A method and system for quantitative characterization of oil reservoir parameters

Abstract

The invention provides a method and system for quantitatively characterizing reservoir parameters in an oil region, and relates to the technical field of petroleum engineering. The invention collects historical oil region reserve information, core data and well logging data of oil region geological data, extracts features from the core data and well logging data to obtain porosity, permeability and water saturation, and generates oil region recoverability through the features; trains and optimizes a model with oil region recoverability and oil region reserves as labels and historical core data and well logging data as data sets, inputs the core data and well logging data of the oil region to be evaluated into the optimized model to obtain oil region recoverability and oil region reserves, analyzes the oil region recoverability to obtain safe oil region recoverability, generates an oil region potential index through the core data, constructs an oil region revenue model according to the oil region reserves, the oil region potential index and the safe oil region recoverability, and generates an oil region revenue index; and quantitatively evaluates the exploitation revenue of the oil region through the oil region revenue index.

Description

Technical Field

[0001] The invention relates to the technical field of petroleum engineering, and in particular to a method and system for quantitatively characterizing reservoir parameters in an oil region. Background Art

[0002] In the field of oil exploration and production, accurate assessment of reservoir characteristics in oil fields is not only the key to improving production efficiency and economic benefits, but also the basis for ensuring sustainable resource utilization and environmental protection. Traditional reservoir evaluation methods, such as relying on limited geological data and empirical judgment, often fail to fully reflect the complexity and dynamic changes of reservoirs. This limitation is particularly prominent in the face of increasingly complex reservoir conditions and increasing amounts of data. With the deepening of oil field development, reservoir conditions have become more complex and changeable, including the diversity of geological structures, the variability of fluid properties, and the dynamic changes of reservoir responses during production, all of which put forward higher requirements for reservoir evaluation.

[0003] When dealing with these complex situations, traditional methods often lack accuracy and reliability in their evaluation results due to limited data processing capabilities, insufficient model complexity, and poor dynamic adaptability. For example, traditional methods may not be able to effectively handle the flow behavior of multiphase fluids in complex geological structures, or lack sufficient accuracy in predicting reservoir dynamic changes. In addition, with the advancement of exploration technology, such as the application of technologies such as 3D seismic imaging, high-resolution logging, and core analysis, a large amount of high-dimensional data has been generated, which is difficult to be effectively used in traditional methods.

[0004] Therefore, developing a method that can comprehensively utilize multi-source data and use advanced mathematical models and machine learning techniques to quantitatively characterize reservoir parameters has become a technical problem that the oil industry urgently needs to solve. This method not only needs to be able to process and analyze large amounts of complex data, but also needs to be able to build accurate mathematical models to simulate reservoir behavior and continuously optimize model parameters through machine learning technology to adapt to the dynamic changes of the reservoir. Through such a method, the recoverability of the reservoir can be more accurately evaluated, the mining strategy can be optimized, the resource utilization rate can be improved, and the environmental risks can be reduced, so as to achieve the sustainable development of oil exploration and mining.

[0005] In the prior art, the publication number CN112304843B discloses a method for quantitatively characterizing the adsorption of shale gas in mudstone, which relates to the technical field of shale gas development. Shale core samples are taken along the vertical surface of the formation, and after oil-clearing and drying treatment, the total specific surface area of ​​the shale core samples is obtained by liquid nitrogen adsorption test experiment; the pore type distribution map of the shale core samples is obtained by scanning electron microscopy imaging, and the proportion of each pore type is obtained; the specific surface area of ​​each pore type is obtained according to the total specific surface area of ​​the shale core samples and the proportion of each pore type; a core sample of a single pore type is taken, and the shale gas adsorption test is performed to obtain the specific surface area of ​​the single pore type. The shale gas adsorption per unit area of ​​the core sample is obtained according to the specific surface area of ​​the shale core sample, the specific surface area of ​​each pore type in the shale core sample and the shale gas adsorption per unit area of ​​the core sample of a single pore type. However, the existing technology still has shortcomings. First, the existing technology only collects a core sample of mud shale. The sampling of this method is too single and cannot effectively eliminate the contingency; therefore, its results are difficult to fully reflect the shale gas adsorption characteristics of the entire mud shale; secondly, there are many factors that affect the shale gas adsorption, including but not limited to pore structure, pressure, temperature, water content and organic matter content, all of which have a significant impact on the adsorption. However, when quantitatively characterizing the shale gas adsorption, the existing technology only conducts quantitative analysis through pore structure. This method is obviously not convincing enough because it fails to fully consider the comprehensive effects of the above-mentioned other key factors.

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0007] The purpose of the present invention is to provide a method and system for quantitatively characterizing oil reservoir parameters to solve the problems raised in the above background technology.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A method for quantitative characterization of reservoir parameters in an oil region, comprising the following specific steps:

[0010] Step 1: Collect historical oil field reserves information, historical core data and historical well logging data of oil field geological data, wherein the core data includes core volume, core void volume, fluid viscosity, core length, core cross-sectional area, fluid pressure difference, oil and gas saturation, API gravity and sulfur content, and the well logging data includes formation water resistivity, formation resistivity and reservoir thickness;

[0011] Step 2: Analyze and process the core data to obtain porosity and permeability, perform mathematical analysis on formation water resistivity, formation resistivity and porosity, and estimate water saturation based on the analysis results; and construct an oil zone recoverability model by analyzing water saturation, permeability and porosity to generate oil zone recoverability;

[0012] Step 3: The core data and well logging data in the historical geological data are used to form a data set, and the oil field evaluation model is constructed with oil field reserves and oil field recoverability as labels. The data set is used to train and optimize the oil field evaluation model to obtain the final oil field evaluation model;

[0013] Step 4: Input the core data and logging data of the oil zone to be analyzed into the final oil zone assessment model to obtain the oil zone reserves and oil zone recoverability, perform threshold processing on the oil zone recoverability by comparing the oil zone recoverability with a preset threshold, and obtain the safe oil zone recoverability according to the threshold processing result;

[0014] Step 5: Generate an oil field potential index by mathematically analyzing reservoir thickness, oil and gas saturation, API gravity and sulfur content, and then construct an oil field profit model based on the oil field reserves, oil field potential index and safe oil field recoverability to generate an oil field profit index; quantitatively evaluate the oil field mining profit through the oil field profit index.

[0015] Furthermore, the specific logic for generating water saturation is: mathematical analysis is performed through formation water resistivity, formation resistivity and porosity, and water saturation is estimated based on the analysis results. The specific formula is:

[0016] ;

[0017] in, is the water saturation, Cementation coefficient, is the formation water resistivity, is the porosity, is the formation resistivity.

[0018] Furthermore, the specific logic for generating the oil zone recoverability is: the oil zone recoverability is generated through water saturation, permeability and porosity, and the specific formula is:

[0019] ;

[0020] in, is the recoverability of the oil field, is the porosity, is the water saturation, is the permeability.

[0021] Furthermore, the root mean square error is used as the loss function, and Regularization adjusts the loss function. The specific adjustment function is:

[0022] ;

[0023] in, are model parameters, is the number of model parameter groups, For the The actual oil recovery of the oil field corresponding to the group model parameters, For the The predicted oil zone recoverability corresponding to the group model parameters, is the regularization parameter, is the total number of data types in the model parameters, is the model parameter core data or well logging data.

[0024] Furthermore, the specific logic for threshold segmentation of oil zone recoverability is: setting an oil zone recoverable threshold, comparing the oil zone recoverable with the oil zone recoverable threshold, and marking the oil zone recoverable that is less than or equal to the oil zone recoverable threshold as zero. The specific formula is:

[0025] ;

[0026] in, For the safe oil zone recoverability, is the recoverability of the oil field, It is the recoverable threshold of the oil zone.

[0027] Furthermore, the specific logic for generating the oil zone potential index is as follows: the oil zone potential model is generated through the reservoir thickness, oil and gas saturation, API gravity and sulfur content in the core data to obtain the oil zone potential index. The specific formula is as follows:

[0028] ;

[0029] in, is the oil field potential index, is the reservoir thickness, is the oil and gas saturation, For API heavy, The sulfur content.

[0030] Furthermore, the specific logic for generating the oil region profit index is as follows: constructing an oil region profit model based on the oil region reserves, oil region potential index and safe oil region recoverability to generate the oil region profit index; the specific formula is as follows:

[0031] ;

[0032] in, Oil Regions Revenue Index, For the safe oil zone recoverability, is the oil field potential index, For oil reserves.

[0033] The present invention further provides a system for quantitative characterization of oil field reservoir parameters, the system being used to implement the above-mentioned method for quantitative characterization of oil field reservoir parameters, comprising:

[0034] A data acquisition module, for collecting historical oil field reserves information, historical core data and historical well logging data of oil field geological data, wherein the core data includes core volume, core void volume, fluid viscosity, core length, core cross-sectional area, fluid pressure difference, oil and gas saturation, API gravity and sulfur content, and the well logging data includes formation water resistivity, formation resistivity and reservoir thickness;

[0035] The data analysis module is used to analyze and process the core data to obtain porosity and permeability, perform mathematical analysis through formation water resistivity, formation resistivity and porosity, and estimate water saturation based on the analysis results; and construct an oil zone recoverability model by analyzing water saturation, permeability and porosity to generate oil zone recoverability;

[0036] The modeling optimization module is used to form a data set from core data and well logging data in historical geological data, build an oil field evaluation model with oil field reserves and oil field recoverability as labels, and use the data set to train and optimize the oil field evaluation model to obtain the final oil field evaluation model;

[0037] A data processing module is used to input the core data and logging data of the oil zone to be analyzed into the final oil zone assessment model to obtain the oil zone reserves and oil zone recoverability, perform threshold processing on the oil zone recoverability by comparing the oil zone recoverability with a preset threshold value, and obtain the safe oil zone recoverability according to the threshold processing result;

[0038] The quantitative characterization module is used to generate an oil field potential index by mathematically analyzing reservoir thickness, oil and gas saturation, API gravity and sulfur content, and then construct an oil field profit model based on the oil field reserves, oil field potential index and safe oil field recoverability to generate an oil field profit index; the oil field profit index is used to quantitatively evaluate the oil field exploitation profit.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] The present invention has successfully overcome the limitation of single sampling in the prior art by comprehensively collecting core data and logging data covering the entire reservoir, and significantly reduced the randomness of the results. When accurately and quantitatively characterizing reservoir parameters, the present invention comprehensively considers the multi-dimensional parameters that affect the characteristics, thereby avoiding the problem of inaccurate feature description that may result from relying solely on the main parameters, and ensuring the comprehensiveness and accuracy of the characterization results.

[0041] After quantitatively characterizing the recoverability and potential index of an oil field, the present invention integrates them with known data to generate an oil field profit index reflecting the profit obtained from exploiting the oil field, providing an important basis for the evaluation of the oil field. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 Schematic diagram of the process of quantitative characterization of reservoir parameters in oil fields.

[0043] Figure 2 Schematic diagram of the structure of the quantitative characterization system for oil field reservoir parameters. DETAILED DESCRIPTION

[0044] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0045] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0046] Example:

[0047] See also Figure 1 , the present invention provides a technical solution:

[0048] A method for quantitative characterization of reservoir parameters in an oil region, comprising the following specific steps:

[0049] Step 1: Collect historical oil field reserves information, historical core data and historical well logging data of oil field geological data, wherein the core data includes core volume, core void volume, fluid viscosity, core length, core cross-sectional area, fluid pressure difference, oil and gas saturation, API gravity and sulfur content, and the well logging data includes formation water resistivity, formation resistivity and reservoir thickness;

[0050] In this embodiment, the rock samples in the middle of the reservoir are brought to the surface through coring and sent to the laboratory. The core volume, core pore volume, core length, core cross-sectional area, fluid volume flow rate and fluid pressure difference are obtained by core analyzer; the original liquid in the pores is extracted, and the fluid viscosity, API gravity and sulfur content are measured using a viscometer, a density meter and a sulfur analyzer; the oil and gas saturation is measured by gamma ray attenuation technology. The formation water resistivity and formation resistivity are measured by resistivity logging tools, and the reservoir thickness is measured by sonic logging tools.

[0051] Step 2: Analyze and process the core data to obtain porosity and permeability, perform mathematical analysis on formation water resistivity, formation resistivity and porosity, and estimate water saturation based on the analysis results; and construct an oil zone recoverability model by analyzing water saturation, permeability and porosity to generate oil zone recoverability;

[0052] The specific formula for obtaining the porosity is:

[0053] ;

[0054] in, is the porosity, is the core pore volume, is the core volume. Porosity reflects the size of rock pores. The larger the value, the larger the pores in the rock. It is an important parameter for evaluating the characteristics of oil fields.

[0055] The formula for obtaining the permeability is:

[0056] ;

[0057] in, is the permeability, is the fluid viscosity, is the core length, is the cross-sectional area of ​​the core, is the fluid pressure difference, is the fluid volume flow rate, permeability Reflects the ability of fluid to pass through rock. The larger the value, the stronger the ability of fluid to pass through rock and the easier it is to mine. It is an important parameter for evaluating oil zone performance.

[0058] The specific logic for generating water saturation is: mathematical analysis is performed through formation water resistivity, formation resistivity and porosity, and water saturation is estimated based on the analysis results. The specific formula is:

[0059] ;

[0060] in, is the water saturation, Cementation coefficient, is the formation water resistivity, is the porosity, is the formation resistivity. Water saturation It reflects the relative content of water in the rock pores. The larger the value, the greater the volume proportion of water in the rock pores. Correspondingly, the smaller the volume proportion of oil and gas. It can be used as an important basis for evaluating the performance characteristics of oil fields.

[0061] The specific logic for generating oil zone recoverability is: the oil zone recoverability is generated through water saturation, permeability and porosity, and the specific formula is:

[0062] ;

[0063] in, is the recoverability of the oil field, is the porosity, is the water saturation, is the permeability. Oil zone recoverability Reflects the recoverability of oil and gas in the oil and gas reservoir. The larger the value, the greater the recoverability of oil and gas. Water saturation It reflects the relative content of water in the rock pores. The larger the value, the greater the volume proportion of water in the rock pores. Correspondingly, the smaller the volume proportion of oil and gas, the more difficult it is to extract, and the higher the permeability. Reflects the ability of fluid to pass through rock. The larger the value, the stronger the ability of fluid to pass through rock and the easier it is to mine. It reflects the size of rock pores. The larger the value, the larger the pores in the rock, and the easier it is to exploit. This formula reflects the combined impact of these parameters on the recoverability of an oil field, and provides an important basis for evaluating the recoverability of an oil field.

[0064] Step 3: The core data and well logging data in the historical geological data are used to form a data set, and the oil field evaluation model is constructed with oil field reserves and oil field recoverability as labels. The data set is used to train and optimize the oil field evaluation model to obtain the final oil field evaluation model;

[0065] The root mean square error is used as the loss function, and Regularization adjusts the loss function. The specific adjustment function is:

[0066] ;

[0067] in, are model parameters, is the number of model parameter groups, For the The actual oil recovery of the oil field corresponding to the group model parameters, For the The predicted oil zone recoverability corresponding to the group model parameters, is the regularization parameter, is the total number of data types in the model parameters, is the model parameter The model parameters is a vector composed of core data and well logging data;

[0068] When the label is oil field reserves, the oil field recoverability in the above formula is replaced by oil field reserves. The two labels are calculated independently and added together in a 1:1 ratio to calculate the total loss;

[0069] Step 4: Input the core data and logging data of the oil zone to be analyzed into the final oil zone assessment model to obtain the oil zone reserves and oil zone recoverability, perform threshold processing on the oil zone recoverability by comparing the oil zone recoverability with a preset threshold, and obtain the safe oil zone recoverability according to the threshold processing result;

[0070] The specific logic for threshold segmentation of oil zone recoverability is: set an oil zone recoverable threshold, compare the oil zone recoverable with the oil zone recoverable threshold, and mark the oil zone recoverable that is less than or equal to the oil zone recoverable threshold as zero. The specific formula is:

[0071] ;

[0072] in, For the safe oil zone recoverability, is the recoverability of the oil field, The recoverable threshold of the oil zone. It reflects the safe degree of oil and gas recovery in the oil and gas reservoir. The larger the value, the greater the degree of oil and gas recovery. It reflects the recoverability of oil and gas in the oil and gas reservoir. The larger the value, the greater the safe recovery of oil and gas. When the oil level is lower than the preset threshold, the oil field cannot be safely mined and such an oil field has no mining value at present. Therefore, the mining degree of the oil field that cannot be safely mined is marked as 0 through the oil field mining threshold.

[0073] Step 5: Generate an oil field potential index by mathematically analyzing reservoir thickness, oil and gas saturation, API gravity and sulfur content, and then construct an oil field profit model based on the oil field reserves, oil field potential index and safe oil field recoverability to generate an oil field profit index; quantitatively evaluate the oil field mining profit through the oil field profit index.

[0074] The specific logic for generating the oil zone potential index is: the oil zone potential model is generated through the reservoir thickness, oil and gas saturation, API gravity and sulfur content in the core data to obtain the oil zone potential index. The specific formula is:

[0075] ;

[0076] in, is the oil field potential index, is the reservoir thickness, is the oil and gas saturation, For API heavy, is the sulfur content. Oil field potential index ; Oilfield potential index Reflects the development potential and economic value of oil and gas reservoirs. The larger the value, the greater the development potential and economic value of the oil and gas reservoirs; reservoir thickness Reflects the vertical depth of the oil and gas reservoir. The larger the value, the greater the vertical depth of the oil and gas reservoir, the more oil reserves in the oil and gas reservoir, and the higher the development potential of the oil and gas reservoir; Oil and gas saturation It reflects the ratio of the volume of oil and gas in rock pores to the total volume of pores. The larger the value, the higher the ratio of oil and gas in rock pores, and the greater the potential of oil and gas reservoirs. Reflects the density of crude oil. The larger the value, the smaller the density of crude oil, the easier it is to mine and transport crude oil, and therefore the greater the potential of the oil field. It reflects the sulfur content in crude oil. The smaller the value, the higher the quality of the crude oil. This is because crude oil with low sulfur content has less impact on the environment during processing and use, and has lower processing costs.

[0077] The specific logic for generating the oil region profit index is as follows: constructing an oil region profit model based on the oil region reserves, oil region potential index and safe oil region recoverability to generate the oil region profit index; the specific formula is as follows:

[0078] ;

[0079] in, Oil Regions Revenue Index, For the safe oil zone recoverability, is the oil field potential index, is the oil field reserves. Oil field profit index Reflects the benefits of the oil production area. The larger the value, the greater the benefits that can be obtained from the oil production area. Reflects the safe exploitation degree of oil and gas in the oil and gas reservoirs in the oil field. The larger the value, the greater the exploitation degree of oil and gas. Oil field potential index It reflects the development potential and economic value of oil and gas reservoirs. The larger the value, the greater the development potential and economic value of oil and gas reservoirs. It reflects the amount of oil stored in the oil field. The larger the value, the more oil the oil field stores and the greater the profit from exploiting the oil field.

[0080] See also Figure 2 The present invention also provides a system for quantitative characterization of oil reservoir parameters, which is used to implement the above-mentioned method for quantitative characterization of oil reservoir parameters, and includes:

[0081] A data acquisition module, for collecting historical oil field reserves information, historical core data and historical well logging data of oil field geological data, wherein the core data includes core volume, core void volume, fluid viscosity, core length, core cross-sectional area, fluid pressure difference, oil and gas saturation, API gravity and sulfur content, and the well logging data includes formation water resistivity, formation resistivity and reservoir thickness;

[0082] The data analysis module is used to analyze and process the core data to obtain porosity and permeability, perform mathematical analysis through formation water resistivity, formation resistivity and porosity, and estimate water saturation based on the analysis results; and construct an oil zone recoverability model by analyzing water saturation, permeability and porosity to generate oil zone recoverability;

[0083] The modeling optimization module is used to form a data set from core data and well logging data in historical geological data, build an oil field evaluation model with oil field reserves and oil field recoverability as labels, and use the data set to train and optimize the oil field evaluation model to obtain the final oil field evaluation model;

[0084] A data processing module is used to input the core data and logging data of the oil zone to be analyzed into the final oil zone assessment model to obtain the oil zone reserves and oil zone recoverability, perform threshold processing on the oil zone recoverability by comparing the oil zone recoverability with a preset threshold value, and obtain the safe oil zone recoverability according to the threshold processing result;

[0085] The quantitative characterization module is used to generate an oil field potential index by mathematically analyzing reservoir thickness, oil and gas saturation, API gravity and sulfur content, and then construct an oil field profit model based on the oil field reserves, oil field potential index and safe oil field recoverability to generate an oil field profit index; the oil field profit index is used to quantitatively evaluate the oil field exploitation profit.

[0086] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0087] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0088] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0089] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for quantitative characterization of oil reservoir parameters, characterized in that: The specific steps include: Step 1: Collect historical oil field reserves information, historical core data and historical well logging data of oil field geological data, wherein the core data includes core volume, core void volume, fluid viscosity, core length, core cross-sectional area, fluid pressure difference, oil and gas saturation, API gravity and sulfur content, and the well logging data includes formation water resistivity, formation resistivity and reservoir thickness; Step 2: Analyze and process the core data to obtain porosity and permeability, and perform mathematical analysis on formation water resistivity, formation resistivity and porosity to estimate water saturation based on the analysis results; and construct an oil zone recoverability model by analyzing water saturation, permeability and porosity to generate oil zone recoverability; Step 3: The core data and well logging data in the historical geological data are used to form a data set, and the oil field evaluation model is constructed with oil field reserves and oil field recoverability as labels. The data set is used to train and optimize the oil field evaluation model to obtain the final oil field evaluation model; Step 4: Input the core data and logging data of the oil zone to be analyzed into the final oil zone assessment model to obtain the oil zone reserves and oil zone recoverability, perform threshold processing on the oil zone recoverability by comparing the oil zone recoverability with a preset threshold, and obtain the safe oil zone recoverability according to the threshold processing result; Step 5: Generate an oil field potential index by mathematically analyzing reservoir thickness, oil and gas saturation, API gravity and sulfur content, and then construct an oil field profit model based on the oil field reserves, oil field potential index and safe oil field recoverability to generate an oil field profit index; quantitatively evaluate the oil field mining profit through the oil field profit index.

2. The method for quantitative characterization of oil reservoir parameters according to claim 1, characterized in that: The specific logic for generating water saturation is: mathematical analysis is performed through formation water resistivity, formation resistivity and porosity, and water saturation is estimated based on the analysis results. The specific formula is: ； in, is the water saturation, Cementation coefficient, is the formation water resistivity, is the porosity, is the formation resistivity.

3. The method for quantitative characterization of oil reservoir parameters according to claim 1, characterized in that: The root mean square error is used as the loss function, and Regularization adjusts the loss function. The specific adjustment function is: ； in, are model parameters, is the number of model parameter groups, For the The actual oil recovery of the oil field corresponding to the group model parameters, For the The predicted oil zone recoverability corresponding to the group model parameters, is the regularization parameter, is the total number of data types in the model parameters, is the model parameter core data or well logging data.

4. The method for quantitative characterization of oil reservoir parameters according to claim 1, characterized in that: The specific logic for generating oil zone recoverability is: the oil zone recoverability is generated through water saturation, permeability and porosity, and the specific formula is: ； in, is the recoverability of the oil field, is the porosity, is the water saturation, is the permeability.

5. The method for quantitative characterization of oil reservoir parameters according to claim 1, characterized in that: The specific logic for threshold segmentation of oil zone recoverability is as follows: Set an oil zone recoverable threshold, compare the oil zone recoverable degree with the oil zone recoverable threshold, and calibrate the oil zone recoverable degree that is less than or equal to the oil zone recoverable threshold to zero. The specific formula is: ； in, For the safe oil zone recoverability, is the recoverability of the oil field, It is the recoverable threshold of the oil zone.

6. The method for quantitative characterization of oil reservoir parameters according to claim 1, characterized in that: The specific logic for generating the oil field potential index is as follows: The oil zone potential model is generated through the reservoir thickness, oil and gas saturation, API gravity and sulfur content in the core data, and the oil zone potential index is obtained. The specific formula is as follows: ； in, is the oil field potential index, is the reservoir thickness, is the oil and gas saturation, For API heavy, For the sulfur content.

7. The method for quantitatively characterizing reservoir parameters of an oil region according to claim 1, characterized in that: The specific logic for generating the oil region profit index is as follows: constructing an oil region profit model based on the oil region reserves, oil region potential index and safe oil region recoverability to generate the oil region profit index; the specific formula is as follows: ； in, Oil Regions Revenue Index, For the safe oil zone recoverability, is the oil field potential index, For oil reserves.

8. A system for quantitative characterization of oil reservoir parameters, characterized in that: The system is used to implement the method for quantitative characterization of oil reservoir parameters according to any one of claims 1 to 7, comprising: A data acquisition module, for collecting historical oil field reserves information, historical core data and historical well logging data of oil field geological data, wherein the core data includes core volume, core void volume, fluid viscosity, core length, core cross-sectional area, fluid pressure difference, oil and gas saturation, API gravity and sulfur content, and the well logging data includes formation water resistivity, formation resistivity and reservoir thickness; The data analysis module is used to analyze and process the core data to obtain porosity and permeability, perform mathematical analysis through formation water resistivity, formation resistivity and porosity, and estimate water saturation based on the analysis results; and construct an oil zone recoverability model by analyzing water saturation, permeability and porosity to generate oil zone recoverability; The modeling optimization module is used to form a data set from core data and well logging data in historical geological data, build an oil field evaluation model with oil field reserves and oil field recoverability as labels, and use the data set to train and optimize the oil field evaluation model to obtain the final oil field evaluation model; A data processing module is used to input the core data and logging data of the oil zone to be analyzed into the final oil zone assessment model to obtain the oil zone reserves and oil zone recoverability, perform threshold processing on the oil zone recoverability by comparing the oil zone recoverability with a preset threshold value, and obtain the safe oil zone recoverability according to the threshold processing result; The quantitative characterization module is used to generate an oil field potential index by mathematically analyzing reservoir thickness, oil and gas saturation, API gravity and sulfur content, and then construct an oil field profit model based on the oil field reserves, oil field potential index and safe oil field recoverability to generate an oil field profit index; the oil field profit index is used to quantitatively evaluate the oil field exploitation profit.

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