Oil reservoir isolation valve self-adaptive control method and device in combination with oil reservoir distribution scene

Abstract

The invention discloses an oil reservoir isolation valve self-adaptive control method and device in combination with an oil reservoir distribution scene, and relates to the technical field of isolation valves. The method comprises the following steps: monitoring and acquiring an oil reservoir scene environment data flow through a sensor network; constructing a reservoir distribution three-dimensional model; performing oil reservoir parameter analysis and flow simulation to obtain oil reservoir spatial distribution characteristics and oil reservoir flow trends, performing multi-dimensional risk assessment, and determining an oil reservoir risk region parameter set; an oil reservoir isolation valve control strategy is obtained; and performing adaptive control analysis on the oil reservoir risk area parameter set based on the oil reservoir isolation valve control strategy to obtain isolation valve coordination control parameters, and performing adaptive feedback control on the target oil reservoir isolation valve through the isolation valve coordination control parameters. The technical problem that in the prior art, due to the fact that an oil reservoir isolation valve cannot achieve accurate control for a complex oil reservoir distribution scene, the oil reservoir exploitation efficiency is low is solved, and the technical effect of improving the exploitation efficiency is achieved by adjusting the isolation valve control strategy in a self-adaptive mode.

Description

Technical Field

[0001] The present invention relates to the technical field of isolation valves, and in particular to a method and device for adaptively controlling an oil reservoir isolation valve in combination with an oil reservoir distribution scenario. Background Art

[0002] With the continuous development of oil and gas field mining technology, the scenarios faced by reservoir mining are becoming increasingly complex, especially in environments where multiple factors such as multi-layer reservoir structures, different geological conditions, pressure, temperature, etc. are intertwined. The flow trend of reservoir fluids is difficult to predict, and the risks that may be encountered during the mining process are also increasing. In traditional reservoir isolation valve control methods, it is often impossible to accurately control according to the actual distribution of the reservoir, resulting in the isolation valve being difficult to adapt to the complex reservoir environment. There are problems such as inaccurate isolation and fluid loss of control, which seriously affect the mining efficiency and safety of oil and gas fields. The current market demand for technologies that can dynamically adjust and accurately control isolation valves is increasingly urgent to cope with the changing reservoir distribution scenarios and ensure the safety and efficiency of the reservoir mining process. Summary of the Invention

[0003] The present application provides an adaptive control method and device for a reservoir isolation valve in combination with a reservoir distribution scenario, which solves the technical problem in the prior art that the reservoir isolation valve cannot achieve precise control for complex reservoir distribution scenarios, resulting in low reservoir extraction efficiency.

[0004] In view of the above problems, the present application provides a method and device for adaptive control of reservoir isolation valves in combination with reservoir distribution scenarios.

[0005] A first aspect of the present application provides a method for adaptively controlling a reservoir isolation valve in combination with a reservoir distribution scenario, the method comprising: A sensor network is deployed in the target reservoir distribution scenario area, wherein the sensor network integration includes pressure sensors, temperature sensors, and flow sensors, and the sensor network is used to monitor and obtain the reservoir scenario environment data stream; the drilling exploration signal stream is collected and obtained, and reservoir simulation modeling is performed based on the reservoir scenario environment data stream and the drilling exploration signal stream to construct a three-dimensional reservoir distribution model; reservoir parameter analysis and flow simulation are performed based on the three-dimensional reservoir distribution model to obtain reservoir spatial distribution characteristics and reservoir flow trends, and a multi-dimensional risk assessment is performed on the reservoir spatial distribution characteristics and reservoir flow trends to determine a reservoir risk area parameter set; a reservoir isolation valve control strategy library is constructed, and the structural characteristic information of the target reservoir isolation valve and the reservoir production target are matched with the reservoir isolation valve control strategy library to obtain a reservoir isolation valve control strategy; an adaptive control analysis of the reservoir risk area parameter set is performed based on the reservoir isolation valve control strategy to obtain isolation valve coordination control parameters, and adaptive feedback control of the target reservoir isolation valve is performed through the isolation valve coordination control parameters.

[0006] A second aspect of the present application provides an adaptive control device for a reservoir isolation valve in combination with a reservoir distribution scenario, the device comprising: A data acquisition module is used to deploy a sensor network in the target reservoir distribution scenario area, wherein the sensor network integration includes pressure sensors, temperature sensors, and flow sensors, and the reservoir scenario environment data stream is monitored and acquired through the sensor network; a simulation modeling module is used to acquire a drilling exploration signal stream, perform reservoir simulation modeling based on the reservoir scenario environment data stream and the drilling exploration signal stream, and construct a three-dimensional reservoir distribution model; a simulation analysis module is used to perform reservoir parameter analysis and flow simulation based on the three-dimensional reservoir distribution model to obtain the spatial distribution characteristics and reservoir Flow trend, perform multi-dimensional risk assessment on the spatial distribution characteristics of the reservoir and the reservoir flow trend, and determine the reservoir risk area parameter set; a matching module, the matching module is used to construct a reservoir isolation valve control strategy library, match the reservoir isolation valve control strategy library according to the structural characteristic information of the target reservoir isolation valve and the reservoir production target, and obtain the reservoir isolation valve control strategy; a control module, the control module is used to perform adaptive control analysis on the reservoir risk area parameter set based on the reservoir isolation valve control strategy, obtain isolation valve coordination control parameters, and perform adaptive feedback control on the target reservoir isolation valve through the isolation valve coordination control parameters.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: First, a sensor network is deployed in the target reservoir distribution scenario. The sensor network integrates pressure sensors, temperature sensors, and flow sensors. The sensor network monitors and acquires reservoir environment data streams. Next, drilling exploration signal streams are collected and simulated based on the reservoir environment data streams and drilling exploration signal streams, constructing a three-dimensional reservoir distribution model. Reservoir parameter analysis and flow simulation are then performed based on the three-dimensional reservoir distribution model to determine the reservoir spatial distribution characteristics and reservoir flow trends. A multidimensional risk assessment is then performed on these characteristics and flow trends to determine the reservoir risk zone parameter set. Next, a reservoir isolation valve control strategy library is constructed. Based on the structural characteristics of the target reservoir isolation valve and the reservoir production target, the control strategy is matched to the library to obtain the reservoir isolation valve control strategy. Finally, adaptive control analysis is performed on the reservoir risk zone parameter set based on the reservoir isolation valve control strategy to obtain the isolation valve coordinated control parameters. Adaptive feedback control of the target reservoir isolation valve is then performed using the isolation valve coordinated control parameters. The method solves the technical problem in the existing technology that the reservoir isolation valve cannot achieve precise control for complex reservoir distribution scenarios, resulting in low reservoir extraction efficiency. By adaptively adjusting the isolation valve control strategy, the technical effect of improving extraction efficiency is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0009] Figure 1 A schematic flow chart of a method for adaptively controlling a reservoir isolation valve in accordance with an embodiment of the present application in combination with a reservoir distribution scenario; Figure 2 Schematic diagram of the structure of the adaptive control device for reservoir isolation valves combined with the reservoir distribution scenario provided in an embodiment of the present application.

[0010] Description of the accompanying drawings: data acquisition module 11, simulation modeling module 12, simulation analysis module 13, matching module 14, control module 15. DETAILED DESCRIPTION

[0011] This application solves the technical problem in the prior art that reservoir isolation valves cannot achieve precise control for complex reservoir distribution scenarios, resulting in low reservoir extraction efficiency, by providing an adaptive control method and device for reservoir isolation valves combined with reservoir distribution scenarios.

[0012] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0013] It should be noted that the terms "including" and "having" are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products or devices.

[0014] Example 1, as Figure 1 As shown, the present application provides an adaptive control method for reservoir isolation valves in combination with reservoir distribution scenarios, wherein the method includes: A sensor network is deployed in the target reservoir distribution scenario area, wherein the sensor network integration includes pressure sensors, temperature sensors, and flow sensors, and the reservoir scenario environment data stream is monitored and acquired through the sensor network.

[0015] A sensor network is deployed within the target reservoir distribution scenario to comprehensively cover key reservoir areas. The sensor network integrates multiple sensing devices, including pressure sensors, temperature sensors, and flow sensors, to monitor the reservoir's environmental conditions in real time. Pressure sensors monitor pressure changes within the reservoir, temperature sensors detect temperature changes in various reservoir regions, and flow sensors monitor the flow rate of oil and gas fluids within the reservoir. The sensor network monitors the reservoir's environmental conditions in real time to obtain a reservoir scenario environmental data stream, which includes parameters such as pressure, temperature, and flow within the reservoir area.

[0016] The drilling exploration signal stream is acquired, and reservoir simulation modeling is performed based on the reservoir scene environment data stream and the drilling exploration signal stream to construct a three-dimensional reservoir distribution model.

[0017] While acquiring the reservoir scenario data stream, the drilling exploration signal stream is also collected. The drilling exploration signal typically includes geological information, rock properties, and fluid composition data generated during the drilling process. By combining the reservoir scenario data stream with the drilling exploration signal stream and utilizing simulation modeling technology, an accurate 3D reservoir distribution model can be constructed. This 3D reservoir distribution model reflects the reservoir's geographic characteristics and fluid flow conditions.

[0018] Furthermore, the construction of the three-dimensional reservoir distribution model includes: The drilling exploration signal stream is decomposed and denoised using the ensemble empirical mode decomposition technique to obtain a usable drilling exploration signal stream; the reservoir scenario environment data stream is subjected to noise characteristic analysis to obtain data noise characteristic information, and a data filter is designed based on the data noise characteristic information; the reservoir scenario environment data stream is subjected to filtering preprocessing based on the data filter to obtain a usable reservoir scenario environment data stream; correlation parameter extraction and modeling are performed on the usable drilling exploration signal stream and the usable reservoir scenario environment data stream to obtain a reservoir structural model, a reservoir sedimentary model and a reservoir dynamic model; the reservoir structural model, the reservoir sedimentary model and the reservoir dynamic model are fused and tuned for modeling to construct the three-dimensional reservoir distribution model.

[0019] Specifically, the ensemble empirical mode decomposition technology is used to decompose and reduce the noise of the acquired drilling exploration signal stream. The ensemble empirical mode decomposition technology can effectively extract the effective components in the signal and remove the background noise, thereby obtaining a usable drilling exploration signal stream; the noise characteristics of the reservoir scene environmental data stream are analyzed to identify and quantify the noise characteristics in the data, such as random noise, periodic noise, etc.; based on the obtained data noise characteristic information, a data filter suitable for the noise characteristics is designed. The data filter is used to eliminate or reduce the noise in the environmental data stream to ensure the accuracy and availability of the data; the designed data filter is used to perform filtering preprocessing on the reservoir scene environmental data stream to obtain to the cleaned available reservoir scenario environment data stream; key parameters related to reservoir structure, reservoir deposition and reservoir dynamics, including formation pressure, temperature, permeability, porosity, fluid saturation, etc., are extracted from the available drilling exploration signal stream and the available reservoir scenario environment data stream. Based on the extracted correlation parameters, a reservoir structure model, a reservoir deposition model and a reservoir dynamic model are established respectively. These models reflect the geological structure, sedimentary characteristics and fluid dynamic behavior of the reservoir respectively; the constructed reservoir structure model, reservoir deposition model and reservoir dynamic model are integrated and tuned for modeling. Through the synergistic effect of multiple models and comprehensive consideration of the influence of different factors, a complete three-dimensional reservoir distribution model is finally constructed.

[0020] Furthermore, obtaining the available drilling exploration signal stream includes: The drilling exploration signal stream is decomposed and averaged using an ensemble empirical mode decomposition technique to obtain an IMF signal component; effective signal selection and filter design are performed based on the frequency characteristics and energy distribution of the IMF signal component to determine a signal low-pass filter; noise reduction and square wave shaping are performed on the drilling exploration signal stream using a signal low-pass filter to obtain a denoised drilling exploration signal stream; noise reduction effect evaluation and signal noise reduction optimization are performed on the denoised drilling exploration signal stream to obtain the usable drilling exploration signal stream.

[0021] Specifically, the ensemble empirical mode decomposition technology adds tiny white noise to the drilling exploration signal stream multiple times, performs empirical mode decomposition on the signal after each noise addition, and then performs ensemble average of all decompositions of intrinsic mode functions (IMFs) to obtain IMF signal components; analyzes the frequency characteristics and energy distribution of the IMF signal components, and identifies which IMF components contain effective signals related to drilling exploration (such as formation response, fluid characteristics, etc.), as well as the frequency range and energy intensity of these signals, which can provide a basis for subsequent filter design; based on the analysis results of the IMF signal components, designs a suitable A low-pass filter is used to filter out high-frequency noise and retain the main signal components. The drilling exploration signal stream is subjected to noise reduction processing using the designed low-pass filter. After noise removal, square wave shaping processing is also required to enhance signal clarity and readability. The noise reduction effect of the noise-reduced drilling exploration signal stream is evaluated, and relevant indicators (such as signal-to-noise ratio and mean square error) are used to quantify the noise reduction effect. Based on the results of the noise reduction effect evaluation, the noise reduction process is optimized, including adjusting filter parameters, improving the noise reduction algorithm, and adding additional signal processing techniques to further improve signal quality and ultimately obtain a usable drilling exploration signal stream.

[0022] Reservoir parameter analysis and flow simulation are performed based on the three-dimensional reservoir distribution model to obtain reservoir spatial distribution characteristics and reservoir flow trends, and multi-dimensional risk assessment is performed on the reservoir spatial distribution characteristics and reservoir flow trends to determine a reservoir risk area parameter set.

[0023] Utilize various data from the three-dimensional reservoir distribution model to analyze reservoir parameters, evaluate the physical and chemical properties of the reservoir through numerical simulation, and obtain key parameters such as permeability, porosity, and saturation; use flow simulation software to model the flow trend of the fluid inside the reservoir, including calculating the flow path, flow velocity, and pressure changes of the fluid under different conditions, so as to fully understand the dynamic behavior within the reservoir; extract the spatial distribution characteristics of the reservoir by analyzing the simulation results, and identify the fluid distribution and reserve characteristics in different areas of the reservoir; combine the results of flow simulation to evaluate the flow trend of the reservoir fluid and obtain the reservoir flow trend; conduct multi-dimensional risk assessment based on the extracted spatial distribution characteristics and flow trends of the reservoir, involving the analysis of potential risk factors (such as fluid loss of control, formation collapse, etc.) and evaluating their impact on mining safety and efficiency; determine the parameter set of the reservoir risk area through risk assessment.

[0024] Furthermore, the determination of the reservoir risk area parameter set includes: The three-dimensional reservoir distribution model is divided into reservoir areas according to the spatial distribution characteristics of the reservoir and the reservoir flow trend to obtain a set of reservoir distribution blocks; reservoir risk factor information is obtained, and the reservoir risk factor information includes reservoir geological risk, reservoir engineering risk and reservoir environmental safety risk; based on the reservoir risk factor information, a multi-dimensional assessment is performed on the spatial distribution characteristics of the reservoir and the reservoir flow trend to obtain a set of reservoir area risk parameters; based on the reservoir area risk parameter set, the reservoir distribution block set is risk-classified and identified to determine the reservoir risk area parameter set.

[0025] Specifically, the three-dimensional reservoir distribution model is regionally segmented based on the spatial distribution characteristics and reservoir flow trends. By dividing the reservoir into multiple blocks, a collection of reservoir distribution blocks is obtained. Each block has relatively consistent geological characteristics and fluid flow trends, which facilitates more accurate analysis and management of the characteristics and risks of each region. Reservoir risk factor information is collected. Reservoir risk factor information includes reservoir geological risks (including reservoir heterogeneity, fault and fracture development, rock mechanical properties, etc.), reservoir engineering risks (risks involved in engineering operations such as drilling, completion, water injection, and oil production), and reservoir environmental safety risks (the impact of reservoir development on the surrounding environment, such as groundwater pollution, soil erosion, and ecological damage). ; Combine the collected reservoir risk factor information with the spatial distribution characteristics and flow trends of the reservoir to conduct a multi-dimensional risk assessment. Quantitative and qualitative analysis methods can be used to identify the potential impact of different risk factors on reservoir exploitation, determine the risk level of each area, and then obtain a set of reservoir regional risk parameters. The reservoir regional risk parameter set may include risk probability, risk impact degree, risk level, etc., which are used to quantitatively represent the risk status of different areas of the reservoir; Based on the reservoir regional risk parameter set, the reservoir distribution block set is risk-classified and identified, and each block is classified according to its risk level, clearly identifying high-risk areas, low-risk areas, etc.; Combined with the risk classification results, a reservoir risk area parameter set is formed.

[0026] A reservoir isolation valve control strategy library is constructed, and the structural characteristic information of the target reservoir isolation valve and the reservoir production target are matched with the reservoir isolation valve control strategy library to obtain the reservoir isolation valve control strategy.

[0027] The structural characteristics of the target reservoir isolation valve are collected, including its material, dimensions, operating mechanism, and response time. Reservoir production objectives are determined, including production volume, production rate, recovery factor, and safety requirements. Based on the reservoir production objectives and the structural characteristics of the reservoir isolation valve, the control tasks required by the isolation valve during the production process, such as flow regulation, pressure control, and emergency shutdown, are analyzed. For each control task, a corresponding control strategy is designed. The designed and validated control strategies are integrated into a unified strategy library, resulting in a reservoir isolation valve control strategy library. This library contains multiple control strategies covering different operating conditions and expected outcomes to address various changes during reservoir production. During the production process, real-time monitoring data (such as pressure, temperature, and flow) is analyzed in conjunction with reservoir characteristics to select the control strategy that best matches the structural characteristics of the target reservoir isolation valve and the production objectives. Through matching, a reservoir isolation valve control strategy suitable for specific reservoir conditions is ultimately obtained.

[0028] Furthermore, obtaining the reservoir isolation valve control strategy includes: Based on the structural characteristic information of the target reservoir isolation valve and the reservoir production target, similarity calculation is performed with the reservoir isolation valve control strategy library to obtain a control strategy similarity set; the reservoir isolation valve control strategy library is arranged in descending order according to the control strategy similarity set to obtain an isolation valve control strategy sequence set; strategy matching optimization is performed based on the isolation valve control strategy sequence set to obtain a matching isolation valve control strategy; deviation analysis and strategy optimization are performed on the matching isolation valve control strategy based on the control strategy similarity set to obtain the reservoir isolation valve control strategy.

[0029] Specifically, based on the structural characteristic information and production objectives of the target reservoir isolation valve, similarity calculation is performed with each strategy in the control strategy library, and algorithms (such as cosine similarity, Euclidean distance, etc.) are used to quantify the similarity between each strategy and the target characteristics to form a control strategy similarity set; based on the calculated control strategy similarity set, the reservoir isolation valve control strategy library is sorted in descending order to obtain an isolation valve control strategy sequence set; in the isolation valve control strategy sequence set, strategy matching optimization is performed to select the control strategy with the greatest similarity as the matching isolation valve control strategy; for the selected matching isolation valve control strategy, deviation analysis is performed based on the control strategy similarity set to identify possible minor deviations between the selected strategy and the target requirements; based on the results of the deviation analysis, necessary adjustments are made to the matching isolation valve control strategy, including fine-tuning of control parameters, adding additional monitoring or protection mechanisms, or optimizing the strategy execution process; after deviation analysis and strategy optimization, the final reservoir isolation valve control strategy is obtained.

[0030] Based on the reservoir isolation valve control strategy, adaptive control analysis is performed on the reservoir risk area parameter set to obtain isolation valve coordinated control parameters, and adaptive feedback control is performed on the target reservoir isolation valve using the isolation valve coordinated control parameters.

[0031] Using the reservoir isolation valve control strategy, adaptive control analysis is performed on the reservoir risk zone parameter set to derive the isolation valve coordinated control parameters. Subsequently, adaptive feedback control is performed on the isolation valves in the target reservoir using these coordinated control parameters to ensure reservoir recovery efficiency and safety.

[0032] Furthermore, obtaining the isolation valve coordinated control parameters includes: According to the reservoir isolation valve control strategy, an adaptive learning network architecture is selected; the reservoir isolation valve control parameter set is collected and acquired, and the strategy control factors of the reservoir isolation valve control parameter set are classified according to the reservoir isolation valve control strategy to obtain the isolation valve control factor parameter set; the isolation valve control factor parameter set is branch-trained and optimized using the adaptive learning network architecture to determine the isolation valve control adaptive network; based on the isolation valve control adaptive network, the reservoir risk area parameter set is adaptively controlled and analyzed to output the isolation valve coordination control parameters.

[0033] Specifically, based on the characteristics of the reservoir isolation valve control strategy, an appropriate adaptive learning network architecture (such as a deep neural network or recursive neural network) is selected. This architecture should be capable of processing complex data relationships and real-time learning to support dynamic adjustment of control parameters. The control parameter set of the reservoir isolation valve, including various operating states, environmental conditions, and system responses, is collected. According to the reservoir isolation valve control strategy, the reservoir isolation valve control parameter set is classified, and the main control factors affecting isolation valve performance are identified, thereby obtaining the isolation valve control factor parameter set, such as switch state, opening control, rotation speed, and torque. For each isolation valve control factor parameter set, the selected adaptive learning network architecture is used for branch training and optimization. That is, a network branch is established for each control factor, and each branch focuses on learning and optimizing the control strategy related to that factor. Through training, the network can identify the relationship between each parameter and the control effect, thereby enhancing the model's predictive ability and adaptability. Based on the trained isolation valve control adaptive network, adaptive control analysis is performed on the reservoir risk area parameter set to obtain the isolation valve coordinated control parameters.

[0034] Furthermore, determining the isolation valve control adaptive network includes: The isolation valve control factor parameter set is branch-trained and integrated using the adaptive learning network architecture to obtain an initial isolation valve control fusion network; the initial isolation valve control fusion network is verified and evaluated to obtain network performance parameters, and a network optimizer is selected based on the network performance parameters; a network loss function is constructed based on the adaptive network fusion optimization target; the network loss function is minimized based on the network optimizer, and the parameters of the initial isolation valve control fusion network are iteratively optimized based on the solution result to determine the isolation valve control adaptive network.

[0035] Specifically, an adaptive learning network architecture is used to perform independent branch training on each isolation valve control factor parameter set. Each branch network focuses on learning and optimizing the feature representation and control strategy related to its corresponding control factor. After the branch training is completed, an appropriate fusion strategy (such as weighted averaging) is used to fuse the outputs of each branch network to obtain an initial isolation valve control fusion network. The initial isolation valve control fusion network is verified and evaluated to evaluate the network's performance in prediction and control, and to obtain relevant network performance parameters (such as accuracy, loss value, etc.). Based on the obtained network performance parameters, a comprehensive analysis of the network performance is conducted. Identify the aspects in which the network performs well and the aspects in which it has deficiencies; select a suitable network optimizer (such as stochastic gradient descent, momentum optimizer, etc.) based on the results of network performance analysis and the characteristics of the network optimizer; construct a corresponding network loss function based on the adaptive network fusion optimization goal. The loss function is used to quantify the gap between the network prediction results and the actual control effect to guide the network optimization process; use the selected network optimizer to minimize the constructed network loss function, and continuously adjust the network parameters through the backpropagation algorithm to reduce the value of the loss function, thereby improving the prediction ability of the model; based on the solution results, iteratively optimize the parameters of the initial isolation valve control fusion network, continuously improve the network performance, and finally determine the isolation valve control adaptive network.

[0036] In summary, the embodiments of the present application have at least the following technical effects: First, a sensor network is deployed in the target reservoir distribution scenario. The sensor network integrates pressure sensors, temperature sensors, and flow sensors. The sensor network monitors and acquires reservoir environment data streams. Next, drilling exploration signal streams are collected and simulated based on the reservoir environment data streams and drilling exploration signal streams, constructing a three-dimensional reservoir distribution model. Reservoir parameter analysis and flow simulation are then performed based on the three-dimensional reservoir distribution model to determine the reservoir spatial distribution characteristics and reservoir flow trends. A multidimensional risk assessment is then performed on these characteristics and flow trends to determine the reservoir risk zone parameter set. Next, a reservoir isolation valve control strategy library is constructed. Based on the structural characteristics of the target reservoir isolation valve and the reservoir production target, the control strategy is matched to the library to obtain the reservoir isolation valve control strategy. Finally, adaptive control analysis is performed on the reservoir risk zone parameter set based on the reservoir isolation valve control strategy to obtain the isolation valve coordinated control parameters. Adaptive feedback control of the target reservoir isolation valve is then performed using the isolation valve coordinated control parameters. The method solves the technical problem in the existing technology that the reservoir isolation valve cannot achieve precise control for complex reservoir distribution scenarios, resulting in low reservoir extraction efficiency. By adaptively adjusting the isolation valve control strategy, the technical effect of improving extraction efficiency is achieved.

[0037] The second embodiment is based on the same inventive concept as the adaptive control method of the reservoir isolation valve in combination with the reservoir distribution scenario in the above embodiment. Figure 2 As shown, the present application provides an adaptive control device for a reservoir isolation valve in combination with a reservoir distribution scenario, wherein the device includes: A data acquisition module 11 is used to deploy a sensor network in the target reservoir distribution scenario area, wherein the sensor network integration includes pressure sensors, temperature sensors, and flow sensors, and the reservoir scenario environment data stream is monitored and acquired through the sensor network; a simulation modeling module 12 is used to acquire a drilling exploration signal stream, perform reservoir simulation modeling based on the reservoir scenario environment data stream and the drilling exploration signal stream, and construct a three-dimensional reservoir distribution model; a simulation analysis module 13 is used to perform reservoir parameter analysis and flow simulation based on the three-dimensional reservoir distribution model, and obtain the reservoir spatial distribution characteristics and Reservoir flow trend, multi-dimensional risk assessment of the reservoir spatial distribution characteristics and reservoir flow trend, and determination of a reservoir risk area parameter set; a matching module 14, the matching module 14 is used to construct a reservoir isolation valve control strategy library, and match the reservoir isolation valve control strategy library according to the structural characteristic information of the target reservoir isolation valve and the reservoir production target to obtain the reservoir isolation valve control strategy; a control module 15, the control module 15 is used to perform adaptive control analysis on the reservoir risk area parameter set based on the reservoir isolation valve control strategy, obtain isolation valve coordination control parameters, and perform adaptive feedback control on the target reservoir isolation valve through the isolation valve coordination control parameters.

[0038] Furthermore, the simulation modeling module 12 is used to perform the following method: The drilling exploration signal stream is decomposed and denoised using the ensemble empirical mode decomposition technique to obtain a usable drilling exploration signal stream; the reservoir scenario environment data stream is subjected to noise characteristic analysis to obtain data noise characteristic information, and a data filter is designed based on the data noise characteristic information; the reservoir scenario environment data stream is subjected to filtering preprocessing based on the data filter to obtain a usable reservoir scenario environment data stream; correlation parameter extraction and modeling are performed on the usable drilling exploration signal stream and the usable reservoir scenario environment data stream to obtain a reservoir structural model, a reservoir sedimentary model and a reservoir dynamic model; the reservoir structural model, the reservoir sedimentary model and the reservoir dynamic model are fused and tuned for modeling to construct the three-dimensional reservoir distribution model.

[0039] Furthermore, the simulation modeling module 12 is used to perform the following method: The drilling exploration signal stream is decomposed and averaged using an ensemble empirical mode decomposition technique to obtain an IMF signal component; effective signal selection and filter design are performed based on the frequency characteristics and energy distribution of the IMF signal component to determine a signal low-pass filter; noise reduction and square wave shaping are performed on the drilling exploration signal stream using a signal low-pass filter to obtain a denoised drilling exploration signal stream; noise reduction effect evaluation and signal noise reduction optimization are performed on the denoised drilling exploration signal stream to obtain the usable drilling exploration signal stream.

[0040] Furthermore, the simulation analysis module 13 is used to perform the following method: The three-dimensional reservoir distribution model is divided into reservoir areas according to the spatial distribution characteristics of the reservoir and the reservoir flow trend to obtain a set of reservoir distribution blocks; reservoir risk factor information is obtained, and the reservoir risk factor information includes reservoir geological risk, reservoir engineering risk and reservoir environmental safety risk; based on the reservoir risk factor information, a multi-dimensional assessment is performed on the spatial distribution characteristics of the reservoir and the reservoir flow trend to obtain a set of reservoir area risk parameters; based on the reservoir area risk parameter set, the reservoir distribution block set is risk-classified and identified to determine the reservoir risk area parameter set.

[0041] Furthermore, the matching module 14 is configured to execute the following method: Based on the structural characteristic information of the target reservoir isolation valve and the reservoir production target, similarity calculation is performed with the reservoir isolation valve control strategy library to obtain a control strategy similarity set; the reservoir isolation valve control strategy library is arranged in descending order according to the control strategy similarity set to obtain an isolation valve control strategy sequence set; strategy matching optimization is performed based on the isolation valve control strategy sequence set to obtain a matching isolation valve control strategy; deviation analysis and strategy optimization are performed on the matching isolation valve control strategy based on the control strategy similarity set to obtain the reservoir isolation valve control strategy.

[0042] Furthermore, the control module 15 is configured to execute the following method: According to the reservoir isolation valve control strategy, an adaptive learning network architecture is selected; the reservoir isolation valve control parameter set is collected and acquired, and the strategy control factors of the reservoir isolation valve control parameter set are classified according to the reservoir isolation valve control strategy to obtain the isolation valve control factor parameter set; the isolation valve control factor parameter set is branch-trained and optimized using the adaptive learning network architecture to determine the isolation valve control adaptive network; based on the isolation valve control adaptive network, the reservoir risk area parameter set is adaptively controlled and analyzed to output the isolation valve coordination control parameters.

[0043] Furthermore, the control module 15 is configured to execute the following method: The isolation valve control factor parameter set is branch-trained and integrated using the adaptive learning network architecture to obtain an initial isolation valve control fusion network; the initial isolation valve control fusion network is verified and evaluated to obtain network performance parameters, and a network optimizer is selected based on the network performance parameters; a network loss function is constructed based on the adaptive network fusion optimization target; the network loss function is minimized based on the network optimizer, and the parameters of the initial isolation valve control fusion network are iteratively optimized based on the solution result to determine the isolation valve control adaptive network.

[0044] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0045] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

[0046] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. An adaptive control method for reservoir isolation valves in combination with reservoir distribution scenarios, characterized in that: The method comprises: Deploy a sensor network in the target reservoir distribution scenario area, wherein the sensor network integration includes a pressure sensor, a temperature sensor, and a flow sensor, and monitors and obtains the reservoir scenario environment data flow through the sensor network; Acquire the drilling exploration signal stream, perform reservoir simulation modeling based on the reservoir scene environment data stream and the drilling exploration signal stream, and construct a three-dimensional reservoir distribution model; Performing reservoir parameter analysis and flow simulation based on the three-dimensional reservoir distribution model to obtain reservoir spatial distribution characteristics and reservoir flow trends, performing multi-dimensional risk assessment on the reservoir spatial distribution characteristics and reservoir flow trends, and determining a reservoir risk area parameter set; Constructing a reservoir isolation valve control strategy library, matching the target reservoir isolation valve's structural characteristic information and the reservoir production target with the reservoir isolation valve control strategy library to obtain the reservoir isolation valve control strategy; Based on the reservoir isolation valve control strategy, an adaptive control analysis is performed on the reservoir risk area parameter set to obtain isolation valve coordination control parameters, and adaptive feedback control is performed on the target reservoir isolation valve through the isolation valve coordination control parameters.

2. The method for adaptively controlling reservoir isolation valves in combination with reservoir distribution scenarios according to claim 1, characterized in that: The construction of the three-dimensional reservoir distribution model comprises: Using ensemble empirical mode decomposition technology to decompose and reduce noise on the drilling exploration signal stream to obtain a usable drilling exploration signal stream; Performing noise characteristic analysis on the reservoir scene environment data stream to obtain data noise characteristic information, and designing a data filter based on the data noise characteristic information; Performing filtering preprocessing on the reservoir scene environment data stream based on the data filter to obtain a usable reservoir scene environment data stream; Performing correlation parameter extraction modeling on the available drilling exploration signal stream and the available reservoir scene environment data stream to obtain a reservoir structural model, a reservoir deposition model and a reservoir dynamic model; The reservoir structure model, reservoir sedimentation model and reservoir dynamic model are integrated and optimized to build the three-dimensional reservoir distribution model.

3. The method for adaptively controlling reservoir isolation valves in combination with reservoir distribution scenarios according to claim 2, characterized in that: The method of obtaining the available drilling exploration signal stream comprises: Using ensemble empirical mode decomposition technology to decompose and ensemble average the drilling exploration signal stream to obtain IMF signal components; Perform effective signal selection and filter design based on the frequency characteristics and energy distribution of the IMF signal components, and determine a signal low-pass filter; Using a signal low-pass filter to perform noise reduction and square wave shaping on the drilling exploration signal stream to obtain a denoised drilling exploration signal stream; The denoising drilling exploration signal stream is subjected to denoising effect evaluation and signal denoising optimization to obtain the usable drilling exploration signal stream.

4. The method for adaptively controlling reservoir isolation valves in combination with reservoir distribution scenarios according to claim 1, characterized in that: The step of determining a set of reservoir risk area parameters includes: Divide the three-dimensional reservoir distribution model into reservoir areas according to the reservoir spatial distribution characteristics and reservoir flow trends to obtain a reservoir distribution block set; Acquiring reservoir risk factor information, wherein the reservoir risk factor information includes reservoir geological risk, reservoir engineering risk, and reservoir environmental safety risk; Based on the reservoir risk factor information, a multi-dimensional evaluation is performed on the reservoir spatial distribution characteristics and reservoir flow trend to obtain a reservoir regional risk parameter set; The reservoir distribution block set is risk classified and identified based on the reservoir area risk parameter set to determine the reservoir risk area parameter set.

5. The method for adaptively controlling reservoir isolation valves in combination with reservoir distribution scenarios according to claim 1, characterized in that: The obtaining of the reservoir isolation valve control strategy comprises: Based on the structural characteristic information of the target reservoir isolation valve and the reservoir production target, similarity calculation is performed with the reservoir isolation valve control strategy library to obtain a control strategy similarity set; Arranging the reservoir isolation valve control strategy library in descending order according to the control strategy similarity set to obtain an isolation valve control strategy sequence set; Performing strategy matching optimization based on the isolation valve control strategy sequence set to obtain a matching isolation valve control strategy; Based on the control strategy similarity set, deviation analysis and strategy optimization are performed on the matching isolation valve control strategy to obtain the reservoir isolation valve control strategy.

6. The method for adaptively controlling reservoir isolation valves in combination with reservoir distribution scenarios according to claim 1, characterized in that: The obtaining of the isolation valve coordinated control parameters comprises: selecting an adaptive learning network architecture according to the reservoir isolation valve control strategy; Acquire a reservoir isolation valve control parameter set, classify the reservoir isolation valve control parameter set by strategy control factors according to the reservoir isolation valve control strategy, and obtain an isolation valve control factor parameter set; Using the adaptive learning network architecture, branch training optimization is performed on the isolation valve control factor parameter set to determine an isolation valve control adaptive network; Based on the isolation valve control adaptive network, an adaptive control analysis is performed on the reservoir risk area parameter set, and the isolation valve coordinated control parameters are output.

7. The method for adaptively controlling reservoir isolation valves in combination with reservoir distribution scenarios according to claim 6, characterized in that: The determining isolation valve control adaptive network comprises: Using the adaptive learning network architecture, branch training and fusion are performed on the isolation valve control factor parameter set to obtain an initial isolation valve control fusion network; Verifying and evaluating the initial isolation valve control fusion network to obtain network performance parameters, and selecting and obtaining a network optimizer based on the network performance parameters; Construct a network loss function based on the adaptive network fusion optimization goal; The network loss function is minimized based on the network optimizer, and the initial isolation valve control fusion network is iteratively optimized according to the solution result to determine the isolation valve control adaptive network.

8. An adaptive control device for reservoir isolation valves in combination with reservoir distribution scenarios, characterized in that: The device for implementing the method for adaptively controlling a reservoir isolation valve in combination with a reservoir distribution scenario according to any one of claims 1 to 7 comprises: A data acquisition module, wherein the data acquisition module is used to deploy a sensor network in the target reservoir distribution scene area, wherein the sensor network integration includes a pressure sensor, a temperature sensor, and a flow sensor, and the reservoir scene environment data flow is monitored and acquired through the sensor network; A simulation modeling module, the simulation modeling module is used to collect and acquire the drilling exploration signal stream, perform reservoir simulation modeling based on the reservoir scene environment data stream and the drilling exploration signal stream, and construct a three-dimensional model of reservoir distribution; A simulation analysis module, which is used to perform reservoir parameter analysis and flow simulation based on the three-dimensional reservoir distribution model, obtain reservoir spatial distribution characteristics and reservoir flow trends, perform multi-dimensional risk assessment on the reservoir spatial distribution characteristics and reservoir flow trends, and determine a reservoir risk area parameter set; A matching module, wherein the matching module is used to construct a reservoir isolation valve control strategy library, and to match the reservoir isolation valve control strategy library with the structural characteristic information of the target reservoir isolation valve and the reservoir production target to obtain the reservoir isolation valve control strategy; A control module, the control module is used to perform adaptive control analysis on the reservoir risk area parameter set based on the reservoir isolation valve control strategy, obtain isolation valve coordination control parameters, and perform adaptive feedback control on the target reservoir isolation valve through the isolation valve coordination control parameters.

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