Anti-backflow system and method for half-way well cementation sliding sleeve

Abstract

The invention discloses an anti-backflow system and method for a half-way well cementation sliding sleeve, and belongs to the technical field of petroleum well cementation. The method comprises the steps that original data are collected and preprocessed to obtain first data; constructing a dynamic environment model for describing the relationship between the environment state and the first data by using a self-adaptive nonlinear function to obtain second environment data; inputting the second environment data to obtain an action space, and obtaining an optimal strategy through multiple rounds of training iteration; inputting the optimal strategy to obtain a pressure control parameter, and inputting the second environment data and the pressure control parameter to obtain a flow control parameter; and a flow control parameter and a pressure control parameter are input to verify the sealing effect of the sealing ring, and an optimization control strategy is fed back. According to the method, the cement flowing state and the sealing process are optimized through the reinforcement learning driven sealing pressure optimization and self-adaptive flow control technology, and therefore the backflow prevention capacity and the well cementation quality are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of oil well cementing, and particularly relates to an anti-backflow system and method for a half-way cementing sliding sleeve. Background Art

[0002] In the process of oil and gas field development, the cementing technology is a key link to ensure the wellbore integrity and long-term production capacity. As an important well completion tool, the half-way cementing sliding sleeve technology is widely used in liner cementing and wellbore isolation operations. The existing half-way cementing sliding sleeve usually adopts a structure in which the cementing liner hanger is first set and then cemented, and then the string is lifted and the sliding sleeve is set by pressing down. This structure can meet the cementing construction requirements to a certain extent, but there are still some insurmountable defects. First, during the cementing process, due to the complex and variable downhole pressure, the traditional cementing sliding sleeve needs to be operated by external tools to close after cementing. This process is easily affected by the complex downhole environment, which may lead to sealing delay or isolation failure, and then cause the cement slurry to flow back, affecting the cementing quality.

[0003] Secondly, the existing cementing sliding sleeve sealing rings usually adopt materials with fixed stiffness, and the sealing pressure cannot be adjusted. However, factors such as downhole temperature, pressure, and cement slurry viscosity have great uncertainties. The sealing rings with fixed stiffness are difficult to maintain the best sealing effect under all working conditions, which may lead to insufficient sealing and cement backflow, or excessive sealing pressure resulting in damage to the sealing rings. In addition, the traditional sliding sleeve switching tools rely on mechanical matching structures, but the downhole environment often has high temperature and pressure, and solid particles in the cement slurry may also deposit inside the sliding sleeve, resulting in jamming or even complete unrecoverability of the sliding sleeve during the switching process, affecting the construction progress and safety. In addition, during the cementing process, the flow state of the cement slurry is affected by factors such as downhole pressure gradient, density, and rheological properties. The existing cementing sliding sleeves usually cannot intelligently adjust the flow rate, making the cement slurry fill unevenly during the cementing process, which may lead to a decrease in cementing quality and even affect subsequent well completion operations. Finally, the traditional sliding sleeve cannot monitor the sealing state in real time during the cementing construction process. The sealing effect completely depends on the experience of construction personnel for judgment, lacking downhole data feedback and intelligent control mechanisms. Once the sealing problem occurs, it cannot be adjusted in time, increasing the risk of cementing construction failure.

[0004] Therefore, we propose an anti-backflow system and method for a half-way cementing sliding sleeve to solve the above problems. Summary of the Invention

[0005] The purpose of the present invention is to solve the problem that the existing technology cannot intelligently adjust the flow rate, resulting in uneven filling of the cement slurry during the cementing process, and to propose an anti-backflow system and method for a half-way cementing sliding sleeve.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] A backflow prevention system for a half - stage cementing sliding sleeve, comprising

[0008] A data acquisition module, which is configured to collect raw data through sensors and pre - process the raw data to obtain first data;

[0009] A model construction module, which is configured to use an adaptive non - linear function to construct a dynamic environment model describing the relationship between the environmental state and the first data. Among them, the first environmental data is weighted and summed through a neural network model, and after passing through multiple activation layers for non - linear mapping to output the predicted value of the environmental state, obtaining second environmental data;

[0010] A decision - making module, which is configured to obtain an action space according to the second environmental data. The action space consists of parameters for controlling the sealing pressure, and the optimal action space is optimized through a deep Q - learning algorithm. At the same time, a reward function is set to adjust the balance between pressure adjustment and equipment safety, and an optimal strategy is obtained through multiple rounds of training iterations;

[0011] A pressure control module, which is configured to obtain the pressure control parameters for control at the corresponding time point by inputting the optimal strategy, and return the environmental feedback at each time point to the decision - making module to adjust the pressure control parameters;

[0012] A flow control module, which adjusts the flow distribution and regulation strategy based on the second environmental data and the pressure control parameters finally obtained in the pressure control module, and finally obtains the flow control parameters through objective function optimization;

[0013] A feedback optimization module, which regularly verifies the sealing effect of the sealing ring by inputting the flow control parameters and the pressure control parameters, measures the deviation between the current parameters and the target value through the pressure error, and optimizes the control strategy based on the deviation feedback.

[0014] Preferably, the raw data includes different physical quantities, specifically temperature, pressure, flow velocity, and viscosity.

[0015] Preferably, the data pre - processing is to smooth the data through a weighted filtering algorithm, and perform weighted averaging on the data at adjacent time points through a weighted filter to reduce noise.

[0016] Preferably, in the reward function, a penalty term for pressure change and pressure constraints are introduced to balance pressure adjustment and equipment safety.

[0017] Preferably, a constraint optimization algorithm is also set in the pressure control module. By adding pressure stability constraints and penalizing the rate of change of pressure on the basis of minimizing the difference between the target pressure and the actual downhole pressure, the smoothness of parameter changes is controlled to optimize the sealing ring pressure regulation process.

[0018] Preferably, the objective function used in the flow control module is defined as minimizing the difference between the current pressure and the target pressure after flow regulation, ensuring the smooth change and stability of the flow rate.

[0019] Preferably, an adaptive optimization strategy is introduced in the flow control module. By feedback-adjusting the control parameters, calculating the effect of the flow regulation strategy, and adjusting the control parameters of the flow rate according to the optimization objective.

[0020] A method for preventing backflow of a half-stage cementing sliding sleeve, comprising:

[0021] Collecting original data through sensors and preprocessing the original data to obtain first data;

[0022] Using an adaptive non-linear function to construct a dynamic environment model describing the relationship between the environmental state and the first data, wherein the first environmental data is weighted and summed through a neural network model, and non-linear mapping is performed through multiple activation layers to output the predicted value of the environmental state, obtaining second environmental data;

[0023] Inputting the second environmental data to obtain an action space, which consists of parameters for controlling the sealing pressure, and optimizing the action space through the deep Q-learning algorithm. At the same time, a reward function is set to adjust the balance between pressure adjustment and equipment safety, and an optimal strategy is obtained through multiple rounds of training iterations;

[0024] Inputting the optimal strategy to obtain the pressure control parameters for control at the corresponding time points, and returning the environmental feedback at each time point to the decision-making module to adjust the pressure control parameters;

[0025] Inputting the second environmental data and the pressure control parameters to adjust the distribution and regulation strategy of the flow rate, and finally obtaining the flow control parameters through optimization of the objective function;

[0026] Inputting the flow control parameters and the pressure control parameters to verify the sealing effect of the sealing ring, and measuring the deviation between the current parameters and the target value through the pressure error, and instantaneously feedback-optimizing the control strategy based on the deviation.

[0027] In summary, the technical effects and advantages of the present invention: Through the sealing pressure optimization and adaptive flow control technologies driven by reinforcement learning, the present invention realizes the intelligent optimization of the cement flow state and the sealing process, thereby improving the anti-backflow ability and the cementing quality. Brief Description of the Drawings

[0028] Figure 1 It is a schematic diagram of the system structure in the present invention;

[0029] Figure 2 It is a schematic diagram of the method steps in the present invention. Specific implementation manners

[0030] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments.

[0031] As Figure 1 shown, a backflow prevention system for a half - stage cementing sliding sleeve includes a data acquisition module, and the data acquisition module is configured to collect raw data through sensors and pre - process the raw data to obtain first data;

[0032] A model construction module, the model construction module is configured to use an adaptive non - linear function to construct a dynamic environment model describing the relationship between the environmental state and the first data, wherein the first environmental data is weighted and summed through a neural network model, and passes through multiple activation layers for non - linear mapping to output a predicted value of the environmental state to obtain second environmental data;

[0033] A decision - making module, the decision - making module is configured to obtain an action space according to the second environmental data, the action space consists of parameters for controlling the sealing pressure, and optimize to obtain an optimal action space through a deep Q - learning algorithm. At the same time, a reward function is set to adjust the balance between pressure adjustment and equipment safety, and an optimal strategy is obtained through multiple rounds of training iterations;

[0034] A pressure control module, the pressure control module is configured to obtain real - time control pressure control parameters by inputting the optimal strategy, and return the environmental feedback at each time point to the decision - making module to adjust the pressure control parameters in real time;

[0035] A flow control module, the flow control module adjusts the flow distribution and regulation strategy based on the second environmental data and the pressure control parameters finally obtained in the pressure control module, and optimizes through an objective function to finally obtain flow control parameters;

[0036] A feedback optimization module, the feedback optimization module inputs the flow control parameters and the pressure control parameters to verify the sealing effect of the sealing ring in real time, measures the deviation between the current parameters and the target value through the pressure error, and optimizes the control strategy in real time based on the deviation.

[0037] The technical solutions in the embodiments of the present application at least have the following technical effects or advantages: The reinforcement learning algorithm is used to optimize the sealing pressure control strategy of the sliding sleeve. This method analyzes real-time data such as downhole pressure, flow rate, temperature, and viscosity, and dynamically adjusts the expansion degree of the sealing ring based on the reinforcement learning model, enabling the sliding sleeve sealing ring to always maintain the best sealing state under different downhole working conditions, avoiding both the problem of cement backflow caused by insufficient sealing and the problem of sealing ring damage caused by excessive sealing pressure.

[0038] The embodiments of the present application also provide a method for preventing backflow of a half-way cementing sliding sleeve, as Figure 2 shown

[0039] Collect the original data through sensors and preprocess the original data to obtain the first data;

[0040] Use an adaptive non-linear function to construct a dynamic environment model describing the relationship between the environmental state and the first data, where the first environmental data is weighted and summed through a neural network model, and non-linear mapping is performed through multiple activation layers to output the predicted value of the environmental state, obtaining the second environmental data;

[0041] Input the second environmental data to obtain the action space, which consists of parameters for controlling the sealing pressure, and optimize the optimal action space through the deep Q-learning algorithm. At the same time, a reward function is set to adjust the balance between pressure adjustment and equipment safety, and an optimal strategy is obtained through multiple rounds of training iterations;

[0042] Input the optimal strategy to obtain the pressure control parameters for control at the corresponding time points, and return the environmental feedback at each time point to the decision-making module to adjust the pressure control parameters;

[0043] Input the second environmental data and pressure control parameters to adjust the flow distribution and regulation strategy, and finally obtain the flow control parameters through the optimization of the objective function;

[0044] Input the flow control parameters and pressure control parameters to verify the sealing effect of the sealing ring, measure the deviation between the current parameters and the target value through the pressure error, and instantaneously feedback and optimize the control strategy based on the deviation.

[0045] Step 1: Downhole data collection and environmental modeling

[0046] In this step, we hope to collect environmental data through the downhole sensor network and use this data to establish a dynamic environment model, providing the necessary input for the subsequent optimization steps.

[0047] 1.1 Data collection and sensor network design

[0048] Data acquisition consists of multiple downhole sensors (such as temperature, pressure, flow rate, viscosity, etc.), and these sensors collect data at a frequency of updates per second. Each data point contains multiple physical quantities, such as temperature , pressure , flow rate and viscosity . The total amount of data is , which can be expressed as:

[0049]

[0050] where:

[0051] represents the temperature at the th moment;

[0052] represents the pressure at the th moment;

[0053] represents the flow rate at the th moment;

[0054] represents the viscosity at the th moment.

[0055] 1.2 Data Preprocessing and Smoothing

[0056] The collected data usually contains noise, so preprocessing is required. In order to remove noise and strengthen the trend of the signal, we use a weighted filtering algorithm to smooth the data. The weighted filter reduces noise by taking a weighted average of the data at adjacent moments. The formula is as follows:

[0057]

[0058] where:

[0059] is the smoothed data;

[0060] is the original data;

[0061] is the weighting coefficient, using an exponential decay weight:

[0062]

[0063] where, controls the decay rate, is the time difference between the data moment and the current moment .

[0064] This method effectively reduces high-frequency noise while preserving the trend information of the data, providing a stable input for environmental modeling.

[0065] 1.3 Environmental Modeling

[0066] By modeling the smoothed data, we establish a dynamic environmental model that can reflect the state changes of the underground environment in real time. We use an adaptive non-linear function to describe the relationship between the environmental state and the data. The core formula of the model is:

[0067]

[0068] Where:

[0069] is the state estimate of the underground environment at time ;

[0070] is a non-linear function, and the parameters of are adaptively adjusted according to real-time data.

[0071] To effectively capture the complex non-linear relationships in environmental data, we choose to use a neural network model to construct the function . Specifically, we use a multi-layer perceptron (MLP), which is a deep learning model widely used in regression problems. This neural network performs a weighted sum on the input data and undergoes non-linear mapping through multiple activation layers to output the predicted value of the environmental state.

[0072] During the training process, the weight parameters of the neural network are automatically adjusted according to the actual data, enabling the model to predict the state of the underground environment more and more accurately.

[0073] To address the challenges of data changes in a dynamic environment, we design an incremental learning algorithm. Different from traditional batch learning methods, incremental learning can update the model in real time as new data arrives without having to retrain the entire model. This is crucial for our environmental modeling because the underground environmental data is constantly changing, and the model needs to flexibly respond to data changes at each moment. Specifically, the model parameters are adjusted according to the following rules:

[0074]

[0075] Where:

[0076] is the learning rate;

[0077] is the gradient of the loss function, representing the error between the model's predicted value and the actual observed value.

[0078] This algorithm ensures that the model can be effectively updated when new data is input, so as to improve the accuracy of environmental prediction.

[0079] 1.4 Optimization of the environmental model

[0080] To avoid overfitting of the model, a regularization term is added to promote the smooth change of model parameters. The optimization objective is:

[0081]

[0082] Where:

[0083] is the loss function, representing the prediction error of the model;

[0084] is the regularization term, used to smooth parameter changes and prevent the model from relying too much on local data.

[0085] This optimization objective enables the model to not only pursue the minimization of error during training, but also ensures the stability of model parameters. Through the above steps, we have achieved the entire process from data collection to environmental modeling, ensuring the efficiency of data processing and the dynamic update ability of the model. This model will provide accurate environmental state predictions for subsequent optimization steps and effective decision-making support for underground operations.

[0086] Step 2: Optimize the sealing pressure by reinforcement learning

[0087] Environmental and state definition We first need to use the dynamic environmental model defined in Step 1 to obtain the real-time state of underground equipment. These state variables include temperature, pressure, flow rate, viscosity, etc., which together constitute the environmental state . The specific state variable definitions are as follows:

[0088]

[0089] Where:

[0090] : Underground pressure

[0091] : Underground temperature

[0092] : Fluid flow rate

[0093] : Fluid viscosity

[0094] Reinforcement Learning Model Establishment and Action Space Design In the reinforcement learning framework, we model the downhole seal pressure control task as a Markov decision process. The action space consists of the parameters for controlling the seal pressure, which are adjusted within a specific range.

[0095] The goal of reinforcement learning is to maximize the operating effect of downhole equipment through an intelligent policy that gives the corresponding optimal action according to the environmental state

[0096] Action Value Function Update and Deep Q-Learning Algorithm We use the Deep Q-Learning (DQN) algorithm to optimize the seal pressure control strategy. The action-value function is used to represent the expected reward for taking action in a given state. Based on the Bellman equation, we update the Q value through the following formula:

[0097]

[0098] Where:

[0099] : Learning rate, controlling the amplitude of each update

[0100] : Immediate reward

[0101] : Discount factor, controlling the influence of future rewards

[0102] And : Next state and next action

[0103] The Deep Q-Network (DQN) approximates the action-value function through a neural network The input of the network structure is the environmental state and the output is the Q value corresponding to each possible action

[0104] Reward Function Design and Innovation To more accurately reflect the working objectives of downhole equipment, we innovatively design a new reward function that not only considers the adjustment of the seal pressure but also introduces a penalty term for pressure changes and pressure constraints. The specific form is:

[0105]

[0106] Where:

[0107] : Target seal pressure

[0108] : Current downhole pressure

[0109] : Pressure change

[0110] : Indicator function to ensure that the pressure does not exceed the maximum safe pressure

[0111] 、 : Importance of controlling pressure fluctuations and safety constraints

[0112] Through this reward function, the agent can effectively balance pressure adjustment and equipment safety, reducing the negative impact caused by excessive adjustment.

[0113] Policy optimization and iteration During the multi-round training process, the agent updates the sealing pressure control policy according to the environmental state continuously , and iteratively optimizes the policy through the deep Q-learning method. Finally, the agent obtains an optimal policy that can dynamically adjust the sealing pressure value according to the real-time state, ensuring that the equipment always operates in the best working state.

[0114] During the training process, the agent needs to balance exploration and exploitation to avoid unstable policies caused by excessive exploration. Through repeated training and evaluation, the policy will continuously approach the optimal solution.

[0115] Output policy deployment and real-time control After training is completed, the optimized policy can be deployed in actual downhole operations. By obtaining the downhole environmental state in real time, the agent can calculate and output the optimal sealing pressure control value in real time . This intelligent control can adapt to changes in the downhole environment and improve the operation stability and safety of the equipment.

[0116] Step 3: Intelligent seal ring adaptive control

[0117] Intelligent seal ring adaptive control model design In the adaptive control step of the intelligent seal ring, we designed a feedback regulation mechanism based on the aforementioned reinforcement learning model (the deep Q-network model defined in step 2 ). This mechanism adjusts the operating parameters of the seal ring by monitoring the downhole environmental state in real time . The input is the optimal control policy from step 2 , and the output is the real-time control seal ring pressure adjustment parameter , this control decision is updated according to the current environmental state at each moment. On this basis, we design the control of the sealing ring as a closed-loop feedback system, which can be adjusted in real time according to the changes in the downhole environmental state. Specifically, the control strategy according to each time step of the environmental feedback to calculate the sealing ring pressure adjustment action , which directly affects the sealing performance of the sealing ring.

[0118] Intelligent pressure regulation algorithm: Constrained objective optimization To further improve the stability and response speed of the control system, the present invention proposes a constrained optimization algorithm, which optimizes the sealing ring pressure regulation process by adding pressure stability constraints and considering the smoothness of control parameter changes. The constrained optimization model can be expressed in the following form:

[0119]

[0120] Where:

[0121] is the target sealing pressure, set as the ideal downhole pressure value.

[0122] is the current downhole pressure.

[0123] is the pressure change rate, reflecting the pressure change after the sealing ring adjustment.

[0124] is the maximum pressure that the sealing ring can withstand.

[0125] is an indicator function, which takes the value of 0 when exceeds the maximum pressure, and 1 otherwise.

[0126] is an adjustment parameter, controlling the pressure fluctuation and safety weight.

[0127] This formula designs an objective function, by minimizing the difference between the target pressure and the actual downhole pressure, and on this basis, through a penalty term for the pressure change rate to smooth the adjustment process and avoid too drastic pressure changes.

[0128] Real-time update and optimization of the adaptive control strategy To cope with the complex and dynamic changes in the downhole environment, the present invention proposes an adaptive feedback control mechanism, which can update and adjust the sealing ring control strategy in real time. Specifically, based on the optimal control action calculated in the previous optimization step , online training is carried out through the iterative process of reinforcement learning to continuously optimize the operation of the sealing ring. Its control update formula is:

[0129]

[0130] Where:

[0131] is the control parameter of the sealing ring at the previous moment.

[0132] is the learning rate, which controls the amplitude of each parameter update.

[0133] is the action-value function in the current state.

[0134] is the action-value function in the previous state.

[0135] By continuously updating the control strategy, this method can achieve dynamic adaptation to environmental changes and ensure that the sealing ring can maintain the best working state in any environment.

[0136] Our steps can be summarized as the following steps:

[0137] Input: The control strategy from Step 2 , the real-time feedback environmental state , including parameters such as downhole temperature and pressure.

[0138] Processing: Calculate the difference between the target pressure and the current pressure according to the environmental state adjust the pressure through the above optimization formula, and adjust the pressure change rate according to the real-time feedback, and finally calculate the adjusted sealing ring pressure control value .

[0139] Output: The updated control parameter of the sealing ring , used for pressure adjustment at the next moment. The final control strategy forms a closed-loop system through continuous feedback and optimization.

[0140] Application and Deployment In the actual deployment process, through the continuously updated intelligent control strategy , the control system of the sealing ring can automatically adjust the sealing pressure according to the environmental state at each time step, maximizing the stability and safety of downhole equipment. The entire system can flexibly adjust under different working conditions through real-time feedback and adaptive mechanisms, avoiding equipment damage or low-efficiency operation caused by excessive operation.

[0141] Step 4: Adaptive Flow Optimization Control

[0142] In the previous steps, we have optimized the pressure regulation of the sealing ring through the deep Q-network model and the intelligent sealing ring adaptive control design. The main objective of this step is to further improve the performance of the entire system in the downhole environment, especially regarding the impact of dynamic flow rate changes on the sealing ring pressure control. The flow rate we are concerned with is the flow velocity and flow density of the liquid or gas around the sealing ring, which directly affects the sealing performance and pressure stability of the sealing ring. Therefore, real-time optimization control of the flow rate is very important because flow rate fluctuations may lead to unstable pressure control, thereby affecting the sealing effect of the sealing ring. We hope that by introducing adaptive flow rate optimization control, we aim to adjust the distribution of the liquid or gas flow rate in real time to ensure that the sealing ring can continuously maintain the ideal working pressure under different flow rate conditions and avoid damage to the sealing ring due to excessive or insufficient flow rate fluctuations.

[0143] The input data includes:

[0144] Environmental state (such as temperature, pressure, flow rate, etc. downhole).

[0145] The sealing ring control parameters output from the previous step (the adjustment value affecting the sealing ring pressure).

[0146] Flow rate optimization model Design To achieve adaptive flow rate optimization control, we propose a feedback control mechanism based on the relationship between flow rate fluctuations and pressure changes. This mechanism adjusts the flow rate distribution and regulation strategy by monitoring the changes in the flow rate in real time and according to the relationship between the flow rate and pressure. Specifically, based on the current downhole flow rate and pressure information, the optimal flow rate distribution strategy is calculated.

[0147] The goal is to stabilize the pressure of the sealing ring within the ideal range through flow rate adjustment. The objective function can be defined as minimizing the pressure fluctuations after flow rate adjustment while ensuring the smooth change and stability of the flow rate. The optimization model can be expressed as:

[0148]

[0149] Where:

[0150] is the flow rate adjustment parameter at the current time point.

[0151] is the target pressure (the ideal downhole pressure).

[0152] is the current downhole pressure.

[0153] is the rate of change of the current pressure, representing the pressure change after flow rate adjustment.

[0154] is the flow rate change rate, which indicates the smoothness of flow adjustment.

[0155] To adjust the parameters, the pressure fluctuation and flow rate change smoothness are controlled separately.

[0156] The objective function is designed to minimize the difference between the current pressure and the target pressure while taking into account the smoothness of pressure changes and the smooth regulation of flow to ensure that no excessive pressure fluctuations or flow changes will occur during the operation of the seal, thereby minimizing the impact on the performance of the seal.

[0157] Adaptive Optimization Control Strategy In the process of flow optimization, we introduced an adaptive online optimization mechanism, which enables the control system to adjust the control parameters according to real-time feedback. The core of flow optimization is based on the current state , combined with the changes in flow and pressure at the previous moment, update the current flow regulation parameters .

[0158] Control strategy update formula:

[0159]

[0160] in:

[0161] is the flow control parameter at the previous moment.

[0162] is the learning rate, which controls the magnitude of each flow adjustment.

[0163] It is the flow-value function in the current state.

[0164] It is the flow-value function at the previous moment.

[0165] The update formula is based on reinforcement learning, using the traffic-value function , calculate the effect of the current flow regulation strategy in real time, and adjust the flow control parameters according to the optimization goal Through continuous feedback adjustment, the system can adaptively optimize pressure regulation under different flow conditions to ensure the best working condition of the sealing ring.

[0166] In actual application, through adaptive flow optimization control, the system can automatically adjust the flow distribution and pressure control parameters according to each feedback, ensuring that the sealing ring always maintains the ideal working pressure in a dynamically changing environment and avoiding the negative impact of flow fluctuations on the equipment. The final output flow adjustment parameters and the sealing ring pressure control parameters are applied to subsequent downhole operations, achieving fully adaptive intelligent control.

[0167] Step 5: Closed-loop verification and adjustment of the sealing state

[0168] In this step, we will implement the closed-loop verification and adjustment of the sealing ring system. This step is based on the output of the previous flow optimization control. The goal is to ensure that the sealing ring can always maintain its ideal working state during actual operation, and adjust the sealing state by further optimizing the control parameters. We adopt a closed-loop feedback mechanism to verify the sealing state in real time and adjust the control strategy based on the verification results, thereby ensuring the reliability and stability of the sealing system.

[0169] Input data The input for this step comes from the output of Step 4 adaptive flow optimization control, including:

[0170] Flow regulation parameters : The optimization result of flow adjustment.

[0171] Pressure control parameters : The adjusted value of the sealing ring pressure obtained through adaptive control optimization.

[0172] These output parameters are used to further adjust the working state of the sealing ring and calibrate the control strategy through real-time verification.

[0173] Closed-loop verification model design During actual operation, the verification of the sealing state is achieved by detecting the sealing effect of the sealing ring under specific working conditions. We designed a closed-loop feedback model based on the difference between the working pressure and the target pressure of the sealing ring to evaluate the effectiveness of the current control strategy.

[0174] To achieve closed-loop verification, we first define the working pressure error of the sealing ring , and calculate the difference between the current pressure and the target pressure:

[0175]

[0176] Where:

[0177] is the pressure error of the sealing ring.

[0178] is the downhole pressure at the current moment.

[0179] is the set target pressure (i.e., the ideal working pressure).

[0180] Pressure error It is used to measure the deviation between the current pressure regulation and the target value. The smaller this value is, the more effective the current control strategy is.

[0181] The feedback adjustment mechanism is designed based on the pressure error of the sealing ring , and we propose a feedback mechanism for real-time adjustment of the control strategy. By weighted adjustment of the pressure error, we can optimize the flow regulation parameters and the pressure control parameters to achieve precise control of the sealing ring pressure.

[0182] Specifically, the system will adjust the flow regulation parameters and the pressure control parameters according to the magnitude of the current pressure error . We use the following feedback adjustment formula:

[0183]

[0184]

[0185] Where:

[0186] and are the feedback adjustment coefficients, controlling the adjustment amplitude.

[0187] is the flow adjustment parameter at the current moment, controlling the sensitivity of the flow adjustment.

[0188] is the pressure control parameter at the current moment, controlling the sensitivity of the pressure adjustment.

[0189] In the flow adjustment formula, will be corrected according to the pressure error to adapt to the current pressure demand.

[0190] In the pressure control formula, will be adjusted according to the error to ensure that the working pressure of the sealing ring is closer to the target value.

[0191] This step further optimizes the flow and pressure control strategies through closed-loop verification and adjustment of the sealing state. By real-time monitoring of the pressure error and dynamically adjusting the control parameters, we can ensure that the sealing ring always maintains an ideal working state and effectively cope with the changes in the downhole environment. This solution not only improves the performance of the sealing ring but also enhances the adaptive ability of the system, ultimately achieving stable and reliable operation of the sealing system.

[0192] The technical solutions in the embodiments of the present application at least have the following technical effects or advantages: By combining reinforcement learning to optimize the sealing pressure and adaptive flow control technology, the problems of unstable sealing of existing cementing sliding sleeves, cement backflow, and uncontrollable flow velocity are solved, making the cementing process more intelligent and efficient, and can be widely applied to complex working conditions such as semi - stage cementing, liner cementing, and horizontal well cementing.

[0193] The working principle is as follows: A reinforcement learning algorithm is used to optimize the sealing pressure control strategy of the sliding sleeve. This method analyzes real - time data such as downhole pressure, flow velocity, temperature, and viscosity, and dynamically adjusts the expansion degree of the sealing ring based on the reinforcement learning model, so that the sliding sleeve sealing ring can always maintain the best sealing state under different downhole working conditions, avoiding both the problem of cement backflow caused by insufficient sealing and the problem of sealing ring damage caused by excessive sealing pressure. An adaptive intelligent flow control technology is adopted. The flow velocity of the cement slurry is monitored in real time through a downhole sensor network, and the flow state of the cement is analyzed in combination with a time - series prediction model, so as to intelligently adjust the opening and closing degree of the sliding sleeve to ensure uniform filling of the cement slurry and improve the overall quality of cementing. A downhole real - time monitoring and intelligent feedback mechanism is introduced. The data such as the sealing state, pressure, and flow rate are collected by the downhole sensing system and analyzed in real time by the intelligent control unit. If abnormal sealing or a trend of cement backflow is detected, the system can automatically adjust the sealing pressure or the sliding sleeve flow rate, thus forming a closed - loop control to ensure the safety and reliability of the cementing process.

[0194] As mentioned above, the above are only the preferred specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solutions and inventive concepts of the present invention, makes equivalent substitutions or changes, and all should be covered by the protection scope of the present invention.

Claims

1. A backflow prevention system for a half-way cementing sliding sleeve, characterized in that, including: a data acquisition module, which is configured to collect raw data through sensors and preprocess the raw data to obtain first data; a model construction module, which is configured to use an adaptive non - linear function to construct a dynamic environment model describing the relationship between the environmental state and the first data, wherein the first environmental data is weighted and summed through a neural network model, and non - linear mapping is performed through multiple activation layers to output a predicted value of the environmental state, obtaining second environmental data; a decision - making module, which is configured to obtain an action space according to the second environmental data, the action space consists of parameters for controlling the sealing pressure, and the optimal action space is optimized through a deep Q - learning algorithm. At the same time, a reward function is set to adjust the balance between pressure adjustment and equipment safety, and an optimal strategy is obtained through multiple rounds of training iterations; a pressure control module, which is configured to obtain pressure control parameters for control at corresponding time points by inputting the optimal strategy, and return the environmental feedback at each time point to the decision - making module to adjust the pressure control parameters; a flow control module, which adjusts the flow distribution and regulation strategy based on the second environmental data and the pressure control parameters finally obtained in the pressure control module, and finally obtains the flow control parameters through the optimization of the objective function; a feedback optimization module, which regularly verifies the sealing effect of the sealing ring by inputting the flow control parameters and the pressure control parameters, measures the deviation between the current parameters and the target value through the pressure error, and optimizes the control strategy based on the deviation feedback; 2. The anti-backflow system for the half-way cementing sliding sleeve according to claim 1, characterized in that, The raw data includes different physical quantities, specifically temperature, pressure, flow rate and viscosity.

3. The anti-backflow system for the half-way cementing slip sleeve according to claim 1, characterized in that, The data pre - processing is to smooth the data through a weighted filtering algorithm, and perform weighted averaging on the data at adjacent moments through a weighted filter to reduce noise.

4. The anti-backflow system for the half-way cementing sliding sleeve according to claim 1, wherein In the reward function, a penalty term for pressure change and pressure constraints are introduced to balance pressure adjustment and equipment safety.

5. The anti-backflow system for the half-way cementing sliding sleeve according to claim 1, wherein, A constraint optimization algorithm is also set in the pressure control module. By adding pressure stability constraints and based on minimizing the difference between the target pressure and the actual downhole pressure, a penalty term for the pressure change rate is used to control the smoothness of parameter changes to optimize the sealing ring pressure adjustment process.

6. The anti-backflow system for the semi-batch cementing sliding sleeve according to claim 1, wherein, The objective function used in the flow control module is defined as minimizing the difference between the current pressure and the target pressure after flow regulation, ensuring the smooth change and stability of the flow.

7. The anti-backflow system for the half-way cementing sliding sleeve according to claim 1, wherein, An adaptive optimization strategy is introduced in the flow control module. By feedback - adjusting the control parameters, calculating the effect of the flow regulation strategy, and adjusting the flow control parameters according to the optimization objective.

8. A method for preventing backflow of a half-way cementing sliding sleeve, characterized in that, including: collecting raw data through sensors and preprocessing the raw data to obtain first data; using an adaptive non - linear function to construct a dynamic environment model describing the relationship between the environmental state and the first data, wherein the first environmental data is weighted and summed through a neural network model, and non - linear mapping is performed through multiple activation layers to output a predicted value of the environmental state, obtaining second environmental data; The second environmental data is input to obtain an action space, which consists of parameters for controlling the seal pressure and is optimized by the deep Q-learning algorithm to obtain the optimal action space. At the same time, a reward function is set to adjust the balance between pressure adjustment and equipment safety. After multiple rounds of training iterations, an optimal strategy is obtained; The optimal strategy is input to obtain the pressure control parameters for control at the corresponding time points, and the environmental feedback at each time point is returned to the decision-making module to adjust the pressure control parameters; The second environmental data and pressure control parameters are input to adjust the flow distribution and regulation strategy, and the flow control parameters are finally obtained through optimization of the objective function; The flow control parameters and pressure control parameters are input to verify the sealing effect of the sealing ring, and the pressure error is used to measure the deviation between the current parameters and the target value, and the control strategy is optimized based on the deviation for immediate feedback.

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