquisition, the wide-area apparent resistivity profile An is obtained and determined as the target inverted apparent resistivity profile. That is, An is the final profile result containing information from all target layers. (Refer to...) Figure 4 As shown. Furthermore, based on the An profile of each line, planar results can be obtained.

[0059] In this embodiment, as the multi-target layer progressive inversion strategy is implemented, the initial geoelectric model will be continuously optimized and corrected. After each target layer is inverted, the geoelectric model will be adjusted based on the inversion results to better reflect the actual geological conditions. Through this continuous progressive and feedback approach, a target inversion apparent resistivity profile containing information from all target layers can ultimately be obtained, providing strong support for accurate fluid identification and classification.

[0060] Compared with this application, existing joint inversion techniques for seismic-electromagnetic data still have the following bottlenecks. First, deep inversion depends on the accuracy of the geoelectric model, especially the shallow model; a single initial model cannot simultaneously satisfy the resistivity response characteristics of different depth strata. Second, there is a lack of inter-layer constraint transmission mechanisms; the inversion of each layer is independent, which risks violating the continuity of stratigraphic deposition. Third, a closed-loop correction process of "shallow inversion results guiding deep model optimization" has not been implemented, resulting in the inability to effectively control deep layer identification errors. These technical deficiencies make it difficult for existing methods to meet the actual production requirements for fluid identification accuracy in the exploration of multi-layered strata in terrestrial superimposed basins.

[0061] The multi-purpose layer progressive inversion method of this application has the following advantages. First, it achieves dynamic optimization of the geoelectric model through progressive correction of multiple target layers (F1→F2→...→Fn); second, it adopts a cascaded feedback mechanism of "shallow inversion results correcting deep models" (K1→A1→K2→A2→...→Kn→An); finally, it also realizes a closed-loop optimization process for joint inversion of the entire layer system for the first time. In terms of the details of this technology, it realizes a dynamically updated geoelectric model architecture, increases the mutual constraint relationship between inter-layer inversion results, realizes the cumulative effect of vertical progressive accuracy, and allows cross-validation through multi-purpose layer inversion results, effectively improving inversion accuracy and speed, and significantly improving vertical resolution.

[0062] In some implementations, fluid identification and segmentation are performed based on the target inverted apparent resistivity profile, including: Based on geological information, a wide-area fluid identification standard was obtained, which identifies and classifies fluids within the target layer based on the wide-area apparent resistivity. Based on the wide-area fluid identification standard, the target inverted apparent resistivity profile is divided into fluid identification sections to obtain the fluid identification results.

[0063] In this embodiment, the target layer is identified using a wide-area fluid identification standard on multiple inverted apparent resistivity distribution profiles to obtain the fluid identification results for the target layer. The combined identification results from multiple profiles can be used to form the planar fluid identification results for the target layer, resulting in the overall fluid identification data for the target layer. This allows for the division of fluid types and distribution ranges on a plane, generating a planar distribution map of the target layer fluids within the study area, and ultimately enabling the division of oil, gas, and water distribution regions on a plane.

[0064] The aforementioned wide-area fluid identification standard is based on well logging resistivity, establishing a coupling relationship between well logging resistivity and wide-area apparent resistivity. Based on geological understanding, it divides different identification units on well logging resistivity, and establishes the range between the wide area and the identification units according to the coupling relationship.

[0065] The reservoir fluid identification method provided in this application can be executed by a reservoir fluid identification device 200. This application uses the reservoir fluid identification device 200 executing the reservoir fluid identification method as an example to illustrate the reservoir fluid identification device 200 provided in this application.

[0066] Please see Figure 5 This is a schematic diagram of the structure of a reservoir fluid identification device 200 provided in an embodiment of this application. Figure 5 As shown, the reservoir fluid identification device 200 includes: Data acquisition module 201 is used to acquire geological information data of the study area, including seismic data and well logging data; The initial geoelectric model establishment module 202 is used to establish an initial geoelectric model based on seismic data and well logging data; The top and bottom depth determination module 203 is used to determine the top and bottom depths of each target layer in the study area based on seismic data and well logging data; there are n target layers. The first module 204 is used to obtain wide-area apparent resistivity data of all strata in the study area based on the wide-area electromagnetic method, and to obtain the overall wide-area apparent resistivity profile. The second module 205 is used to delete the data of each target layer in the overall wide-area apparent resistivity profile according to the top and bottom depths of each target layer, so as to obtain the original wide-area apparent resistivity profile. The acquisition module 206 is used to acquire wide-area apparent resistivity data of each target layer based on spectrum encryption technology and wide-area electromagnetic method. The progressive inversion module 207 is used to take the initial geoelectric model as the current geoelectric model, execute a multi-target layer progressive inversion strategy based on the initial geoelectric model, the original wide-area apparent resistivity profile and the wide-area apparent resistivity data of each target layer, and obtain the target inversion apparent resistivity profile containing information of all target layers. The fluid identification module 208 is used to identify and classify fluids based on the target inverted apparent resistivity profile. Among them, the multi-purpose layered progressive inversion strategy includes: Select the i-th target layer as the current target layer; Based on the wide-area apparent resistivity data of the current target layer and the current geoelectric model, a constrained inversion is performed to obtain the inverted apparent resistivity profile corresponding to the current target layer. Fill the original wide-area apparent resistivity profile with the inverted apparent resistivity profile corresponding to the current target layer to obtain the intermediate inverted apparent resistivity profile containing the information of the current target layer. Based on the intermediate inverted apparent resistivity profile, stratigraphic correction is performed according to seismic and well logging data to obtain an intermediate geoelectric model containing information of the current target layer. The intermediate geoelectric model is then used as the current geoelectric model. If i is less than or equal to n-1, assign i+1 to i and return to select the i-th destination layer as the current destination layer. With all target layers selected, the intermediate inverted apparent resistivity profile is determined as the target inverted apparent resistivity profile.

[0067] In some implementations, the initial geoelectric model establishment module 202 can be used for: Based on seismic data, obtain stratigraphic information for the study area; Based on well logging data, the well logging resistivity of each formation is obtained; Based on the stratigraphic information and the well logging resistivity of each stratigraphic layer, a stratigraphic framework is constructed, and an initial geoelectric model is established.

[0068] In some implementations, the top and bottom depth determination module 203 can be used for: Based on seismic data, stratigraphic information of the study area is obtained, and the stratigraphic information is corrected and realigned based on well logging data to obtain the top and bottom depths of each stratum in the study area. The top and bottom depths of each target layer are obtained based on the top and bottom depths of each stratum within the study area.

[0069] In some implementations, the acquisition module 206 can be used to: Based on the skin depth formula, the wide-area transmission frequency range of each target layer is obtained according to the top and bottom depths of each target layer; Based on spectrum encryption technology, frequency point encryption is performed on each target layer within the corresponding wide-area transmission frequency range; Based on the wide-area electromagnetic method, wide-area apparent resistivity data of each target layer are obtained from encrypted acquisition by a wide-area electromagnetic device.

[0070] In some implementations, the progressive inversion module 207 can be used to: Based on the constrained inversion algorithm, constrained inversion is performed using the wide-area apparent resistivity data of the current target layer and the current geoelectric model.

[0071] In some implementations, the constraint inversion algorithm includes the nonlinear conjugate gradient method, the lateral constraint inversion method, or the longitudinal constraint inversion method.

[0072] In some implementations, the fluid identification module 208 can be used for: Based on geological information, a wide-area fluid identification standard was obtained, which identifies and classifies fluids within the target layer based on the wide-area apparent resistivity. Based on the wide-area fluid identification standard, the target inverted apparent resistivity profile is divided into fluid identification sections to obtain the fluid identification results.

[0073] Since the reservoir fluid identification device 200 adopts all the technical solutions of the reservoir fluid identification method of the above embodiments, it has at least all the beneficial effects brought about by the technical solutions of the above embodiments, and will not be described in detail here.

[0074] Figure 6 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application.

[0075] This electronic device may include a processor 301 and a memory 302 storing computer program instructions.

[0076] Specifically, the processor 301 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0077] Memory 302 may include mass storage for data or instructions. For example, and not limitingly, memory 302 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 302 may include removable or non-removable (or fixed) media. Where appropriate, memory 302 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 302 is non-volatile solid-state memory.

[0078] In some embodiments, memory 302 may include read-only memory (ROM), random access memory (RAM), disk storage media device, optical storage media device, flash memory device, electrical, optical, or other physical / tangible memory storage device. Thus, generally, memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to one aspect of this disclosure.

[0079] The processor 301 reads and executes computer program instructions stored in the memory 302 to implement any of the reservoir fluid identification methods in the above embodiments.

[0080] In one example, the electronic device may also include a communication interface 303 and a bus 310. For example, Figure 6 As shown, the processor 301, memory 302, and communication interface 303 are connected through bus 310 and complete communication with each other.

[0081] The communication interface 303 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0082] Bus 310 includes hardware, software, or both, that couples components of an online data traffic metering device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 310 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, any suitable bus or interconnect is contemplated herein.

[0083] This electronic device can execute the reservoir fluid identification method in the embodiments of this application, thereby achieving a combination Figure 1 and Figure 5 The reservoir fluid identification method and apparatus are described.

[0084] In addition, in conjunction with the reservoir fluid identification method in the above embodiments, this application embodiment can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the reservoir fluid identification methods in the above embodiments.

[0085] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0086] The functional blocks shown in the above-described structural diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0087] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0088] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0089] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A method for identifying reservoir fluids, characterized in that, include: Obtain geological information data for the study area, including seismic data and well logging data; Based on the seismic data and the well logging data, an initial geoelectric model is established; Based on the seismic data and the well logging data, determine the top and bottom depths of each target layer in the study area; there are n target layers; Based on the wide-area electromagnetic method, the wide-area apparent resistivity data of all strata in the study area are obtained to obtain the overall wide-area apparent resistivity profile. Based on the top and bottom depths of each target layer, the data of each target layer in the overall wide-area apparent resistivity profile are deleted to obtain the original wide-area apparent resistivity profile. Based on spectrum encryption technology and the wide-area electromagnetic method, the wide-area apparent resistivity data of each of the target layers are obtained; Based on the initial geoelectric model, the original wide-area apparent resistivity profile, and the wide-area apparent resistivity data of each of the target layers, the initial geoelectric model is used as the current geoelectric model, and a multi-target layer progressive inversion strategy is executed to obtain a target inversion apparent resistivity profile containing information of all target layers. Based on the target inverted apparent resistivity profile, fluid identification and classification are performed; The multi-purpose layer progressive inversion strategy includes: Select the i-th target layer as the current target layer; Based on the wide-area apparent resistivity data of the current target layer and the current geoelectric model, a constrained inversion is performed to obtain the inverted apparent resistivity profile corresponding to the current target layer. Fill the original wide-area apparent resistivity profile with the inverted apparent resistivity profile corresponding to the current target layer to obtain an intermediate inverted apparent resistivity profile containing information about the current target layer. Based on the intermediate inverted apparent resistivity profile, stratigraphic correction is performed according to the seismic data and the well logging data to obtain an intermediate geoelectric model containing the current target layer information, and the intermediate geoelectric model is used as the current geoelectric model. If i is less than or equal to n-1, assign i+1 to i and return to the step of selecting the i-th target layer as the current target layer. With all the target layers selected, the intermediate inverted apparent resistivity profile is determined as the target inverted apparent resistivity profile.

2. The reservoir fluid identification method according to claim 1, characterized in that, The step of establishing an initial geoelectric model based on the seismic data and the well logging data includes: Based on the earthquake data, obtain the stratigraphic information of the study area; Based on the well logging data, the well logging resistivity of each formation was obtained; Based on the stratigraphic layering information and the well logging resistivity of each stratigraphic layer, a stratigraphic framework is constructed, and the initial geoelectric model is established.

3. The reservoir fluid identification method according to claim 1, characterized in that, The determination of the top and bottom depths of each target layer based on the seismic data and the well logging data includes: Based on the seismic data, stratigraphic information of the study area is obtained, and the stratigraphic information is corrected and realigned based on the well logging data to obtain the top and bottom depths of each stratum in the study area. The top and bottom depths of each target layer are obtained based on the top and bottom depths of each stratum within the study area.

4. The reservoir fluid identification method according to claim 1, characterized in that, The acquisition of wide-area apparent resistivity data for each target layer based on spectrum encryption technology and the wide-area electromagnetic method includes: Based on the skin depth formula, the wide-area emission frequency range of each target layer is obtained according to the top and bottom depths of each target layer; Based on spectrum encryption technology, frequency point encryption is performed on each of the target layers within the corresponding wide-area transmission frequency range; Based on the wide-area electromagnetic method, the wide-area apparent resistivity data of each target layer are obtained by encrypted acquisition of wide-area electromagnetic devices.

5. The reservoir fluid identification method according to claim 1, characterized in that, The constrained inversion based on the wide-area apparent resistivity data of the current target layer and the current geoelectric model includes: Based on the constrained inversion algorithm, constrained inversion is performed according to the wide-area apparent resistivity data of the current target layer and the current geoelectric model.

6. The reservoir fluid identification method according to claim 5, characterized in that, The constraint inversion algorithm includes the nonlinear conjugate gradient method, the lateral constraint inversion method, or the vertical constraint inversion method.

7. The reservoir fluid identification method according to claim 1, characterized in that, The step of performing fluid identification and classification based on the target inverted apparent resistivity profile includes: Based on the geological information, a wide-area fluid identification standard is obtained, wherein the wide-area fluid identification standard identifies and classifies the fluids within the target layer based on the wide-area apparent resistivity; Based on the wide-area fluid identification standard, the target inverted apparent resistivity profile is divided into fluid identification sections to obtain the fluid identification results.

8. A reservoir fluid identification device, characterized in that, include: The data acquisition module is used to acquire geological information data of the study area, including seismic data and well logging data; The initial geoelectric model establishment module is used to establish an initial geoelectric model based on the seismic data and the well logging data; The top and bottom depth determination module is used to determine the top and bottom depths of each target layer in the study area based on the seismic data and the well logging data; there are n target layers; The first module is used to obtain wide-area apparent resistivity data of all strata in the study area based on the wide-area electromagnetic method, and to obtain an overall wide-area apparent resistivity profile. The second obtaining module is used to delete the data of each of the target layers in the overall wide-area apparent resistivity profile according to the top and bottom depths of each of the target layers, so as to obtain the original wide-area apparent resistivity profile. The acquisition module is used to acquire wide-area apparent resistivity data of each of the target layers based on spectrum encryption technology and the wide-area electromagnetic method. The progressive inversion module is used to take the initial geoelectric model as the current geoelectric model, execute a multi-target-layer progressive inversion strategy, and obtain a target inversion apparent resistivity profile containing information of all target layers, based on the initial geoelectric model, the original wide-area apparent resistivity profile, and the wide-area apparent resistivity data of each target layer. The fluid identification module is used to identify and classify fluids based on the apparent resistivity profile retrieved from the target. The multi-purpose layer progressive inversion strategy includes: Select the i-th target layer as the current target layer; Based on the wide-area apparent resistivity data of the current target layer and the current geoelectric model, a constrained inversion is performed to obtain the inverted apparent resistivity profile corresponding to the current target layer. Fill the original wide-area apparent resistivity profile with the inverted apparent resistivity profile corresponding to the current target layer to obtain an intermediate inverted apparent resistivity profile containing information about the current target layer. Based on the intermediate inverted apparent resistivity profile, stratigraphic correction is performed according to the seismic data and the well logging data to obtain an intermediate geoelectric model containing the current target layer information, and the intermediate geoelectric model is used as the current geoelectric model. If i is less than or equal to n-1, assign i+1 to i and return to the step of selecting the i-th target layer as the current target layer. With all the target layers selected, the intermediate inverted apparent resistivity profile is determined as the target inverted apparent resistivity profile.

9. An electronic device, characterized in that, It includes a processor and a memory, the memory storing a program or instructions that can run on the processor, the program or instructions being executed by the processor to implement the steps of the reservoir fluid identification method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions for causing a computer to perform the reservoir fluid identification method as described in any one of claims 1 to 7.

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