Novel heterogeneous reservoir starting pressure and remaining oil distribution comprehensive analysis method

Abstract

The invention discloses a novel heterogeneous reservoir starting pressure and remaining oil distribution comprehensive analysis method, which relates to the technical field of reservoir analysis, and comprises the steps of using multi-scale data fusion, intelligent algorithm optimization and a dynamic monitoring and feedback mechanism to comprehensively analyze the starting pressure and the remaining oil distribution rule. The method has the advantages that high-precision data such as the permeability and the porosity obtained through micro-scale core analysis serve as basic input of reservoir numerical simulation, the simulation result is closer to the real reservoir seepage condition, the influence of the heterogeneity and the geologic structure of a reservoir on fluid flow is better considered in combination with macro-scale well logging seismic data, and the simulation result is more accurate. The precision and reliability of numerical reservoir simulation are improved, more powerful support is provided for formulation and optimization of a reservoir development scheme, and data such as porosity and oil saturation provided by microscopic core analysis are combined with reservoir volume and structure information reflected by macroscopic logging seismic data.

Description

Technical Field

[0001] The invention relates to the technical field of reservoir analysis, in particular to a novel comprehensive analysis method for the starting pressure and residual oil distribution of heterogeneous reservoirs. Background Art

[0002] A heterogeneous reservoir refers to a reservoir with spatial variations in rock physical properties (such as porosity and permeability), fluid properties (such as density and viscosity) and geological structure (such as formation thickness and fault distribution). This variation can occur at different scales, from the microscopic pore scale to the macroscopic reservoir scale. Heterogeneous reservoirs are widely present in oil and gas reservoirs. Their complex geological structure and fluid seepage characteristics bring many challenges to oil and gas extraction. Accurately grasping the starting pressure and remaining oil distribution of heterogeneous reservoirs is extremely critical for optimizing development plans and improving recovery rates.

[0003] Research on residual oil distribution at the microscale mainly focuses on fluid distribution and displacement mechanism at the pore scale, while research at the macroscale focuses on the residual oil distribution characteristics of the entire reservoir. There is a lack of effective scale conversion methods between the two, making it difficult to accurately predict the macroscopic residual oil distribution based on microscopic mechanisms, and it is also difficult to deeply understand the formation and distribution mechanism of microscopic residual oil from macroscopic phenomena. To this end, we propose a new comprehensive analysis method for the start-up pressure and residual oil distribution of heterogeneous reservoirs. Summary of the invention

[0004] The purpose of the present invention is to provide a novel comprehensive analysis method for the starting pressure and remaining oil distribution of heterogeneous reservoirs.

[0005] In order to solve the problems raised in the above background technology, the present invention provides the following technical solutions: a novel method for comprehensive analysis of startup pressure and residual oil distribution in heterogeneous reservoirs, comprising the use of multi-scale data fusion, intelligent algorithm optimization, and dynamic monitoring and feedback mechanism to comprehensively analyze the startup pressure and residual oil distribution law. The specific operation steps of the novel method for comprehensive analysis of startup pressure and residual oil distribution in heterogeneous reservoirs are as follows: Step 1: Collect micro-scale core analysis data, meso-scale well logging data, and macro-scale seismic data; Step 2: Nest the unit models of micro, meso and macro scales to form a reservoir model, and integrate the collected production dynamic data with the geological static data; Step 3: Map the microscopic, mesoscopic and macroscopic scale data using the data conversion model, train the model using the mapped data, and train the model to predict the starting pressure; Step 4: Use multi-scale simulation equations to simulate the flow and distribution of heterogeneous residual oil at the micro and macro levels; Step 5: All optical fiber sensor data are fused and processed, characteristic parameters are extracted from the processed data, and the pressure and temperature changes of the heterogeneous reservoir are monitored in real time; Step 6: Assimilate the real-time collected production data and integrate it into the model, and provide real-time feedback and adaptive updates to the analysis model.

[0006] As a further solution of the present invention: in the step 1, core samples are obtained according to drilling, and micro-pore structure analysis is performed on the core samples using micro-CT technology to obtain the lithology, pore structure, permeability and porosity physical properties of the core. According to the pore size, the flow units are divided into three types: micropores with a radius of less than 0.1um, small holes with a radius between 0.1um and 1um, and large holes with a radius of more than 1um. The scattering intensity of gamma rays after the interaction with formation materials is measured by density logging to determine the mesoscopic formation distribution. Flow units based on sedimentary microphases are divided within the formation. The division of mesoscopic flow units reflects the heterogeneity between different sedimentary microphases in the same formation. Seismic detectors are arranged on the ground to excite and receive artificial seismic waves, and the refraction and reflection signals of seismic waves in the formation are recorded and analyzed. The seismic data show the macroscopic structure of the underground formation, including the reservoir structure, the change in formation thickness, and the connectivity between different strata. The macroscopic flow units of the entire reservoir are divided. The division of macroscopic flow units grasps the fluid flow trend of the reservoir and the macroscopic pattern of remaining oil distribution as a whole.

[0007] As a further solution of the present invention: in the step 2, the flow unit models of microscopic, mesoscopic and macroscopic scales are integrated into a multi-scale flow unit nested reservoir model. In the model, the microscopic flow unit is nested in the mesoscopic flow unit, and the mesoscopic flow unit is nested in the macroscopic flow unit. The scales influence and restrict each other through the established association relationship. The production dynamic data of the oil well, including the change of the oil well production, bottom hole pressure and water content over time, are collected. By randomly perturbing the model parameters, a group of set members is generated. Each set member is time-advanced according to the reservoir model. During the time advancement process, the reservoir model is used to predict the production dynamic data corresponding to each set member, and the difference between the predicted production dynamic data of each set member and the actual observed data is calculated. The weight of each set member is determined by combining the prediction error covariance matrix and the observation error covariance matrix. The calculation formula is: ; in, represents the elements of the forecast error covariance matrix, Representative The first in the state vector of the ensemble member prediction variables, represents the first variables, Representative The first in the ensemble member's prediction state vector variables, represents the first variables, Represents the number of all members in the set. The diagonal elements of the prediction error covariance matrix represent the variance of the prediction errors of each state parameter, and the off-diagonal elements show the correlation between the prediction errors of different parameters. The prediction error covariance matrix is ​​used to update the weights when estimating the reservoir state, and the set members are weighted averaged to make the reservoir model more consistent with the actual production situation and enable the continuous integration of production dynamic data and geological static data.

[0008] As a further solution of the present invention: In the step 3, a microscopic, mesoscopic and macroscopic data conversion model based on a neural network is established. Since the dimensions and numerical ranges of the microscopic, mesoscopic and macroscopic data are different, the formula is used: ; in, represents the normalized value, X represents the original data, Represents the minimum value of the data, Represents the maximum value of the data. The above formula can be used to map different micro, meso and macro data to the (0,1) interval. The processed micro core data, meso observation well data and macro seismic data are divided into training set, validation set and test set. The training set is used to train the neural network model, accounting for 60%-80% of the total data set. The validation set is used to adjust the hyperparameters of the model, including the number of hidden layers, the number of neurons and the learning rate, accounting for 10%-20% of the total data set. The test set is used to evaluate the final performance of the model, accounting for 10%-20% of the total set. During the training process, the training set data is input into the model, and iterative training is performed according to the steps of forward propagation, loss calculation, back propagation and weight update.

[0009] As a further solution of the present invention: in step 3, each iteration becomes a training round epoch, and the error between the output and the real micro data is calculated, and the formula is: ; in, represents the sample size, Represents real micro data, represents the micro data predicted by the neural network, Represents the average deviation between the predicted value and the true value of the model. After each epoch, the loss value on the validation set is calculated, and the trend of the validation loss is observed to monitor the fitting effect of the model. The correlation coefficient is used The performance of the model on the test set was evaluated. After evaluation, the model was applied to actual microscopic, mesoscopic and macroscopic data. Based on geological exploration data and production dynamic data of nearby oil wells, the model was used to predict the start-up pressure at different well locations and different development stages.

[0010] As a further solution of the present invention: In the step 4, the collected reservoir geological data, including core analysis data, well logging data and seismic data, are used to describe the spatial distribution pattern of geological variables using a multi-point geostatistical method, and the remaining oil distribution in the reservoir is predicted by a multi-scale simulation equation, the equation being: ; in, represents the temperature of the particle, represents the temperature of the fluid, represents the radius of the particle, represents the heat transfer coefficient between particles and fluid, represents the heat release rate of the chemical reaction on the interface per unit area, represents the surface of the fluid, represents the unit external normal vector of the surface at the calculation point, represents thermal conductivity, represents the temperature gradient, represents the velocity of the fluid, represents the density of the fluid, Represents the specific heat capacity of the fluid. The above equation shows that the mass flux of particle dissolution and adsorption on the interface between the particle surface and the surrounding fluid is equal at the micro and macro scales. The finite element method is used to divide the solution area into a finite number of units. The approximate solution is constructed on each unit to approximate the true solution of the entire area, accurately simulating the flow and distribution of residual oil in heterogeneous reservoirs at the micro and macro scales.

[0011] As a further solution of the present invention: in the step 4, in the numerical simulation of the remaining oil distribution, the relevant parameters in the remaining oil distribution simulation are encoded, and a multi-level encoding structure is adopted to incorporate the geological parameters, fluid parameters and development operation parameters of the remaining oil distribution into the encoding system, and a certain number of individuals are randomly generated to form an initial population, each individual represents a remaining oil distribution situation, and the remaining oil saturation distribution in the initial population is initialized with a bias by referring to the statistical law of the remaining oil distribution in the produced oil reservoirs at different development stages. The initialization function is: ; in, represents the remaining oil recovery factor, represents the development cost, Represents the development cycle, represents the environmental impact indicator, , , and represents the weight coefficient, , represents the regional difference factor, represents the policy factor, represents the technical feasibility factor, , , ; An indicator representing the fitness of an individual under multiple objectives, using a selection operator to select fitness from the current population The higher individual is taken as the parent, and the selected parent individuals are randomly paired, and each gene position of the parent individual is exchanged using uniform crossover. The offspring individuals obtained after the crossover operation are randomly selected for mutation operation on the gene position on the chromosome of the offspring individual to change its gene value. The mutation operation increases the diversity of the population and prevents the algorithm from converging to the local optimal solution too early. After each iteration, the optimal individual in the population that meets the termination condition is output as the result of the remaining oil distribution simulation.

[0012] As a further solution of the present invention: in step five, the optical fiber sensor is buried near the reservoir, the sampling frequency of pressure and temperature is set to 1 Hz-5 Hz, the optical fiber sensor converts the perceived changes in temperature and pressure into changes in optical signals, the optical fiber sensor is connected to the data acquisition equipment and the monitoring system through an optical fiber jumper, the monitoring system processes and analyzes the received data, fuses the data from all the optical fiber sensors, extracts characteristic parameters related to the reservoir pressure and temperature changes from the processed data, and monitors the reservoir pressure and temperature changes in real time.

[0013] As a further solution of the present invention: In step six, according to the parameters monitored in real time during the production process, different production data have different dimensions and numerical ranges, and different parameters are mapped to the (0,1) interval using a formula, and the preprocessed production data is used to estimate and train the parameters of the neural network model, and the real-time production data is assimilated and integrated into the model to achieve adaptive updating of the model, and update conditions are set according to the degree of change of the production data, the quality of the data and the performance indicators of the model. When the newly collected production data deviates from the model prediction data > 0.1, triggering a model update, using data not used for model training and updating to verify the updated model, and comparing the model's prediction results with the actual production data, including the degree of deviation and correlation coefficient , evaluate the performance improvement after the model update.

[0014] By adopting the above technical solution, compared with the prior art, the beneficial effects of the present invention are: The high-precision data such as permeability and porosity obtained by the micro-scale core analysis of the present invention are used as the basic input for numerical simulation of oil reservoirs, so that the simulation results are closer to the actual oil reservoir seepage situation. Combined with the macro-scale logging seismic data, the influence of reservoir heterogeneity and geological structure on fluid flow is better considered, the accuracy and reliability of numerical simulation of oil reservoirs are improved, and more powerful support is provided for the formulation and optimization of oil reservoir development plans. The porosity, oil saturation and other data provided by the micro-core analysis are combined with the reservoir volume and structural information reflected by the macro-logging seismic data, so that the reserves of the oil reservoir can be calculated more accurately, avoiding the reserve estimation error that may be caused by relying solely on single-scale data, and providing a more accurate basis for the rational development and utilization of oil and gas resources. The present invention optimizes the key parameters of the numerical simulation model by adopting a genetic algorithm, determines the encoding method of the parameters and the fitness function. The fitness function is based on the matching degree between the simulation results and the actual production data (such as oil well production, water content, etc.). Through the selection, crossover and mutation operations of the genetic algorithm, the optimal parameter combination is continuously iterated and searched. This optimization method can effectively improve the prediction accuracy of the numerical simulation model for the distribution of residual oil in heterogeneous reservoirs, avoid simulation errors caused by improper parameter selection, and provide a reliable basis for formulating reasonable residual oil production strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 Schematic diagram of the steps for comprehensive analysis of the starting pressure and remaining oil distribution of a novel heterogeneous reservoir in an embodiment of the present invention. DETAILED DESCRIPTION

[0016] The specific embodiments of the present invention are further described below in conjunction with the accompanying drawings. It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0017] The present invention discloses a novel method for comprehensively analyzing the startup pressure and the residual oil distribution of heterogeneous reservoirs, which includes using multi-scale data fusion, intelligent algorithm optimization, and dynamic monitoring and feedback mechanisms to comprehensively analyze the startup pressure and the residual oil distribution law. The specific operation steps of the novel method for comprehensively analyzing the startup pressure and the residual oil distribution of heterogeneous reservoirs are as follows: Step 1: Collect micro-scale core analysis data, meso-scale well logging data, and macro-scale seismic data; Step 2: Nest the unit models of micro, meso and macro scales to form a reservoir model, and integrate the collected production dynamic data with the geological static data; Step 3: Map the microscopic, mesoscopic and macroscopic scale data using the data conversion model, train the model using the mapped data, and train the model to predict the starting pressure; Step 4: Use multi-scale simulation equations to simulate the flow and distribution of heterogeneous residual oil at the micro and macro levels; Step 5: All optical fiber sensor data are fused and processed, characteristic parameters are extracted from the processed data, and the pressure and temperature changes of the heterogeneous reservoir are monitored in real time; Step 6: Assimilate the real-time collected production data and integrate it into the model, and provide real-time feedback and adaptive updates to the analysis model.

[0018] In one embodiment of the present invention: in step 1, core samples are obtained according to drilling, and micro-pore structure analysis is performed on the core samples using micro-CT technology to obtain the lithology, pore structure, permeability and porosity physical properties of the core. According to the pore size, the flow units are divided into three types: micropores with a radius of less than 0.1um, small holes with a radius between 0.1um and 1um, and large holes with a radius of more than 1um. The scattering intensity of gamma rays after the interaction with formation materials is measured by density logging to determine the mesoscopic formation distribution. Flow units based on sedimentary microphases are divided within the formation. The division of mesoscopic flow units reflects the heterogeneity between different sedimentary microphases in the same formation. Seismic detectors are arranged on the ground to excite and receive artificial seismic waves, and the refraction and reflection signals of seismic waves in the formation are recorded and analyzed. The seismic data show the macroscopic structure of the underground formation, including the reservoir structure, the change in formation thickness, and the connectivity between different strata. The macroscopic flow units of the entire reservoir are divided. The division of macroscopic flow units grasps the fluid flow trend of the reservoir and the macroscopic pattern of remaining oil distribution as a whole.

[0019] In one embodiment of the present invention: in step 2, the flow unit models of micro, meso and macro scales are integrated into a multi-scale flow unit nested reservoir model. In the model, the micro flow unit is nested in the meso flow unit, and the meso flow unit is nested in the macro flow unit. The scales influence and restrict each other through the established association relationship. The production dynamic data of the oil well, including the change of oil well production, bottom hole pressure and water content over time, are collected. By randomly perturbing the model parameters, a group of set members are generated. Each set member is time-advanced according to the reservoir model. During the time advancement process, the reservoir model is used to predict the production dynamic data corresponding to each set member, and the difference between the predicted production dynamic data of each set member and the actual observed data is calculated. The weight of each set member is determined by combining the prediction error covariance matrix and the observation error covariance matrix. The calculation formula is: ; in, represents the elements of the forecast error covariance matrix, Representative The first in the ensemble member's prediction state vector variables, represents the first variables, Representative The first in the ensemble member's prediction state vector variables, represents the first variables, Represents the number of all members in the set. The diagonal elements of the prediction error covariance matrix represent the variance of the prediction errors of each state parameter, and the off-diagonal elements show the correlation between the prediction errors of different parameters. The prediction error covariance matrix is ​​used to update the weights when estimating the reservoir state, and the set members are weighted averaged to make the reservoir model more consistent with the actual production situation and enable the continuous integration of production dynamic data and geological static data.

[0020] In one embodiment of the present invention: In step 3, a microscopic, mesoscopic and macroscopic data conversion model based on a neural network is established. Since the dimensions and numerical ranges of microscopic, mesoscopic and macroscopic data are different, the formula is used: ; in, represents the normalized value, X represents the original data, Represents the minimum value of the data, Represents the maximum value of the data. The above formula can be used to map different micro, meso and macro data to the (0,1) interval. The processed micro core data, meso observation well data and macro seismic data are divided into training set, validation set and test set. The training set is used to train the neural network model, accounting for 60%-80% of the total data set. The validation set is used to adjust the hyperparameters of the model, including the number of hidden layers, the number of neurons and the learning rate, accounting for 10%-20% of the total data set. The test set is used to evaluate the final performance of the model, accounting for 10%-20% of the total set. During the training process, the training set data is input into the model, and iterative training is performed according to the steps of forward propagation, loss calculation, back propagation and weight update.

[0021] In one embodiment of the present invention: In step 3, each iteration becomes a training round epoch, and the error between the output and the real micro data is calculated, and the formula is: ; in, represents the sample size, Represents real micro data, represents the micro data predicted by the neural network, Represents the average deviation between the predicted value and the true value of the model. After each epoch, the loss value on the validation set is calculated, and the trend of the validation loss is observed to monitor the fitting effect of the model. The correlation coefficient is used The performance of the model on the test set was evaluated. After evaluation, the model was applied to actual microscopic, mesoscopic and macroscopic data. Based on geological exploration data and production dynamic data of nearby oil wells, the model was used to predict the start-up pressure at different well locations and different development stages.

[0022] In one embodiment of the present invention: In step 4, the collected reservoir geological data, including core analysis data, well logging data and seismic data, are used to describe the spatial distribution pattern of geological variables using a multi-point geostatistical method, and the remaining oil distribution in the reservoir is predicted by a multi-scale simulation equation, the equation being: ; in, represents the temperature of the particle, represents the temperature of the fluid, represents the radius of the particle, represents the heat transfer coefficient between particles and fluid, represents the heat release rate of the chemical reaction on the interface per unit area, represents the surface of the fluid, represents the unit external normal vector of the surface at the calculation point, represents thermal conductivity, represents the temperature gradient, represents the velocity of the fluid, represents the density of the fluid, Represents the specific heat capacity of the fluid. The above equation shows that the mass flux of particle dissolution and adsorption on the interface between the particle surface and the surrounding fluid is equal at the micro and macro scales. The finite element method is used to divide the solution area into a finite number of units. The approximate solution is constructed on each unit to approximate the true solution of the entire area, accurately simulating the flow and distribution of residual oil in heterogeneous reservoirs at the micro and macro scales.

[0023] In one embodiment of the present invention: in step 4, in the numerical simulation of remaining oil distribution, the relevant parameters in the remaining oil distribution simulation are encoded, and a multi-level encoding structure is adopted to incorporate the geological parameters, fluid parameters and development operation parameters of the remaining oil distribution into the encoding system, and a certain number of individuals are randomly generated to form an initial population, each individual represents a remaining oil distribution situation, and the remaining oil saturation distribution in the initial population is initialized with a bias by referring to the statistical law of the remaining oil distribution of the produced oil reservoirs at different development stages. The initialization function is: ; in, represents the remaining oil recovery factor, represents the development cost, Represents the development cycle, represents the environmental impact indicator, , , and represents the weight coefficient, , represents the regional difference factor, represents the policy factor, represents the technical feasibility factor, , , ; An indicator representing the fitness of an individual under multiple objectives, using a selection operator to select fitness from the current population The higher individual is taken as the parent, and the selected parent individuals are randomly paired, and each gene position of the parent individual is exchanged using uniform crossover. The offspring individuals obtained after the crossover operation are randomly selected for mutation operation on the gene position on the chromosome of the offspring individual to change its gene value. The mutation operation increases the diversity of the population and prevents the algorithm from converging to the local optimal solution too early. After each iteration, the optimal individual in the population that meets the termination condition is output as the result of the remaining oil distribution simulation.

[0024] In one embodiment of the present invention: in step five, a fiber optic sensor is buried near the reservoir, and the sampling frequency of pressure and temperature is set to 1 Hz-5 Hz. The fiber optic sensor converts the perceived changes in temperature and pressure into changes in optical signals. The fiber optic sensor is connected to a data acquisition device and a monitoring system via a fiber optic jumper. The monitoring system processes and analyzes the received data, fuses the data from all fiber optic sensors, extracts characteristic parameters related to reservoir pressure and temperature changes from the processed data, and monitors the pressure and temperature changes of the reservoir in real time.

[0025] In one embodiment of the present invention: In step 6, according to the parameters monitored in real time during the production process, different production data have different dimensions and numerical ranges, and different parameters are mapped to the (0,1) interval using a formula, and the preprocessed production data is used to estimate and train the parameters of the neural network model, and the real-time production data is assimilated and integrated into the model to achieve adaptive updating of the model, and update conditions are set according to the degree of change of the production data, the quality of the data, and the performance indicators of the model. When the newly collected production data deviates from the model prediction data, the update condition is set according to the degree of change of the production data, the quality of the data, and the performance indicators of the model. > 0.1, triggering a model update, using data not used for model training and updating to verify the updated model, and comparing the model's prediction results with the actual production data, including the degree of deviation and correlation coefficient , evaluate the performance improvement after the model update.

[0026] Embodiment 1: In the data assimilation process, the prediction error covariance matrix is ​​used to determine the weight of the model prediction when updating the reservoir state estimate. When the prediction error covariance matrix is ​​large, it means that the uncertainty of the model prediction is high. Relative to the observed data, the weight of the model prediction part in the update process will be reduced. On the contrary, if the prediction error covariance matrix is ​​small, the credibility of the model prediction is high, and its weight will increase accordingly. When updating the estimated value of the reservoir pressure, if the pressure prediction error covariance matrix is ​​small, it means that the model predicts the pressure more accurately. Then, when combined with the observed pressure data for updating, the pressure value predicted by the model will be given a larger weight. When calculating the error between the output and the real micro data, The closer the value is to 0, the higher the prediction accuracy of the model. By adjusting the weights and other parameters of the neural network, The value keeps decreasing.

[0027] Example 2: When evaluating the correlation between predicted data and real data, The closer the absolute value is to 1, the stronger the correlation between the predicted data and the real data, and the better the model performance. After evaluation, the model performance meets expectations and is applied to actual micro- and macro-data conversion. In new undeveloped areas or areas with less data, existing logging and seismic data are used to predict key parameters such as micro-porosity and permeability through trained models, providing a basis for reservoir evaluation and oil and gas development program design. At the same time, the model can also be interpreted to analyze which features of the macro data play a key role in the prediction of micro-data. By calculating feature importance or conducting feature selection experiments, it is found that resistivity logging data contributes more to the prediction of micro-porosity, which can help geologists and engineers better understand the intrinsic connection between data and further optimize data collection and analysis strategies.

[0028] Embodiment 3, the genetic algorithm optimizes the parameters of the remaining oil distribution simulation by continuously iterating the selection, crossover and mutation operations. In each generation of the population, the reservoir parameter combination corresponding to each individual is numerically simulated, and its fitness value is calculated. Genetic operations are performed according to the fitness value to generate a new generation of population. The maximum number of iterations is set to 100 generations. As the number of iterations increases, the individuals in the population gradually evolve toward a better reservoir parameter combination, that is, the simulated remaining oil distribution matches the actual production data better and better. The optimized remaining oil distribution simulation results are compared with the actual remaining oil distribution. On a typical section of the reservoir, the simulated remaining oil saturation contour is compared with the actual remaining oil saturation contour obtained by the reservoir monitoring technology. If the two are consistent in shape, position and value, it means that the optimized simulation can accurately reflect the distribution of the remaining oil.

[0029] As attached Figure 1 As shown, the model is used to convert the collected micro-scale and macro-scale data, integrate the collected dynamic data with the geological static data, and simulate and predict the start-up pressure and remaining oil distribution.

[0030] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited to the described embodiments. For those skilled in the art, various changes, modifications, substitutions and variations of these embodiments are made without departing from the principles and spirit of the present invention, and still fall within the scope of protection of the present invention.

[0031] In the description of the specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0032] The above contents are merely examples and explanations of the present invention. Various modifications or additions to the specific embodiments described or replacements in similar ways by technicians in the technical field shall fall within the protection scope of the present invention as long as they do not deviate from the invention or exceed the scope defined by the claims.

[0033] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A novel method for comprehensive analysis of startup pressure and remaining oil distribution in heterogeneous reservoirs, comprising the use of multi-scale data fusion, intelligent algorithm optimization, and dynamic monitoring and feedback mechanism to comprehensively analyze the startup pressure and remaining oil distribution law, characterized in that: The specific steps are as follows: Step 1: Collect micro-scale core analysis data, meso-scale well logging data, and macro-scale seismic data; Step 2: Nest the unit models of micro, meso and macro scales to form a reservoir model, and integrate the collected production dynamic data with the geological static data; Step 3: Map the microscopic, mesoscopic and macroscopic scale data using the data conversion model, train the model using the mapped data, and train the model to predict the starting pressure; Step 4: Use multi-scale simulation equations to simulate the flow and distribution of heterogeneous residual oil at the micro and macro levels; Step 5: All optical fiber sensor data are fused and processed, characteristic parameters are extracted from the processed data, and the pressure and temperature changes of the heterogeneous reservoir are monitored in real time; Step 6: Assimilate the real-time collected production data and integrate it into the model, and provide real-time feedback and adaptive updates to the analysis model.

2. A novel comprehensive analysis method for heterogeneous reservoir starting pressure and remaining oil distribution according to claim 1, characterized in that: In the step 1, core samples are obtained by drilling, and micro-pore structure analysis is performed on the core samples using micro-CT technology to obtain the lithology, pore structure, permeability and porosity physical properties of the core. According to the pore size, the flow units are divided into three types: micropores with a radius of less than 0.1um, small pores with a radius between 0.1um and 1um, and large pores with a radius of more than 1um. Density logging is used to measure the scattering intensity after the interaction between gamma rays and formation materials to determine the mesoscopic formation distribution. Flow units based on sedimentary microphases are divided within the formation. The division of mesoscopic flow units reflects the heterogeneity between different sedimentary microphases in the same formation. Seismic detectors are arranged on the ground to excite and receive artificial seismic waves, and the refraction and reflection signals of seismic waves in the formation are recorded and analyzed. The seismic data show the macroscopic structure of the underground formation, including the reservoir structure, the change in formation thickness, and the connectivity between different strata. The macroscopic flow units of the entire reservoir are divided. The division of macroscopic flow units grasps the fluid flow trend of the reservoir and the macroscopic pattern of the remaining oil distribution as a whole.

3. A novel comprehensive analysis method for heterogeneous reservoir starting pressure and remaining oil distribution according to claim 2, characterized in that: In the step 2, the flow unit models of micro, meso and macro scales are integrated into a multi-scale flow unit nested reservoir model. In the model, the micro flow unit is nested in the meso flow unit, and the meso flow unit is nested in the macro flow unit. The scales influence and restrict each other through the established association relationship. The production dynamic data of the oil well, including the change of oil well production, bottom hole pressure and water content over time, are collected. By randomly perturbing the model parameters, a group of set members are generated. Each set member is time-advanced according to the reservoir model. During the time advancement process, the reservoir model is used to predict the production dynamic data corresponding to each set member, and the difference between the predicted production dynamic data of each set member and the actual observed data is calculated. The prediction error covariance matrix and the observation error covariance matrix are combined to determine the weight of each set member. The calculation formula is: ； in, represents the elements of the forecast error covariance matrix, Representative The first in the ensemble member's prediction state vector variables, represents the first variables, Representative The first in the state vector of the ensemble member prediction variables, represents the first variables, Represents the number of all members in the set. The diagonal elements of the prediction error covariance matrix represent the variance of the prediction errors of each state parameter, and the off-diagonal elements show the correlation between the prediction errors of different parameters. The prediction error covariance matrix is ​​used to update the weights when estimating the reservoir state, and the set members are weighted averaged to make the reservoir model more consistent with the actual production situation and enable the continuous integration of production dynamic data and geological static data.

4. A novel comprehensive analysis method for heterogeneous reservoir starting pressure and remaining oil distribution according to claim 2, characterized in that: In the step 3, a microscopic, mesoscopic and macroscopic data conversion model based on a neural network is established. Since the dimensions and numerical ranges of microscopic, mesoscopic and macroscopic data are different, the formula is used: ； in, represents the normalized value, X represents the original data, Represents the minimum value of the data, Represents the maximum value of the data. The above formula can be used to map different microscopic, mesoscopic and macroscopic data to the (0,1) interval. The processed microscopic core data, mesoscopic well data and macroscopic seismic data are divided into training set, validation set and test set. The training set is used to train the neural network model, accounting for 60%-80% of the total data set. The validation set is used to adjust the hyperparameters of the model, including the number of hidden layers, the number of neurons and the learning rate, accounting for 10%-20% of the total data set. The test set is used to evaluate the final performance of the model, accounting for 10%-20% of the total set. During the training process, the training set data is input into the model, and iterative training is performed according to the steps of forward propagation, loss calculation, back propagation and weight update.

5. A novel method for comprehensive analysis of heterogeneous reservoir starting pressure and remaining oil distribution according to claim 4, characterized in that: In step 3, each iteration becomes a training round epoch, and the error between the output and the real micro data is calculated using the formula: ； in, represents the sample size, Represents real micro data, represents the micro data predicted by the neural network, Represents the average deviation between the predicted value and the true value of the model. After each epoch, the loss value on the validation set is calculated, and the trend of the validation loss is observed to monitor the fitting effect of the model. The correlation coefficient is used The performance of the model on the test set was evaluated. After evaluation, the model was applied to actual microscopic, mesoscopic and macroscopic data. Based on geological exploration data and production dynamic data of nearby oil wells, the model was used to predict the start-up pressure at different well locations and different development stages.

6. A novel method for comprehensive analysis of heterogeneous reservoir starting pressure and remaining oil distribution according to claim 1, characterized in that: In step 4, the collected reservoir geological data, including core analysis data, well logging data and seismic data, are used to describe the spatial distribution pattern of geological variables using a multi-point geostatistical method, and the remaining oil distribution in the reservoir is predicted using a multi-scale simulation equation, which is: ； in, represents the temperature of the particle, represents the temperature of the fluid, represents the radius of the particle, represents the heat transfer coefficient between particles and fluid, represents the heat release rate of the chemical reaction on the interface per unit area, represents the surface of the fluid, represents the unit external normal vector of the surface at the calculation point, represents thermal conductivity, represents the temperature gradient, represents the velocity of the fluid, represents the density of the fluid, Represents the specific heat capacity of the fluid. The above equation shows that the mass flux of particle dissolution and adsorption on the interface between the particle surface and the surrounding fluid is equal at the micro and macro scales. The finite element method is used to divide the solution area into a finite number of units. The approximate solution is constructed on each unit to approximate the true solution of the entire area, accurately simulating the flow and distribution of residual oil in heterogeneous reservoirs at the micro and macro scales.

7. A novel method for comprehensive analysis of heterogeneous reservoir starting pressure and remaining oil distribution according to claim 1, characterized in that: In the step 4, in the numerical simulation of the remaining oil distribution, the relevant parameters in the remaining oil distribution simulation are encoded, and a multi-level encoding structure is adopted to incorporate the geological parameters, fluid parameters and development operation parameters of the remaining oil distribution into the encoding system, and a certain number of individuals are randomly generated to form an initial population, each individual represents a remaining oil distribution situation, and the remaining oil saturation distribution in the initial population is initialized with a bias by referring to the statistical law of the remaining oil distribution of the produced oil reservoirs at different development stages. The initialization function is: ； in, represents the remaining oil recovery factor, represents the development cost, Represents the development cycle, represents the environmental impact indicator, , , and represents the weight coefficient, , represents the regional difference factor, represents the policy factor, represents the technical feasibility factor, , , ; An indicator representing the fitness of an individual under multiple objectives, using a selection operator to select fitness from the current population The individuals with high probability are taken as parents, and the selected parent individuals are randomly paired. Uniform crossover is used to exchange each gene position of the parent individuals. After the crossover operation, the gene positions on the chromosomes of the offspring individuals are randomly selected for mutation operation to change their gene values. The mutation operation increases the diversity of the population and avoids the algorithm from converging to the local optimal solution too early. After each iteration, the optimal individual in the population that meets the termination condition is output as the result of the remaining oil distribution simulation.

8. The novel method for comprehensive analysis of heterogeneous reservoir starting pressure and remaining oil distribution according to claim 1 is characterized by: In the step five, the optical fiber sensor is buried near the reservoir, the sampling frequency of pressure and temperature is set to 1 Hz-5 Hz, the optical fiber sensor converts the perceived changes in temperature and pressure into changes in optical signals, the optical fiber sensor is connected to the data acquisition equipment and the monitoring system through an optical fiber jumper, the monitoring system processes and analyzes the received data, fuses the data from all the optical fiber sensors, extracts characteristic parameters related to the reservoir pressure and temperature changes from the processed data, and monitors the pressure and temperature changes of the reservoir in real time.

9. A novel method for comprehensive analysis of heterogeneous reservoir starting pressure and remaining oil distribution according to claim 4, characterized in that: In step six, according to the parameters monitored in real time during the production process, different production data have different dimensions and value ranges, and different parameters are mapped to the (0,1) interval using formulas. The preprocessed production data is used to estimate and train the parameters of the neural network model, and the real-time production data is assimilated and integrated into the model to achieve adaptive updating of the model. The updating conditions are set according to the degree of change of the production data, the quality of the data, and the performance indicators of the model. When the newly collected production data deviates from the model prediction data, the update conditions are set according to the degree of change of the production data, the quality of the data, and the performance indicators of the model. > 0.1, triggering a model update, using data not used for model training and updating to verify the updated model, and comparing the model's prediction results with actual production data, including the degree of deviation and correlation coefficient , evaluate the performance improvement after the model update.

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