An intelligent well construction safety management and control system

Abstract

The present invention discloses an intelligent well construction safety management and control system, which relates to the field of coal well construction technology. The system includes a data acquisition and fusion module, a risk assessment module, a decision engine module, a control execution module, a human-computer interaction module, and a decision knowledge base module. The risk assessment module constructs a risk assessment model based on the Transformer encoder-decoder framework to assess potential well construction risks. The decision engine module constructs a decision strategy generation model based on the risk assessment results and the decision knowledge base query results to generate high-level decision instructions and parameter adjustment sequences. The control execution module converts the parameter adjustment sequence into a control instruction sequence, which is then sent to the actual well construction control system for execution via a data communication interface. Based on the Transformer encoder-decoder structure, the present invention introduces an augmentation strategy and self-supervised comparative learning, which improves the generalization ability and robustness of the risk assessment model and realizes time-series prediction of risk situations and risk level classification.

Description

Technical Field

[0001] The present invention relates to the technical field of safety management and control, and in particular to an intelligent well construction safety management and control system. Background Art

[0002] Coal mining is a high-risk operation, with well construction being a critical step. Due to the complex and ever-changing geological conditions underground, the immense rock pressure and surrounding rock compression, well construction presents numerous safety hazards, such as gas inrush, water inrush, well collapse, and pipe damage. These hazards, if encountered, pose significant threats to the safety of personnel and equipment, and can result in significant economic losses.

[0003] Traditional well construction safety management and control methods rely primarily on manual monitoring and empirical judgment, which presents several significant drawbacks. For one thing, delayed response is a major issue. The well construction environment is extremely complex and volatile, and safety risks often emerge swiftly and relentlessly. Relying on manual judgment makes it difficult to achieve real-time monitoring and rapid response. Furthermore, the human factor leads to one-sided and highly subjective risk assessments, which is also a major drawback. Different personnel have varying experience and judgment abilities, and people's ability to maintain sustained attention makes it easy to miss critical risk signals. Traditional methods lack real-time performance, accuracy, and efficiency, making them difficult to promptly detect and control potential risks and ultimately fail to ensure the safety and reliability of well construction operations. Summary of the Invention

[0004] In view of the fact that the existing well construction safety management and control methods mainly rely on manual monitoring and experience judgment, there are obvious defects such as delayed response and one-sided risk assessment. It is difficult to timely discover and control various safety risks and hidden dangers in well construction operations, thus affecting the safety and efficiency of operations. This invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is how to achieve real-time monitoring, intelligent assessment and rapid decision-making of well construction risks, thereby effectively improving the safety level and reliability of well construction operations.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In the first aspect, an embodiment of the present invention provides an intelligent well construction safety management and control system, which includes a data acquisition and fusion module for acquiring various raw data in the well construction process, preprocessing the raw data, constructing a standardized time series data sample set, and inputting the time series data sample set into a risk assessment module; a risk assessment module, which constructs a risk assessment model based on the encoder-decoder framework of Transformer, receives the time series data sample set output by the data acquisition and fusion module, evaluates the potential risks of well construction in real time, and inputs the evaluation results into a decision engine module and a human-computer interaction module, wherein the evaluation results include a risk situation sequence and a risk level; a decision engine module, which sends a query request to a decision knowledge base module, constructs a decision strategy generation model based on the risk situation sequence and risk level output by the risk assessment module and the query results, and generates high-level decision instructions and parameter adjustment sequence, input high-level decision instructions into the human-computer interaction module, and input the parameter adjustment sequence into the control execution module; the control execution module is used to receive the parameter adjustment sequence output by the decision engine module, convert the parameter adjustment sequence into a corresponding control instruction sequence, and send the control instruction sequence to the actual well construction control system for execution through the data communication interface; the machine interaction module includes a visualization interface and a natural language interaction interface. The visualization interface is used to display risk assessment results, decision results and control execution status. The natural language interaction interface supports manual intervention in decision-making and feeds back manual intervention instructions to the risk assessment module, decision engine module or optimization control module; the decision knowledge base module is used to build a decision knowledge base, receive query requests from the decision engine module, retrieve relevant data from the knowledge base according to the query request, and input the retrieval results into the decision engine module.

[0008] As a preferred solution of the intelligent well construction safety management and control system described in the present invention, the operation process of the data acquisition and fusion module is as follows: various raw data in the well construction process are collected to construct an original well construction process data set; feature data related to well construction risk assessment is selected from the original well construction process data set; the selected feature data are fused using a self-attention mechanism, and adaptive weights are assigned to each feature according to its importance to risk assessment; missing value processing is performed on the fused feature data, features with missing values ​​are detected, and the missing values ​​are filled using the historical data interpolation method of similar features; feature encoding is performed on the processed feature data set, one-hot encoding is performed on discrete features, and normalized encoding is performed on continuous features; according to the time attribute of the features, the encoded feature data is divided into three categories: time series features, discrete features, and continuous features; sliding window segmentation is performed on the time series features, the window size is set, and a time series feature sequence is obtained; the segmented time series feature sequence is spliced ​​with the discrete features and continuous features in the corresponding window to construct multivariate feature data; the spliced ​​multivariate feature data is formatted into a time series data sample set, and the formatted time series data sample set is input into the risk assessment module.

[0009] As a preferred solution of the intelligent well construction safety management and control system described in the present invention, the operation process of the risk assessment module is as follows: receiving the time series data sample set output by the data acquisition and fusion module, and dividing the time series data sample set into a training set and a test set according to a preset ratio; augmenting the training set data to obtain an augmented data set, wherein the augmentation includes time series perturbation augmentation, feature mask augmentation and semantic preservation augmentation; designing a Transformer-based encoder-decoder model framework as a risk assessment model; adopting the self-supervised comparative learning strategy of the masked language model to train the risk assessment model, and optimizing the parameters of the encoder and decoder; during the training process, the cycle The current model performance is evaluated on the test set. If the performance meets the predefined conditions, training is stopped early; otherwise, training is continued until the maximum number of training rounds is reached. The generalization performance of the trained risk assessment model is comprehensively evaluated on the test set. If it meets the preset evaluation indicators, it is deployed. The deployed risk assessment model is used to conduct risk assessment on the real-time well construction data stream to obtain the risk situation sequence and risk level. The risk situation sequence is input into the human-computer interaction module, and feedback instructions are received to determine whether the risk assessment model needs to be retrained. If necessary, new well construction process data is collected and retrained and evaluated. If not, the risk situation sequence and risk level are input into the decision engine module.

[0010] As an optimal solution of the intelligent well construction safety management and control system described in the present invention, the framework of the risk assessment model includes an encoder part and a decoder part, the encoder part includes a timing encoder, a risk relationship modeler and an attention fusion module, and the decoder part includes a first decoding head and a second decoding head.

[0011] As a preferred solution of the intelligent well construction safety management and control system of the present invention, the risk assessment model is trained by the following steps: obtaining the original training set and the augmented data set; sampling a batch of data X and X' from the original training data and the augmented data set respectively; randomly masking the partial positions of X and X' to obtain masked inputs X_masked and X'_masked; inputting X_masked and X'_masked into the encoder respectively, and the encoder outputs the corresponding time series feature representation and ; Calculate the contrast loss of the encoder output ;Will and Input the first decoding head and the second decoding head of the decoder respectively. The first decoding head outputs the predicted sequence Y_pred and Y'_pred. Calculate the generation loss of Y_pred and the original label Y The second decoding head outputs the classification probabilities P and P', and calculates the classification loss between P and the original label C. ; The contrast loss , generate losses , classification loss Perform weighted summation to get the total loss Based on total loss , calculate the gradient of the training parameters and use the gradient descent algorithm to backpropagate and optimize the training parameters; contrast loss The specific formula is as follows:

[0012]

[0013] in, and Represent the output representation of the encoder for the original input X and the augmented input X', respectively. for The transpose of is the L2 norm, is a scalar.

[0014] As a preferred solution of the intelligent well construction safety management and control system described in the present invention, the operation process of the decision engine module is as follows: receiving the risk situation sequence and risk level output by the risk assessment module; sending a query request to the decision knowledge base module, the query conditions including the risk situation sequence, risk level and current well construction stage, and receiving the query results returned by the decision knowledge base module, the query results including parameter data subsets, operation process templates, historical risk cases and safety constraints; constructing a decision strategy generation model, using the query results as input, and generating a series of candidate decision strategy sets based on a machine learning algorithm; for each decision strategy in the candidate strategy set, evaluating its expected risk level, and selecting the decision strategy with the lowest risk as output; decomposing the decision strategy into two parts: a well construction parameter adjustment plan and a high-level decision instruction, and inputting the high-level decision instruction into a human-computer interaction module for manual review; if the manual feedback indicates that the decision is unreasonable, inputting a modification instruction through the natural language interaction interface, feeding the modification instruction back to the decision engine module, and regenerating the decision strategy; if the manual feedback indicates that the decision is reasonable, combining the well construction parameter adjustment plan with the real-time well construction parameters, and using the model predictive control algorithm to calculate an optimized parameter adjustment sequence and input it into the control execution module.

[0015] As a preferred solution of the intelligent well construction safety management and control system described in the present invention, the operation process of the control execution module is as follows: receiving the optimized parameter adjustment sequence from the decision engine module; parsing the parameter adjustment sequence, identifying the adjustment object and its adjustment target value; obtaining the current state data of the real-time well construction control system, including the state feedback of each actuator; comparing the parameter adjustment target value with the current mechanism state, and calculating the incremental adjustment amount of the control execution; matching the incremental adjustment amount with the underlying control command set of the well construction control system to generate the corresponding control instruction sequence; and transmitting the control instruction sequence to the control system through industrial Ethernet, fieldbus or other industrial communication protocols. The system sends the sequence of commands to the corresponding execution unit of the well construction control system in real time; it continuously receives execution feedback from the well construction control system to monitor whether the parameters have been adjusted as expected; if it is detected that the execution deviation exceeds the allowable range, the feedback data is passed to the decision engine module to determine whether the decision strategy needs to be regenerated; the execution process is presented on the human-computer interaction interface to support manual monitoring and intervention. If an abnormality is found manually, the correction instruction is input to the control execution module through the natural language interaction interface; after receiving the correction instruction, the control execution module regenerates and sends the control instruction sequence; if the parameter adjustment is completed and the execution result is normal, the final status feedback is passed to the decision engine module.

[0016] In a second aspect, an embodiment of the present invention provides an intelligent well construction safety management and control method, which includes collecting various raw data in the well construction process, preprocessing the raw data, and constructing a standardized time series data sample set; constructing a risk assessment model based on the Transformer encoder-decoder framework, receiving the time series data sample set, and evaluating the potential risks of well construction in real time; inputting the risk assessment results into the decision generation link and the human-computer interaction interface; sending a query request to the decision knowledge base, retrieving relevant data from the decision knowledge base according to the query request, and using the retrieval results for decision generation; based on the risk assessment results and combined with the query results of the decision knowledge base, constructing a decision strategy generation model, generating high-level decision instructions and parameter adjustment sequences; inputting the high-level decision instructions into the human-computer interaction interface, and inputting the parameter adjustment sequence into the control execution link; the visual interface of the human-computer interaction interface displays the risk assessment results, decision results and control execution status; the natural language interaction interface of the human-computer interaction interface supports manual intervention in decision-making, and feeds back the intervention instructions to the risk assessment, decision generation or control execution link; receiving the parameter adjustment sequence output by the decision generation link, converting it into a control instruction sequence, and sending it to the actual well construction control system for execution through the data communication interface.

[0017] In a third aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of the intelligent well construction safety management and control system as described in the first aspect of the present invention are implemented.

[0018] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of the intelligent well construction safety management and control system as described in the first aspect of the present invention are implemented.

[0019] The beneficial effects of the present invention are as follows: the present invention integrates multi-source heterogeneous data, assigns adaptive weights through the self-attention mechanism, improves data quality and feature importance recognition capabilities, and provides high-quality input data for the risk assessment model. Based on the Transformer encoder-decoder structure, the augmentation strategy and self-supervised comparative learning are introduced to improve the generalization ability and robustness of the risk assessment model, and realize the time series prediction of risk situation and risk level classification. Combining the knowledge base and machine learning algorithm, a variety of candidate decision strategies are generated, and the strategy with the lowest risk is optimized and selected, realizing the intelligent generation of decision-making and improving the effectiveness and accuracy of decision-making. Through the human-computer interaction interface, it supports manual real-time monitoring and intervention in the decision-making and control execution process, giving full play to the advantages of human-computer collaboration and improving the interpretability and security of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of 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.

[0021] Figure 1 This is the module connection diagram of the intelligent well construction safety management and control system.

[0022] Figure 2 This is the operation flow chart of the data collection and fusion module of the intelligent well construction safety management and control system.

[0023] Figure 3 Flowchart for training the risk assessment model for the intelligent well construction safety management and control system.

[0024] Figure 4 This is a risk assessment model framework diagram for the intelligent well construction safety management and control system.

[0025] Figure 5 Diagram of computer equipment for the intelligent well safety management and control system. DETAILED DESCRIPTION

[0026] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0027] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from the description. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0028] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0029] Example 1

[0030] Reference Figures 1 to 5 , which is the first embodiment of the present invention, provides an intelligent well construction safety management and control system, including: a data acquisition and fusion module, which is used to collect various raw data during the well construction process, pre-process the data, construct a standardized time series data sample set, and input the time series data sample set into a risk assessment module.

[0031] Preferably, the operation process of the data acquisition and fusion module is as follows: collect various raw data in the well construction process, including well construction data, geological data, well condition data, etc., to construct the original well construction process data set; select feature data related to well construction risk assessment from the original well construction process data set; use the self-attention mechanism to fuse the selected feature data, and assign adaptive weights to each feature according to its importance to risk assessment; perform missing value processing on the fused feature data, detect features with missing values, and use the historical data interpolation method of similar features to fill the missing values; perform feature encoding on the processed feature data set, perform One-Hot encoding on discrete features, and perform One-Hot encoding on continuous features. Normalized encoding is performed on continuous features; according to the time attributes of the features, the encoded feature data is divided into three categories: time series features, discrete features, and continuous features; sliding window segmentation is performed on the time series features, and the window size (time step) is set to B to obtain a time series feature sequence; the segmented time series feature sequence is spliced ​​with the discrete features and continuous features in the corresponding window to construct multivariate feature data; the spliced ​​multivariate feature data is formatted into a time series data sample set, including information such as the number of samples, time step B, time series feature dimension, discrete feature dimension, and continuous feature dimension; the formatted time series data sample set is input into the risk assessment module as input data for the risk assessment model.

[0032] The risk assessment module builds a risk assessment model based on the Transformer encoder-decoder framework, receives the time series data sample set output by the data acquisition and fusion module, evaluates the potential risks of well construction in real time, and inputs the risk situation sequence into the decision engine module and the human-computer interaction module.

[0033] Preferably, the operation process of the risk assessment module is as follows: receive the time series data sample set output by the data acquisition and fusion module, and divide the time series data sample set into a training set and a test set according to a preset ratio; augment the training set data including time series perturbation augmentation, feature mask augmentation and semantic preservation augmentation to obtain an augmented data set; design an encoder-decoder model framework based on Transformer as a risk assessment model; adopt the self-supervised contrastive learning strategy of the masked language model (MLM) to train the risk assessment model and optimize the parameters of the encoder and decoder at the same time; during the training process, periodically evaluate the current model performance on the test set, and if the performance meets the predefined conditions If the risk assessment model meets the pre-set evaluation criteria, training is stopped early; otherwise, training is continued until the maximum number of training rounds is reached; the generalization performance of the trained risk assessment model is comprehensively evaluated on the test set. If it meets the preset evaluation indicators, it is deployed; the deployed risk assessment model is used to conduct risk assessment on the real-time well construction data stream to obtain the risk situation sequence and risk level; the risk situation sequence is input into the human-computer interaction module, and feedback instructions are received to determine whether the risk assessment model needs to be retrained; if necessary, new well construction process data is collected and retrained and evaluated; if not, the risk situation sequence and risk level are input into the decision engine module; feedback data from the actual well construction process is recorded for continuous optimization of the risk assessment model.

[0034] Specifically, the risk assessment model framework consists of an encoder and decoder. The encoder consists of a time series encoder, a risk relationship modeler, and an attention fusion module. The decoder consists of a first decoding head and a second decoding head. The time series encoder, based on the Transformer encoder structure, takes as input the original training set and the augmented dataset, encodes the two input sequences, learns an adversarial time series representation, and outputs a time series feature representation. The risk relationship modeler, based on the Relational Graph Neural Network (RelGNN) structure, constructs a heterogeneous relationship graph between time series factors and risk factors, learns the spatiotemporal risk evolution pattern, and outputs a spatiotemporal risk pattern representation. The attention fusion module, based on a multi-head attention mechanism, concatenates the time series representation vector and the spatiotemporal risk pattern representation, calculates attention weights, fuses the local time series and global risk representations, and outputs a fused risk feature representation. The first decoding head is a risk situation generator, based on the Transformer decoder structure, takes as input the fused risk feature representation, generates a continuous risk situation sequence through autoregression, and outputs a predicted risk situation sequence. The second decoding head is a risk classifier, based on a fully connected neural network structure, takes as input the fused risk representation tensor, and outputs the predicted probability of discrete risk state categories.

[0035] Specifically, training the risk assessment model includes the following steps: obtaining the original training set and the augmented dataset; sampling a batch of data X and X' from the original training data and the augmented dataset respectively; randomly masking parts of X and X' to obtain masked inputs X_masked and X'_masked; inputting X_masked and X'_masked into the encoder respectively, and the encoder outputs the corresponding time series feature representation and ; Calculate the contrast loss of the encoder output ;Will and Input the first decoding head and the second decoding head of the decoder respectively. The first decoding head (risk situation generator) outputs the predicted sequence Y_pred and Y'_pred. Calculate the generation loss of Y_pred and the original label Y The second decoding head (risk classifier) ​​outputs the classification probabilities P and P', and calculates the classification loss between P and the original label C. ; The contrast loss , generate losses , classification loss Perform weighted summation to get the total loss Based on total loss , calculate the gradient of the training parameters, and use the gradient descent algorithm to backpropagate and optimize the training parameters.

[0036] It should be noted that the contrast loss , generate losses , classification loss and total losses The specific formula is as follows:

[0037] ;

[0038] L\_ gen=-\frac {\sum{log(y\_ pred[t])(y\_ true[t])}} {B} ;

[0039] ;

[0040] ;

[0041] in, and Represent the output representation of the encoder for the original input X and the augmented input X', respectively. for The transpose of is the L2 norm, is the original risk situation sequence label, is the predicted sequence probability distribution output by the decoder, The index iterates over each time step of the sequence, represents the sequence time step (window size), One-Hot label for the original risk level, is the classification probability distribution output by the decoder, and is an adjustable weight coefficient (used to balance the relative importance of the three losses), is a scalar, express and is a positive sample pair (original sample and augmented sample), represents a negative sample pair.

[0042] The decision-making knowledge base module is used to build a decision-making knowledge base, receive query requests from the decision engine module, retrieve relevant data from the knowledge base based on the query requests (a subset of well construction operation parameter data, the operation process template of the current stage, historical cases similar to the query risk, and the safety constraints of the current formation environment), and input the retrieval results into the decision engine module.

[0043] Specifically, parameter data recorded in historical well construction operations are collected to construct a well construction operation parameter data set, which covers the historical values ​​of various well construction parameters, such as drilling speed, torque, pump pressure, etc.; operation process templates are extracted from well construction operation specification documents to build an operation process template library. The operation process templates describe the standard operating steps and parameter ranges for each stage of well construction; historical well construction risk events and their handling solutions are summarized and analyzed to build a risk case library. The risk case library provides reference and reference for new risk scenarios; a safety constraint condition library is established, including safety constraint conditions under various formation environments, such as wellbore stability, pressure window, etc.; new decision cases output by the decision engine are received and added to the risk case library to continuously enrich the knowledge base.

[0044] The decision engine module is used to build a decision strategy generation model based on the risk situation sequence and risk level output by the risk assessment module and the decision knowledge base, generate high-level decision instructions and parameter adjustment sequences, input the high-level decision instructions into the human-computer interaction module, and input the parameter adjustment sequence into the control execution module.

[0045] Preferably, the operation process of the decision engine module is as follows: receiving the risk situation sequence and risk level output by the risk assessment module; sending a query request to the decision knowledge base module, the query conditions including the risk situation sequence, risk level and current well construction stage, receiving the query results returned by the decision knowledge base module, including the well construction operation parameter data subset, the operation process template of the current stage, historical cases similar to the query risk, and the safety constraints of the current formation environment; constructing a decision strategy generation model, taking the parameter data subset, the operation process template, the historical risk case and the safety constraints as input, and generating a series of candidate decision strategy sets based on the machine learning algorithm; evaluating the prediction of each decision strategy in the candidate strategy set The risk level in the long term is determined, and the decision strategy with the lowest risk is selected as the output; the decision strategy is decomposed into two parts: the well construction parameter adjustment plan and the high-level decision instructions (parameter adjustment / correction / pause, etc.), and the high-level decision instructions are input into the human-computer interaction module for manual review; if the manual feedback decision is unreasonable, the modification instructions are input through the natural language interaction interface, and the modification instructions are fed back to the decision engine module to regenerate the decision strategy; if the manual feedback decision is reasonable, the well construction parameter adjustment plan is combined with the real-time well construction parameters, and the model predictive control algorithm is used to calculate the optimized parameter adjustment sequence, which is input into the control execution module. At the same time, the decision case is input into the decision knowledge base module for knowledge base update.

[0046] The control execution module is used to receive the parameter adjustment sequence output by the decision engine module, convert it into a corresponding control instruction sequence, and send it to the actual well construction control system for execution through the data communication interface.

[0047] Preferably, the operation process of the control execution module is as follows: receiving the optimized parameter adjustment sequence from the decision engine module; parsing the parameter adjustment sequence, identifying the adjustment object (such as well construction fluid performance parameters, drill pipe speed, etc.) and its adjustment target value; obtaining the current state data of the real-time well construction control system, including the state feedback of each actuator; comparing the parameter adjustment target value with the current mechanism state, and calculating the incremental adjustment amount of the control execution; matching the incremental adjustment amount with the underlying control command set of the well construction control system to generate the corresponding control instruction sequence; sending the control instruction sequence to the well construction control system in real time through industrial Ethernet, field bus or other industrial communication protocols corresponding execution unit; continuously receive execution feedback from the well construction control system to monitor whether the parameters have been adjusted as expected; if it is detected that the execution deviation exceeds the allowable range, the feedback data will be passed to the decision engine module to determine whether the decision strategy needs to be regenerated; at the same time, the execution process is presented on the human-computer interaction interface to support manual monitoring and intervention. If an abnormality is found manually, the correction instruction is input through the natural language interaction interface. After receiving the correction instruction, the control execution module regenerates and issues a new control instruction sequence to overwrite the previous control action; once the parameter adjustment is completed and the execution result is normal, the final status feedback is passed to the decision engine module to provide feedback for subsequent decisions.

[0048] The human-computer interaction module includes a visual interface and a natural language interaction interface. The visual interface is used to display risk assessment results, decision results and control execution status. The natural language interaction interface supports manual intervention in decision-making and feeds back manual intervention instructions to the risk assessment module, decision engine module or optimization control module.

[0049] It should be noted that manual intervention includes feedback instructions, modification instructions, and correction instructions.

[0050] Furthermore, this embodiment also provides an intelligent well construction safety management and control method, including the present invention's fusion of multi-source heterogeneous data, assigning adaptive weights through the self-attention mechanism, improving data quality and feature importance recognition capabilities, and providing high-quality input data for the risk assessment model. Based on the Transformer encoder-decoder structure, the augmentation strategy and self-supervised comparative learning are introduced to improve the generalization ability and robustness of the risk assessment model, and realize the time series prediction of risk situation and risk level classification. Combining the knowledge base and machine learning algorithm, a variety of candidate decision strategies are generated, and the strategy with the lowest risk is optimized and selected, realizing the intelligence of decision generation and improving the effectiveness and accuracy of decision making. Through the human-computer interaction interface, it supports real-time manual monitoring and intervention in the decision-making and control execution process, giving full play to the advantages of human-computer collaboration and improving the interpretability and safety of the system.

[0051] This embodiment also provides a computer device suitable for an intelligent well construction safety management and control system, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the intelligent well construction safety management and control system proposed in the above embodiment.

[0052] The computer device may be a terminal, comprising a processor, memory, a communication interface, a display, and an input device connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores an operating system and computer programs. The internal memory provides an environment for the operating system and computer programs stored in the non-volatile storage media. The communication interface of the computer device is used to communicate with external terminals via wired or wireless communication. Wireless communication may be achieved via Wi-Fi, a carrier network, NFC (near-field communication), or other technologies. The display of the computer device may be a liquid crystal display or an electronic ink display. The input device may be a touchscreen overlay on the display, buttons, a trackball, or a touchpad on the computer device housing, or an external keyboard, touchpad, or mouse.

[0053] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the intelligent well construction safety management and control system proposed in the above embodiment is implemented.

[0054] In summary, the present invention integrates multi-source heterogeneous data, assigns adaptive weights through the self-attention mechanism, improves data quality and feature importance recognition capabilities, and provides high-quality input data for the risk assessment model. Based on the Transformer encoder-decoder structure, the augmentation strategy and self-supervised comparative learning are introduced to improve the generalization ability and robustness of the risk assessment model, and realize the time series prediction of risk situation and risk level classification. Combining the knowledge base and machine learning algorithm, a variety of candidate decision strategies are generated, and the strategy with the lowest risk is optimized and selected, realizing the intelligent generation of decision-making and improving the effectiveness and accuracy of decision-making. Through the human-computer interaction interface, it supports real-time manual monitoring and intervention in the decision-making and control execution process, giving full play to the advantages of human-computer collaboration and improving the interpretability and security of the system.

[0055] Example 2

[0056] Reference Figures 1 to 5 This is the second embodiment of the present invention, which provides an intelligent well construction safety management and control system. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0057] From the actual operational database of 160 wells drilled in the Fuling shale gas field in the Sichuan Basin, 80,000 raw records were extracted through the wellsite monitoring data acquisition system. These records include well construction parameter data, inclination measurement data, and drilling fluid performance data. These records cover a variety of operating conditions, including normal well construction, abnormal events, and various risk scenarios. These raw data are directly recorded by the data acquisition system, ensuring high authenticity and integrity.

[0058] Furthermore, the data acquisition and fusion module extracted 32 features highly correlated with well construction risk from the raw data, such as drilling fluid density, pump pressure, torque, and well inclination. A self-attention mechanism was used to assign adaptive weights to each feature, and missing data was interpolated. Ultimately, a normalized time series dataset containing 65,000 samples with a 5-minute time step was constructed. This dataset was divided into a training set (52,000 samples) and a test set (13,000 samples) in an 8:2 ratio.

[0059] Furthermore, three types of data augmentation (temporal perturbation augmentation, feature mask augmentation, and semantic preserving augmentation) were performed on the training set, expanding the training set to 156,000 augmented samples. The risk assessment model underwent six rounds of iterative training on this large dataset, with a total training cycle of approximately 76 hours. During training, the contrast loss decreased from an initial 2.7 to 0.68, the generation loss decreased from 6.1 to 1.9, and the classification loss decreased from 2.5 to 0.32. On the test set, model evaluation metrics showed: an absolute mean error of 0.072 for risk situation scoring, and an F1 score of 95.8% for risk classification, demonstrating good generalization performance.

[0060] After deploying the risk assessment model, eight real-world well construction risk cases were randomly sampled from the test dataset, including abnormal fracturing pressure, well drift, and well fluid contamination. The risk assessment model was able to issue effective early warnings for these risk events an average of 1.8 hours in advance, with a 90% accuracy rate in risk assessment. In one case of abnormal fracturing pressure in a well, the model issued a four-hour advance warning of a Level III fracturing risk in that section of the well, with a 91% confidence score. Based on this warning, the on-duty well construction consultant promptly implemented emergency measures, such as reducing the drilling rate and increasing drilling density, successfully averting a fracturing accident.

[0061] Furthermore, when another well encountered drift risk during well construction, the decision engine module, after receiving the risk level (Level IV) and risk situation prediction data from the risk assessment module, retrieved parameter statistics for the current well section, standard operating procedure templates, three similar cases, and safety window constraints from the knowledge base. A machine learning algorithm generated two candidate strategies, with predicted risk scores of 0.41 and 0.58, respectively. The strategy with the lower score was ultimately selected, with recommendations for adjusting key parameters. This decision plan was approved and implemented by the on-site chief well engineer.

[0062] Furthermore, the control execution module converts the decision plan into a series of control instructions, which are distributed in real time to the drilling rig, mud tank, and other equipment via the well construction data network. Monitoring data from the control execution process is displayed in real time on the human-machine interface. After approximately 25 minutes, all parameters were adjusted as expected. According to the risk assessment model, the risk score had dropped from 0.83 to 0.32, falling within a manageable range. This decision-making experience and the entire data record were added to the knowledge base, providing new examples for subsequent model training.

[0063] Preferably, the comparison indicators of the present invention and the traditional method are shown in Table 1.

[0064] Table 1 Comparative indexes of the present invention and the traditional method

[0065] index The present invention Traditional methods Data fusion capabilities Using self-attention mechanism to fuse multi-source heterogeneous data Manual rule splicing Risk assessment accuracy 90% 65% Risk warning lead time (average value) 4 0.5 Decision-making optimization efficiency 61% reduction 35% reduction Human-machine collaboration capabilities Support natural language manual intervention and closed-loop optimization Human-machine incoordination and lack of optimization mechanism Generalization ability The risk situation score error is 0.072, and the F1 value is 95.8%. Poor performance on the test set and difficulty in generalization

[0066] Specifically, as can be seen from Table 1, the present invention is significantly superior to traditional manual decision-making and rule model methods in terms of risk assessment accuracy, risk warning lead time, decision optimization efficiency, etc., and has good application prospects.

[0067] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An intelligent well construction safety management and control system, characterized in that: include: The data acquisition and fusion module is used to collect various raw data during the well construction process, pre-process the raw data, construct a standardized time series data sample set, and input the time series data sample set into the risk assessment module; The risk assessment module builds a risk assessment model based on the Transformer encoder-decoder framework, receives the time series data sample set output by the data acquisition and fusion module, evaluates the potential risks of well construction in real time, and inputs the assessment results, including the risk situation sequence and risk level, into the decision engine module and the human-computer interaction module; The decision engine module is used to send query requests to the decision knowledge base module, build a decision strategy generation model based on the risk situation sequence and risk level output by the risk assessment module and the query results, generate high-level decision instructions and parameter adjustment sequences, input the high-level decision instructions to the human-computer interaction module, and input the parameter adjustment sequence to the control execution module; The control execution module is used to receive the parameter adjustment sequence output by the decision engine module, convert the parameter adjustment sequence into a corresponding control instruction sequence, and send the control instruction sequence to the actual well construction control system for execution through the data communication interface; The human-computer interaction module includes a visual interface and a natural language interaction interface. The visual interface is used to display risk assessment results, decision results, and control execution status. The natural language interaction interface supports manual intervention in decision-making and feeds back manual intervention instructions to the risk assessment module, decision engine module, or optimization control module. A decision knowledge base module is used to build a decision knowledge base, receive query requests from the decision engine module, retrieve relevant data from the knowledge base according to the query request, and input the retrieval results into the decision engine module; The operating procedures of the risk assessment module are as follows: Receive the time series data sample set output by the data acquisition and fusion module, and divide the time series data sample set into a training set and a test set according to a preset ratio; Augmenting the training set data to obtain an augmented data set, wherein the augmentation includes temporal perturbation augmentation, feature mask augmentation, and semantic preservation augmentation; Design a Transformer-based encoder-decoder model framework as a risk assessment model; A self-supervised contrastive learning strategy of a masked language model is used to train the risk assessment model and optimize the parameters of the encoder and decoder simultaneously. During the training process, the current model performance is periodically evaluated on the test set. If the performance meets the predetermined conditions, the training is stopped early. Otherwise, the training continues until the maximum number of training rounds is reached. Comprehensively evaluate the generalization performance of the trained risk assessment model on the test set. If it meets the preset evaluation indicators, it will be deployed; Using the deployed risk assessment model, we conduct risk assessment on real-time well construction data streams to obtain risk situation sequences and risk levels. Input the risk situation sequence into the human-computer interaction module, receive feedback instructions, and determine whether the risk assessment model needs to be retrained; If necessary, new well construction process data is collected and retrained and evaluated. If not, the risk situation sequence and risk level are input into the decision engine module.

2. The intelligent well construction safety management and control system according to claim 1, characterized in that: The operation process of the data acquisition and fusion module is as follows: Collect various raw data during the well construction process and construct the original well construction process data set; Select characteristic data related to well construction risk assessment from the original well construction process data set; A self-attention mechanism is used to fuse the selected feature data and assign adaptive weights to each feature based on its importance to risk assessment; Perform missing value processing on the fused feature data, detect features with missing values, and use historical data interpolation of similar features to fill in the missing values; Perform feature encoding on the processed feature data set, perform One-Hot encoding on discrete features, and perform normalized encoding on continuous features; According to the time attribute of the feature, the encoded feature data is divided into three categories: time series features, discrete features and continuous features; Perform sliding window segmentation on the time series features, set the window size, and obtain the time series feature sequence; The segmented time series feature sequence is concatenated with the discrete features and continuous features in the corresponding window to construct multivariate feature data; The spliced ​​multivariate feature data is formatted into a time series data sample set, and the formatted time series data sample set is input into the risk assessment module.

3. The intelligent well construction safety management and control system according to claim 1, characterized in that: The framework of the risk assessment model includes an encoder part and a decoder part, the encoder part includes a temporal encoder, a risk relationship modeler and an attention fusion module, and the decoder part includes a first decoding head and a second decoding head.

4. The intelligent well construction safety management and control system according to claim 1, characterized in that: The training of the risk assessment model comprises the following steps: Get the original training set and augmented dataset; Sample a batch of data X and X' from the original training data and augmented dataset respectively; Randomly mask parts of X and X' to get masked inputs X_masked and X'_masked; Input X_masked and X'_masked into the encoder respectively, and the encoder outputs the corresponding temporal feature representation and ; Compute the contrastive loss of the encoder output ; Will and Input the first decoding head and the second decoding head of the decoder respectively, The first decoding head outputs the predicted sequence Y_pred and Y'_pred, and calculates the generation loss of Y_pred and the original label Y ; The second decoding head outputs the classification probabilities P and P', and calculates the classification loss between P and the original label C ; The contrast loss , generate losses , classification loss Perform weighted summation to get the total loss ; Based on total loss , calculate the gradient of the training parameters and use the gradient descent algorithm to backpropagate and optimize the training parameters; The contrast loss The specific formula is as follows: ； in, and Represent the encoder's output representation of the original input X and the augmented input X', respectively. for The transpose of is the L2 norm, is a scalar.

5. The intelligent well construction safety management and control system according to claim 1, characterized in that: The operation process of the decision engine module is as follows: Receive the risk situation sequence and risk level output by the risk assessment module; Sending a query request to the decision knowledge base module, with query conditions including risk situation sequence, risk level, and current well construction stage, and receiving query results returned by the decision knowledge base module, including parameter data subsets, operation process templates, historical risk cases, and safety constraints; Build a decision strategy generation model, which uses query results as input and generates a series of candidate decision strategy sets based on machine learning algorithms; For each decision strategy in the candidate strategy set, evaluate its expected risk level and select the decision strategy with the lowest risk as the output; Decompose the decision-making strategy into two parts: well construction parameter adjustment plan and high-level decision instructions. The high-level decision instructions are then input into the human-computer interaction module for manual review. If the manual feedback decision is unreasonable, the modification instructions are input through the natural language interactive interface, and the modification instructions are fed back to the decision engine module to regenerate the decision strategy; If the manual feedback decision is reasonable, The well construction parameter adjustment plan is combined with the real-time well construction parameters, and the model predictive control algorithm is used to calculate the optimized parameter adjustment sequence and input it into the control execution module.

6. The intelligent well construction safety management and control system according to claim 1, characterized in that: The operation process of the control execution module is as follows: receiving an optimization parameter adjustment sequence output by a decision engine module; Parse the parameter adjustment sequence, identify the adjustment object and its adjustment target value; Obtain the current status data of the real-time well construction control system, including status feedback of each actuator; Compare the parameter adjustment target value with the current mechanism state and calculate the incremental adjustment amount for control execution; Match the incremental adjustment amount with the underlying control command set of the well construction control system to generate a corresponding control instruction sequence; Through industrial Ethernet, fieldbus or other industrial communication protocols, the control instruction sequence is sent to the corresponding execution unit of the well construction control system in real time; Continuously receive feedback from the well construction control system to monitor whether parameters are adjusted as expected; If it is detected that the execution deviation exceeds the allowable range, the feedback data will be passed to the decision engine module to determine whether the decision strategy needs to be regenerated; The execution process is presented on the human-computer interaction interface, supporting manual monitoring and intervention. If anomalies are found manually, correction instructions are input into the control execution module through the natural language interaction interface; The control execution module regenerates and issues a control instruction sequence after receiving the correction instruction; If the parameter adjustment is completed and the execution result is normal, the final status feedback will be passed to the decision engine module.

7. An intelligent well construction safety management and control method, based on the intelligent well construction safety management and control system according to any one of claims 1 to 6, characterized in that: Also includes, Collect various raw data during the well construction process, pre-process the raw data, and construct a standardized time series data sample set; A risk assessment model is built based on the Transformer encoder-decoder framework, which receives time series data sample sets and evaluates potential well construction risks in real time. Input risk assessment results into the decision-making process and human-computer interaction interface; Send a query request to the decision knowledge base, retrieve relevant data from the decision knowledge base according to the query request, and use the retrieval results for decision generation; Based on the risk assessment results and the query results of the decision knowledge base, a decision strategy generation model is constructed to generate high-level decision instructions and parameter adjustment sequences; Input high-level decision instructions into the human-computer interaction interface and input parameter adjustment sequences into the control execution link; The visual interface of the human-computer interaction interface displays risk assessment results, decision results and control execution status; The natural language interface of the human-computer interaction interface supports manual intervention decisions and feeds intervention instructions back to the risk assessment, decision generation or control execution links; Receive the parameter adjustment sequence output by the decision-making generation link, convert it into a control instruction sequence, and send it to the actual well construction control system for execution through the data communication interface.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the intelligent well construction safety management and control system according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the intelligent well construction safety management and control system according to any one of claims 1 to 6 are implemented.

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