Method and system for calculating hydrothermal strength index TII in reservoir

Abstract

The invention belongs to the field of geological exploration and mineral resource development, particularly relates to a method and a system for calculating a hydrothermal strength index TII in a reservoir, and aims to solve the problem that the hydrothermal activity strength of the reservoir cannot be quickly and accurately calculated in the prior art. The method comprises the following steps: collecting data related to reservoir characteristics in an oil and gas field as input data; preprocessing the input data to obtain preprocessed data; inputting the preprocessed data into the trained TII prediction model to obtain a predicted value of the comprehensive hydrothermal strength index TII; a TII prediction model is constructed based on a depth gradient lifting network; the TII prediction model comprises a first hidden layer, a second hidden layer and an output layer. According to the method, the calculation speed and precision of the hydrothermal activity intensity of the reservoir are improved.

Description

Technical Field

[0001] The invention belongs to the field of geological exploration and mineral resource development, and in particular relates to a method and a system for calculating a hydrothermal intensity index TII in a reservoir. Background Art

[0002] Hydrothermal activity refers to the movement and interaction of hot liquids (such as water, gas and dissolved minerals) in the earth's crust under specific temperature and pressure conditions. This activity plays a vital role in the formation and evolution of mineral deposits, especially in the formation of metal mineral resources, where hydrothermal fluids are often important mineralization media.

[0003] However, the existing hydrothermal intensity calculation methods mainly focus on the analysis of a single parameter and lack comprehensive consideration of multiple geological characteristics. Traditional hydrothermal activity calculations usually rely on empirical rules or qualitative analysis, which makes researchers face the following problems when judging hydrothermal intensity: Data diversity and complexity: There are many factors that affect hydrothermal activity in the reservoir, such as temperature, pressure, chemical composition, etc., and a single indicator is difficult to fully reflect the hydrothermal intensity. Lack of systematic calculation methods: Existing methods often use linear or simple threshold judgments, and cannot make full use of advanced statistical and machine learning techniques for in-depth analysis, and thus cannot determine the main controlling factors of hydrothermal migration. The calculation of the total geothermal intensity of the entire reservoir leads to low efficiency and accuracy in geological exploration. Based on this, the present invention proposes a deep gradient boosting network (DGBN) to improve the prediction accuracy and model generalization ability by constructing a multi-layer structure in a gradient boosting tree (GBT) model. Designing this multi-layer structure includes determining the number of layers, defining the function of each layer, constructing and training each layer of GBT model, defining the loss function and optimization algorithm, and evaluating and adjusting the model performance, thereby forming a deep integrated model to gradually improve the prediction accuracy and optimize the overall performance. Summary of the invention

[0004] In order to solve the above problems in the prior art, that is, to solve the problem that the prior art cannot quickly and accurately calculate the hydrothermal activity intensity of the reservoir, the first aspect of the present invention proposes a method for calculating the hydrothermal intensity index TII in the reservoir, the method comprising: S100, collecting data related to reservoir characteristics in the oil and gas field as input data; the input data includes parameters for characterizing the source and genesis of hydrothermal fluids, parameters reflecting the chemical composition of fluids and their evolution process, parameters affecting the thermodynamic properties of the reservoir, and parameters related to the phase state and physical properties of reservoir fluids; S200, preprocessing the input data to obtain preprocessed data; S300, inputting the preprocessed data into the trained TII prediction model to obtain the predicted value of the comprehensive hydrothermal intensity index TII: The TII prediction model is constructed based on a deep gradient boosting network; the TII prediction model includes a first hidden layer, a second hidden layer, and an output layer; the first hidden layer is used to standardize the preprocessed data to obtain standardized data; the second hidden layer is used to weight and nonlinearly combine the standardized data to obtain the hydrothermal intensity index TII as the initial TII; the output layer contains multiple GBT models; the output layer is used to calculate the residual of each GBT model, use the residual as the target value, and combine the hydrothermal intensity index TII output by the GBT model of the previous layer to update the GBT model of the next layer, thereby obtaining the predicted value of the comprehensive hydrothermal intensity index TII.

[0005] In some preferred embodiments, the preprocessing includes data cleaning, missing value and outlier elimination.

[0006] In some preferred embodiments, the parameter used to characterize the source and genesis of the hydrothermal fluid includes the trace element δEu; the parameter reflecting the chemical composition of the fluid and its evolution process includes the oxygen isotope ratio δ 18 O; the parameters affecting the thermodynamic properties of the reservoir include reservoir temperature; the parameters related to the phase state and physical properties of the reservoir fluid include reservoir pressure.

[0007] In some preferred embodiments, the standardized data is weighted and nonlinearly combined to obtain the hydrothermal intensity index TII, and the method is: ; in, represents the hydrothermal intensity index TII, p represents the nonlinear adjustment factor, , , , represent the standardized trace element δEu, oxygen isotope ratio δ 18 O, reservoir temperature, reservoir pressure, k 1 represents the influence of δEu on TII, α and β represent δEu and δ 18 O represents the weight of TII, γ represents the weight of reservoir temperature, represents the weight of reservoir pressure, and c is a constant used to adjust the baseline value.

[0008] In some preferred embodiments, the adaptive dynamic weight is calculated by: ; in, Represents the weight of the feature, i.e., α, β, γ, , are parameters used to characterize the source and origin of hydrothermal fluids, which are δEu, δ 18O, reservoir temperature or reservoir pressure, , Both represent weight coefficients, Represents the feature importance evaluation transformation value of the nth layer, It is n The weight parameters of the layer.

[0009] In some preferred embodiments, the predicted value of the comprehensive hydrothermal intensity index TII is obtained by: ; ; in, represents the predicted value of the comprehensive hydrothermal intensity index TII, represents the weight of each loss term, Indicates n The TII prediction value of the layer, is δEu, δ 18 The input vector consists of O, reservoir temperature T, and reservoir pressure P. is the output value of the nonlinear transformation inside the residual block, corresponding to the prediction correction of the network for the features of this layer, and is the prediction residual of the nth layer. It is a standardized interlayer residual calculated based on geological prior knowledge, reflecting the offset of the actual rock formation physical property parameters and representing the actual residual. represents the factor that controls the effect of the residual on the total loss, Indicates n The hydrothermal intensity index TII of the layer, Represents an optimization operation, which finds the optimal residual function through the optimization process , so that the overall loss function is minimized, and M represents the total number of iterations of the GBT model in each layer.

[0010] In some preferred embodiments, the loss function of the TII prediction model during training is: ; ; ; in, Represents the loss value, that is, the prediction residual, It is the real TII value. In actual application, the real TII value is the standard value set according to geological conditions. is the TII value predicted by the model, 𝑛 is the total number of test samples, The features of the output of the n-1th layer, is the convolution kernel weight matrix, which extracts geological features, i.e., the features of parameters used to characterize the source and genesis of hydrothermal fluids, Rn2 is the convolution kernel weight matrix, which expands the features back to the original dimension, and ReLU() is the activation function. , are bias parameters, is the measured geological characteristics of the kth sampling point, is a standard value set according to geological conditions. N is the total number of sampling points.

[0011] In some preferred embodiments, after step S300, the method further includes obtaining the main controlling factors of the hydrothermal migration: Extracting connectivity information of faults and fractures and TII data from seismic data, interpolating them into a grid, and calculating the master control degree of faults and the master control degree of fracture networks, and determining the master control factors of hydrothermal migration based on the master control degrees of faults and fracture networks; the master control factors include faults and fracture networks; The method for obtaining the fault master control degree is as follows: ; in, Indicates the fault master degree, represents the overlapping area between the ith TII high value area and the fault area. The TII high value area is the area with significant hydrothermal activity, that is, the area where the TII is greater than the set intensity. The fault area is the belt-shaped area extending along the fault direction and on both sides. represents the average slip rate at the overlap between the ith TII high value area and the fault area, represents the maximum slip rate in all overlapping faults, represents the average value of TII in the overlapping area between the ith TII high value area and the fault area, represents the maximum TII value among all overlapping fault layers, represents the total area of ​​the fault zone; The method for obtaining the master control degree of the fracture network is as follows: ; in, represents the master control degree of the crack network, K represents the total number of crack intersection nodes, TII 交汇点i Represents the TII value of the i-th intersection node, TII 围岩值 It represents the average TII value in the non-crack area within the model range.

[0012] In some preferred embodiments, after step S300, the total amount of geothermal intensity of the entire reservoir is calculated: ; in, T represents the total geothermal intensity of the entire reservoir, Indicates i , j , k Hydrothermal intensity of the grid cell, Δ x , Δ y , Δ z are the x, y, and z direction intervals of the subdivision cells of the study area, m , n , l are the total number of mesh elements in the x, y, and z directions, respectively.

[0013] In a second aspect of the present invention, a system for calculating a hydrothermal intensity index TII in a reservoir is proposed, the system comprising: A data acquisition module is configured to collect data related to reservoir characteristics in the oil and gas field as input data; the input data includes parameters for characterizing the source and genesis of hydrothermal fluids, parameters reflecting the chemical composition of fluids and their evolution process, parameters affecting the thermodynamic properties of the reservoir, and parameters related to the phase state and physical properties of reservoir fluids; A preprocessing module, configured to preprocess the input data to obtain preprocessed data; The model prediction module is configured to input the preprocessed data into the trained TII prediction model to obtain the predicted value of the comprehensive hydrothermal intensity index TII: The TII prediction model is constructed based on a deep gradient boosting network; the TII prediction model includes a first hidden layer, a second hidden layer, and an output layer; the first hidden layer is used to standardize the preprocessed data to obtain standardized data; the second hidden layer is used to weight and nonlinearly combine the standardized data to obtain the hydrothermal intensity index TII as the initial TII; the output layer includes multiple GBT models; the output layer is used to calculate the residual of each GBT model, use the residual as the target value, and combine the hydrothermal intensity index TII output by the GBT model of the previous layer to update the GBT model of the next layer, thereby obtaining the predicted value of the comprehensive hydrothermal intensity index TII; The input of the first GBT model is the initial TII.

[0014] Beneficial effects of the present invention: The invention improves the speed and accuracy of calculating the hydrothermal activity intensity of the reservoir.

[0015] 1) This method first collects data related to reservoir characteristics, including δEu, δ 18O, temperature, pressure and other characteristic variables, and strictly ensure the data quality, and eliminate outliers through preprocessing to improve the reliability of analysis; then, the improved algorithm DGBN is used to calculate the hydrothermal intensity index (TII). The algorithm constructs a multi-level gradient boosting tree (GBT) model and combines a variety of statistical and machine learning techniques to analyze the main parameters affecting the TII value and their specific contributions; this process not only improves the calculation accuracy of TII, but also lays the foundation for subsequent in-depth analysis, such as obtaining the main controlling factors of hydrothermal migration and calculating the total geothermal intensity of the entire reservoir; 2) The present invention also classifies the calculated TII values ​​and formulates reservoir effectiveness evaluation standards. These classification results will provide guidance for oil and gas exploration and help oilfield workers optimize resource allocation and development strategies. By applying this method, the hydrothermal strength of different reservoirs can be effectively evaluated, thereby identifying areas with higher development potential. The present invention not only provides a novel method for calculating hydrothermal strength indicators, but also has important practical value in reservoir evaluation, can promote the efficient development of oil and gas resources, and has broad application prospects and economic benefits.

[0016] 3) The present invention constructs a quantitative model to quickly evaluate the hydrothermal activity intensity of the reservoir, providing a scientific basis for the development, management and optimization of the reservoir, and helping to achieve efficient utilization of underground resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings.

[0018] Figure 1 It is a flow chart of a method for calculating a hydrothermal intensity index TII in a reservoir according to an embodiment of the present invention; Figure 2 It is a detailed flow chart of a method for calculating a hydrothermal intensity index TII in a reservoir according to an embodiment of the present invention; Figure 3 is a schematic diagram of the structure of a TII prediction model according to an embodiment of the present invention; Figure 4 It is a schematic diagram of comparison between the actual value and the predicted value of the quantitative characterization method of the hydrothermal intensity index according to one embodiment of the present invention; Figure 5 It is a schematic diagram of a reservoir plane affected by hot fluid calculated according to the TII index in one embodiment of the present invention; Figure 6 It is a schematic diagram of the sorting of correlation factors of the hydrothermal intensity index influencing parameters according to an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution in the embodiment of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiment is a part of the embodiment of the present invention, not all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] The present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the relevant invention, rather than to limit the invention. It is also necessary to explain that, for ease of description, only the parts related to the relevant invention are shown in the accompanying drawings.

[0021] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application may be combined with each other.

[0022] The method for calculating the hydrothermal intensity index TII in the reservoir of the present invention is as follows: Figure 1 As shown, the following steps are included: S100, collecting data related to reservoir characteristics in the oil and gas field as input data; the input data includes parameters for characterizing the source and genesis of hydrothermal fluids, parameters reflecting the chemical composition of fluids and their evolution process, parameters affecting the thermodynamic properties of the reservoir, and parameters related to the phase state and physical properties of reservoir fluids; S200, preprocessing the input data to obtain preprocessed data; S300, inputting the preprocessed data into the trained TII prediction model to obtain the predicted value of the comprehensive hydrothermal intensity index TII: The TII prediction model is constructed based on a deep gradient boosting network; the TII prediction model includes a first hidden layer, a second hidden layer, and an output layer; the first hidden layer is used to standardize the preprocessed data to obtain standardized data; the second hidden layer is used to weight and nonlinearly combine the standardized data to obtain the hydrothermal intensity index TII as the initial TII; the output layer includes multiple GBT models; the output layer is used to calculate the residual of each GBT model, use the residual as the target value, and combine the hydrothermal intensity index TII output by the GBT model of the previous layer to update the GBT model of the next layer, thereby obtaining the predicted value of the comprehensive hydrothermal intensity index TII; The input of the first GBT model is the initial TII.

[0023] In order to more clearly illustrate the method for calculating the hydrothermal intensity index TII in the reservoir according to the present invention, the following is combined with the attached Figure 2 , each step in an embodiment of the method of the present invention is described in detail.

[0024] The present invention aims to provide a method for rapid calculation of reservoir effectiveness based on the hydrothermal intensity index (TII), mainly to achieve the following goals: by constructing a quantitative model, the hydrothermal activity intensity of the reservoir can be quickly calculated, thereby significantly shortening the time and resources required for traditional calculation (i.e., evaluation) methods. Provide a scientific basis for the development, management, and optimization of reservoirs, and help achieve efficient utilization of underground resources. Provide engineers and decision makers with accurate and reliable reservoir effectiveness evaluation, support the formulation of more effective mining strategies, reduce risks, and improve economic benefits. Provide a new research perspective on the interaction between reservoir characteristics and hydrothermal activity, and promote theoretical development and practical application in related fields such as geology and mineral resource development. Use advanced machine learning technology, combined with cross-validation and feature importance analysis, to ensure the accuracy and generalization ability of the established model, thereby enhancing its reliability in practical applications. By achieving these goals, the present invention will provide strong technical support for the sustainable development and management of underground resources. Specifically as follows: S100, collecting data related to reservoir characteristics in the oil and gas field as input data; the input data includes parameters for characterizing the source and genesis of hydrothermal fluids, parameters reflecting the chemical composition of fluids and their evolution process, parameters affecting the thermodynamic properties of the reservoir, and parameters related to the phase state and physical properties of reservoir fluids; In this embodiment, data related to reservoir characteristics are collected from actual oil and gas fields, including but not limited to trace elements δEu, oxygen isotope ratio δ 18 O, reservoir temperature and pressure and other characteristic variables. These parameters are considered to be the main factors affecting the intensity of hydrothermal activity. Trace element δEu: δEu refers to the isotope ratio of europium in the reservoir, which is an indicative parameter of the intensity of hydrothermal activity. Studies have shown that when δEu>1, it indicates that there may be strong hydrothermal activity in the reservoir, because the distribution of europium is significantly affected by the hydrothermal fluid. 18 O value: δ 18 O refers to the ratio of oxygen isotopes and is often used to indicate the source and temperature of the fluid. 18 When O<-10‰, it indicates a possible low-temperature hydrothermal environment, indicating that the fluid has undergone a relatively low-temperature mineralization process. Temperature (T): The temperature of hydrothermal activity is an important factor affecting mineral formation and chemical reaction rates. Temperature is usually expressed in degrees Celsius (°C) and is directly related to the physical and chemical properties of the fluid. Pressure (P): The pressure in the reservoir is a key parameter affecting hydrothermal flow and mineralization processes. Pressure is usually expressed in megapascals (MPa) and directly affects the phase state and chemical reactions of the fluid.

[0025] S200, preprocessing the input data to obtain preprocessed data; In this embodiment, after data collection, data cleaning is performed to ensure the data quality of the input model and improve the accuracy and reliability of the data by eliminating missing values, outliers and unreasonable data.

[0026] S300, inputting the preprocessed data into a trained TII prediction model to obtain a predicted value of a comprehensive hydrothermal intensity index TII.

[0027] In this embodiment, the TII prediction model is constructed based on a deep gradient boosting network DGBN; including a first hidden layer, a second hidden layer, and an output layer, such as Figure 3 As shown; The first hidden layer (i.e., the first GBT layer) is used to standardize the preprocessed data to obtain standardized data; In the present invention, the Z-score standardization method is preferably used to standardize geological parameters of different units and dimensions to ensure that each parameter is effectively compared on the same scale. Specifically, the formula for Z-score standardization is: The formula for Z-score standardization is: ; Among them, X is the original data value, μ is the sample mean, and σ is the sample standard deviation. Through this formula, the standardized value Z obtained will follow the standard normal distribution (mean is 0, standard deviation is 1), making the parameters comparable when compared.

[0028] The second hidden layer (second GBT layer) is used to perform weighted and nonlinear combination on the standardized data to obtain the hydrothermal intensity index TII as the initial TII; the details are as follows: ; in, represents the hydrothermal intensity index TII, p represents the nonlinear adjustment factor, and controls δ 18 The effect of O on TII can be determined through data analysis. , , , represent the standardized trace element δEu, oxygen isotope ratio δ 18 O, reservoir temperature, reservoir pressure, k 1 represents the influence of δEu on TII, which is a positive value used to amplify or reduce the influence of δEu on TII. α represents the weight of δEu on TII, reflecting its relative importance in the assessment of hydrothermal intensity. Its optimal value can be determined through training data. β represents δ 18 The weight of O on TII is similar to α and is obtained through data training. γ represents the reservoir temperature weight. represents the weight of reservoir pressure, and c is a constant used to adjust the baseline value to ensure that the model output is consistent with the actual situation.

[0029] The present invention is based on Tanh and Sigmoid activation functions, combined with a hierarchical adaptive adjustment mechanism, to dynamically adjust weights according to the importance of features at different levels. Each level performs nonlinear transformations on features to varying degrees, so that the output of each layer can better adapt to the different structures of the data. , calculate the importance of each feature to the model prediction, and assign a dynamic weight to each feature based on the self-attention mechanism. The weight is calculated by the following formula: ; in, Represents the weight of the feature, i.e., α, β, γ, , are parameters used to characterize the source and origin of hydrothermal fluids, which are δEu, δ 18 O, reservoir temperature or reservoir pressure, , Both represent weight coefficients, Represents the feature importance evaluation transformation value of the nth layer, It is n The weight parameters of the layer.

[0030] Among them, the permutation feature importance correlation factors (i.e., feature importance evaluation transformation values) of different influencing factors are shown in Table 1 and Figure 6 As shown: Table 1

[0031] The output layer contains multiple GBT models; the output layer is used to calculate the residual of each GBT model, take the residual as the target value, and update the GBT model of the next layer in combination with the hydrothermal intensity index TII output by the previous layer GBT model, and then obtain the predicted value of the comprehensive hydrothermal intensity index TII. The details are as follows: The output layer receives the output of the second hidden layer and produces the final TII prediction. This layer can contain one or more GBT models, and its output is the final estimate of the comprehensive hydrothermal intensity indicator.

[0032] ;

[0033] Mathematical expression of the hierarchical structure: The overall prediction function F(x) of the DGBN model is expressed in a nested form as follows: ; in, represents the predicted value of the comprehensive hydrothermal intensity index TII, is the weight of each loss item, indicating the contribution of different loss items to the total loss, It is n −1 layer TII prediction value, It is a factor that controls the impact of residuals on total loss. By adjusting it, the sensitivity of the model to residuals can be affected. Indicates n The hydrothermal intensity index TII of the layer, is the output value of the nonlinear transformation inside the residual block, corresponding to the prediction correction of the network for the features of this layer, and is the prediction residual of the nth layer. It is a standardized inter-layer residual calculated based on geological prior knowledge, reflecting the offset of the actual rock formation physical parameters. It represents the actual residual, that is, the difference between layer n and layer n-1, which allows the network to learn the error of the current layer and the information passed from the previous layer. Represents an optimization operation, where the output of each layer is used as the input of the next layer, so that the historical information of each layer can be retained, and complex prediction tasks can be performed through layer-by-layer optimization, and the optimal residual function can be found through the optimization process , so that the overall loss function is minimized, and M represents the total number of iterations of the GBT model in each layer.

[0034] In addition, the loss function of the DGBN model during training is: ; ; ; in, Represents the loss value, that is, the prediction residual, It is the real TII value. In actual application, the real TII value is the standard value set according to geological conditions. is the TII value predicted by the model, 𝑛 is the total number of test samples, The features of the output of the n-1th layer, is the convolution kernel weight matrix, which extracts geological features, i.e., the features of parameters used to characterize the source and genesis of hydrothermal fluids, R n2 is the convolution kernel weight matrix, which expands the features back to the original dimension, and ReLU() is the activation function. , are bias parameters, is the measured geological characteristics of the kth sampling point, is a standard value set according to geological conditions. N is the total number of sampling points.

[0035] In this way, the loss function not only depends on the prediction error, but also takes into account the correction of the prediction error at each layer, such as Figure 4 shown.

[0036] That is, during the training process, the DGBN is first trained using the training set, and then the validation set is used to evaluate the performance of the model. If the error on the validation set is lower than the preset threshold, or the accuracy on the validation set is greater than 90%, the training is stopped and the model is used for prediction, otherwise, the learning step size is adjusted and training is continued.

[0037] S300 also includes: numerical simulation of hydrothermal migration pathways. Hydrothermal migration pathways are mainly divided into faults and fracture networks. Seismic data is used to extract connectivity information of faults and fractures, and TII data, which are interpolated into the grid. The fault master control is an indicator used to quantify the control ability of faults on hydrothermal migration pathways. By analyzing the correlation between hydrothermal activity intensity (TII) and fault spatial distribution, it is determined whether faults are the main controlling factor of hydrothermal migration: ; in, Indicates the main control degree of the fault. The high TII value area is the area with significant hydrothermal activity intensity, that is, the area with TII greater than the set intensity is preferably TII>5). The fault zone is a belt-shaped area extending along the fault direction and on both sides. represents the overlapping area between the ith TII high value area and the fault area, represents the slip rate at the overlap between the ith TII high value area and the fault area, represents the maximum slip rate in all overlapping faults, represents the average value of TII in the overlapping area between the ith TII high value area and the fault area, represents the maximum TII value among all overlapping fault layers, Indicates the total area of ​​the fault zone; the present invention preferably has FDI greater than or equal to 50%, and faults are the main controlling factor of hydrothermal migration; Assume that there are 12 TII high-value areas and the overlapping area of ​​the fault area is 27.39 km 2 The total area of ​​the fault zone is 43.88 km 2 , is 0.28, is 0.74, the fault master degree =58.31%, then faults are the main controlling factor for hydrothermal migration.

[0038] The master control degree of the fracture network is an indicator used to quantify the ability of the fracture network to control the path of hydrothermal migration. By analyzing the correlation between the hydrothermal activity intensity (TII) and the spatial distribution of the fracture network, it is determined whether the fracture network is the main controlling factor of hydrothermal migration. Its core is to evaluate the contribution of the fault area to the formation of hydrothermal channels. The connectivity of the fractures is generated through ant body tracking, curvature attributes or discrete fracture network (DFN) model to generate a three-dimensional fracture network, including fracture surface geometric parameters (strike, dip, length) and topological structure. If the distance between two fracture surfaces in three-dimensional space is ≤ grid resolution (such as 1 m), it is marked as an intersection node, and the intersection node coordinates are mapped to TII three-dimensional grid data to extract the TII value of the corresponding position. The master control degree of the fracture network (Cctrl) is calculated using the formula: ; in, represents the master control degree of the fracture network (in the present invention, the master control degree of the fracture network is preferably greater than 0.3, then the fracture network is the master control factor of hydrothermal migration), K represents the total number of fracture intersection nodes, TII intersection point i represents the TII value of the i-th intersection node (that is, the TII value is calculated by the above formula for calculating the hydrothermal intensity index TII), TII 围岩值 Represents the average TII value in the non-crack area within the model range (calculation method: excluding the average TII value of the grid outside the crack buffer zone).

[0039] Assume TII 围岩值 is 0.0234, K is 851, then the crack master control degree , then the fracture network is not the main controlling factor for hydrothermal migration.

[0040] S300 also includes obtaining the total amount of geothermal intensity of the entire reservoir (the total amount of hydrothermal distribution and geothermal fluid in the entire reservoir) calculated according to the following formula: ; in, represents the hydrothermal intensity (or fluid influence) of the i, j, and k grid cells. It is a predicted value and can be dynamically changing. It is a dynamic weighting factor that can be adjusted according to geological characteristics (such as temperature, pressure, etc.) to adjust the impact of TII of each grid unit on the total amount of fluid. Δx, Δy, Δz are the intervals of the subdivided cells in the study area in the x, y, and z directions, respectively. m, n, and l are the total number of subdivided grid units in the x, y, and z directions, respectively. k, j, and i are the subdivided grid units in the x, y, and z directions, respectively. It helps to quickly identify potential hydrothermal activity areas, provide engineers and decision makers with accurate and reliable reservoir effectiveness evaluation, support the formulation of more effective mining strategies, reduce risks and improve economic benefits.

[0041] S300 also includes: Result interpretation and visualization: Generate detailed hydrothermal intensity for each sample: Integrated hydrothermal intensity index (TII): The calculated TII value reflects the hydrothermal activity intensity of the sample. The relationship between the integrated hydrothermal intensity index (TII) and depth shows the trend of TII value with depth, such as Figure 5 As shown, it clearly reflects the intensity of hydrothermal activity in the sample. It reveals the impact of hydrothermal activity on reservoir characteristics under different depth conditions, which can help researchers understand the dynamic process of deep hydrothermal fluids. By analyzing the depth data, it is possible to identify the high and low changes in TII, thereby evaluating the reservoir effectiveness in different depth intervals.

[0042] Classification: According to the TII value, the samples are classified into different hydrothermal activity levels (such as extremely strong, strong, moderate, weak and no hydrothermal activity), which helps to quickly identify potential hydrothermal activity areas. Parameter analysis: The main parameters that affect the TII value are listed in detail, including trace elements δEu, δ 18 O value, temperature, pressure, etc., as well as the specific values ​​of these parameters in the current sample and their contribution to TII.

[0043] The specific steps are as follows: 1) Set classification standards and use methods such as quantile method to make reasonable divisions; 2) Analyze the reservoir characteristics corresponding to each category of TII value, and formulate reservoir effectiveness evaluation criteria based on geological background and actual data; 3) Finally, a reservoir evaluation system based on TII value is formed to provide a scientific basis for subsequent reservoir development, management and optimization, as shown in Table 2: Table 2

[0044] A system for calculating a hydrothermal intensity index TII in a reservoir according to a second embodiment of the present invention comprises: A data acquisition module is configured to collect data related to reservoir characteristics in the oil and gas field as input data; the input data includes parameters for characterizing the source and genesis of hydrothermal fluids, parameters reflecting the chemical composition of fluids and their evolution process, parameters affecting the thermodynamic properties of the reservoir, and parameters related to the phase state and physical properties of reservoir fluids; A preprocessing module, configured to preprocess the input data to obtain preprocessed data; The model prediction module is configured to input the preprocessed data into the trained TII prediction model to obtain the predicted value of the comprehensive hydrothermal intensity index TII: The TII prediction model is constructed based on a deep gradient boosting network; the TII prediction model includes a first hidden layer, a second hidden layer, and an output layer; the first hidden layer is used to standardize the preprocessed data to obtain standardized data; the second hidden layer is used to weight and nonlinearly combine the standardized data to obtain the hydrothermal intensity index TII as the initial TII; the output layer includes multiple GBT models; the output layer is used to calculate the residual of each GBT model, use the residual as the target value, and combine the hydrothermal intensity index TII output by the GBT model of the previous layer to update the GBT model of the next layer, thereby obtaining the predicted value of the comprehensive hydrothermal intensity index TII; The input of the first GBT model is the initial TII.

[0045] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process and related instructions of the system described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0046] It should be noted that the above embodiment provides a system for calculating the hydrothermal intensity index TII in the reservoir, which is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the modules or steps in the embodiments of the present invention can be decomposed or combined. For example, the modules in the above embodiments can be combined into one module, or further divided into multiple sub-modules to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of the present invention are only for distinguishing the modules or steps, and are not regarded as improper limitations on the present invention.

[0047] A device for calculating a hydrothermal intensity index TII in a reservoir according to the third embodiment of the present invention comprises: at least one processor; and a memory communicatively connected to at least one of the processors; wherein the memory stores instructions executable by the processor, and the instructions are used to be executed by the processor to implement the above-mentioned method for calculating the hydrothermal intensity index TII in a reservoir.

[0048] A computer-readable storage medium according to a fourth embodiment of the present invention stores computer instructions, and the computer instructions are used to be executed by the computer to implement the above-mentioned method for calculating the hydrothermal intensity index TII in a reservoir.

[0049] Technicians in the technical field can clearly understand that, for the convenience and brevity of description, the specific working process and related instructions of the above-described device for calculating the hydrothermal intensity index TII in the reservoir and the computer-readable storage medium can refer to the corresponding process in the aforementioned method example and will not be repeated here.

[0050] Those skilled in the art should be able to appreciate that the modules and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented with electronic hardware, computer software, or a combination of the two, and the programs corresponding to the software modules and method steps can be placed in random access memory (RAM), memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium known in the technical field. In order to clearly illustrate the interchangeability of electronic hardware and software, the composition and steps of each example have been generally described in the above description according to the function. Whether these functions are performed in electronic hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0051] The terms "first", "second", "third", etc. are used to distinguish similar objects rather than to describe or indicate a particular order or sequence.

[0052] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A method for calculating the hydrothermal intensity index TII in a reservoir, characterized in that: The method includes: S100, collecting data related to reservoir characteristics in the oil and gas field as input data; the input data includes parameters for characterizing the source and genesis of hydrothermal fluids, parameters reflecting the chemical composition of fluids and their evolution process, parameters affecting the thermodynamic properties of the reservoir, and parameters related to the phase state and physical properties of reservoir fluids; S200, preprocessing the input data to obtain preprocessed data; S300, inputting the preprocessed data into the trained TII prediction model to obtain the predicted value of the comprehensive hydrothermal intensity index TII: The TII prediction model is constructed based on a deep gradient boosting network; the TII prediction model includes a first hidden layer, a second hidden layer, and an output layer; the first hidden layer is used to standardize the preprocessed data to obtain standardized data; the second hidden layer is used to weight and nonlinearly combine the standardized data to obtain the hydrothermal intensity index TII as the initial TII; the output layer includes multiple GBT models; the output layer is used to calculate the residual of each GBT model, use the residual as the target value, and combine the hydrothermal intensity index TII output by the GBT model of the previous layer to update the GBT model of the next layer, thereby obtaining the predicted value of the comprehensive hydrothermal intensity index TII; The input of the first GBT model is the initial TII.

2. A method for calculating the hydrothermal intensity index TII in a reservoir according to claim 1, characterized in that: The preprocessing includes data cleaning, missing value and outlier elimination.

3. A method for calculating the hydrothermal intensity index TII in a reservoir according to claim 1, characterized in that: The parameters used to characterize the source and genesis of the hydrothermal fluid include trace element δEu; the parameters reflecting the chemical composition of the fluid and its evolution process include oxygen isotope ratio δ 18 O; the parameters affecting the thermodynamic properties of the reservoir include reservoir temperature; the parameters related to the phase state and physical properties of the reservoir fluid include reservoir pressure.

4. A method for calculating the hydrothermal intensity index TII in a reservoir according to claim 3, characterized in that: The standardized data are weighted and nonlinearly combined to obtain the hydrothermal intensity index TII, and the method is as follows: ； in, represents the hydrothermal intensity index TII, p represents the nonlinear adjustment factor, , , , represent the standardized trace element δEu, oxygen isotope ratio δ 18 O, reservoir temperature, reservoir pressure, k 1 represents the influence of δEu on TII, α and β represent δEu and δ 18 O represents the weight of TII, γ represents the weight of reservoir temperature, represents the weight of reservoir pressure, and c is a constant used to adjust the baseline value.

5. A method for calculating the hydrothermal intensity index TII in a reservoir according to claim 4, characterized in that: The adaptive dynamic weight is calculated as follows: ； in, Represents the weight of the feature, i.e., α, β, γ, , are parameters used to characterize the source and origin of hydrothermal fluids, which are δEu, δ 18 O, reservoir temperature or reservoir pressure, , Both represent weight coefficients, Represents the feature importance evaluation transformation value of the nth layer, It is n The weight parameters of the layer.

6. A method for calculating the hydrothermal intensity index TII in a reservoir according to claim 4, characterized in that: The predicted value of the comprehensive hydrothermal intensity index TII is obtained by: ； ； in, represents the predicted value of the comprehensive hydrothermal intensity index TII, represents the weight of each loss term, Indicates n The TII prediction value of the layer, is δEu, δ 18 The input vector consists of O, reservoir temperature T, and reservoir pressure P. is the output value of the nonlinear transformation inside the residual block, corresponding to the prediction correction of the network for the features of this layer, and is the prediction residual of the nth layer. It is a standardized interlayer residual calculated based on geological prior knowledge, reflecting the offset of the actual rock formation physical property parameters and representing the actual residual. represents the factor that controls the effect of the residual on the total loss, Indicates n The hydrothermal intensity index TII of the layer, Represents an optimization operation, which finds the optimal residual function through the optimization process , so that the overall loss function is minimized, and M represents the total number of iterations of the GBT model in each layer.

7. A method for calculating the hydrothermal intensity index TII in a reservoir according to claim 6, characterized in that: The loss function of the TII prediction model during training is: ； ； ； in, Represents the loss value, that is, the prediction residual, It is the real TII value. In actual application, the real TII value is the standard value set according to geological conditions. is the TII value predicted by the model, 𝑛 is the total number of test samples, The features of the output of the n-1th layer, is the convolution kernel weight matrix, which extracts geological features, i.e., the features of parameters used to characterize the source and genesis of hydrothermal fluids, R n2 is the convolution kernel weight matrix, which expands the features back to the original dimension, and ReLU() is the activation function. , are bias parameters, is the measured geological characteristics of the kth sampling point, is a standard value set according to geological conditions. N is the total number of sampling points.

8. A method for calculating the hydrothermal intensity index TII in a reservoir according to claim 4, characterized in that: After step S300, the method further includes obtaining the main controlling factors of the hydrothermal migration: Extracting connectivity information of faults and fractures and TII data from seismic data, interpolating them into a grid, and calculating the master control degree of faults and the master control degree of fracture networks, and determining the master control factors of hydrothermal migration based on the master control degrees of faults and fracture networks; the master control factors include faults and fracture networks; The method for obtaining the fault master control degree is as follows: ； in, Indicates the fault master degree, represents the overlapping area between the ith TII high value area and the fault area. The TII high value area is the area with significant hydrothermal activity, that is, the area where the TII is greater than the set intensity. The fault area is the belt-shaped area extending along the fault direction and on both sides. represents the average slip rate at the overlap between the ith TII high value area and the fault area, represents the maximum slip rate in all overlapping faults, represents the average value of TII in the overlapping area between the ith TII high value area and the fault area, represents the maximum TII value among all overlapping fault layers, represents the total area of ​​the fault zone; The method for obtaining the master control degree of the fracture network is as follows: ； in, represents the master control degree of the crack network, K represents the total number of crack intersection nodes, TII 交汇点i Represents the TII value of the i-th intersection node, TII 围岩值 It represents the average TII value in the non-crack area within the model range.

9. A method for calculating the hydrothermal intensity index TII in a reservoir according to claim 4, characterized in that: After step S300, the total amount of geothermal intensity of the entire reservoir is calculated: ； in, T represents the total geothermal intensity of the entire reservoir, Indicates i , j , k Hydrothermal intensity of the grid cell, Δ x , Δ y , Δ z are the x, y, and z direction intervals of the subdivision cells of the study area, m , n , l are the total number of mesh elements in the x, y, and z directions, respectively.

10. A system for calculating the hydrothermal intensity index TII in a reservoir, characterized in that: The system includes: A data acquisition module is configured to collect data related to reservoir characteristics in the oil and gas field as input data; the input data includes parameters for characterizing the source and genesis of hydrothermal fluids, parameters reflecting the chemical composition of fluids and their evolution process, parameters affecting the thermodynamic properties of the reservoir, and parameters related to the phase state and physical properties of reservoir fluids; A preprocessing module, configured to preprocess the input data to obtain preprocessed data; The model prediction module is configured to input the preprocessed data into the trained TII prediction model to obtain the predicted value of the comprehensive hydrothermal intensity index TII: The TII prediction model is constructed based on a deep gradient boosting network; the TII prediction model includes a first hidden layer, a second hidden layer, and an output layer; the first hidden layer is used to standardize the preprocessed data to obtain standardized data; the second hidden layer is used to weight and nonlinearly combine the standardized data to obtain the hydrothermal intensity index TII as the initial TII; the output layer includes multiple GBT models; the output layer is used to calculate the residual of each GBT model, use the residual as the target value, and combine the hydrothermal intensity index TII output by the GBT model of the previous layer to update the GBT model of the next layer, thereby obtaining the predicted value of the comprehensive hydrothermal intensity index TII; The input of the first GBT model is the initial TII.

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