Intelligent prediction method for yield increasing effect of fractured wellhead

Abstract

The invention discloses an intelligent prediction method for a fracturing wellhead yield increase effect, and the method comprises the steps: carrying out the fusion collection, standardization and embedded dimension reduction of multi-source real-time and historical data, environment and geological parameters and expert experience, cooperatively extracting high-dimensional features, evaluating the uncertainty of the features through a Bayesian model, and carrying out the weighted input of a reinforcement learning architecture, thereby achieving the intelligent prediction of the yield increase effect of a fracturing wellhead. According to the method, causal reasoning and deviation correction are carried out in combination with physical and business indexes, high consistency of a strategy and a multi-target business index is realized by dynamically adjusting a reward function, and self-adaptive retraining and robust optimization of a model in extreme environments such as high noise and data missing are realized through field evaluation regression analysis of output. According to the method, the adaptability and prediction accuracy of the fracturing yield increase prediction and optimization system to complex working conditions are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent prediction technology for oil and gas fracturing, and in particular to an intelligent prediction method for the production enhancement effect of fracturing wellheads. Background Technology

[0002] In the current oil and gas extraction field, especially in predicting the production enhancement effect of wellhead fracturing operations, it is a key link in improving the intelligent management level of oil and gas fields and the ability to optimize fracturing decisions. With the increasing difficulty of oil and gas reservoir development and the widespread exploitation of unconventional resources, traditional methods based on physical laws or empirical rules are gradually showing their inadequacy in complex, variable, and extreme environmental conditions. Therefore, in recent years, the industry has widely focused on introducing intelligent algorithms such as machine learning, especially reinforcement learning (RL), into important applications such as predicting and controlling the production enhancement effect of fracturing processes. However, in the complex scenario of intelligent prediction of fracturing production enhancement, existing methods still have the following prominent technical defects and limitations: (1) The model objective is inconsistent with the business production increase effect. At present, most of the reward functions of the reinforcement learning methods are designed based on the model's own indicators (such as loss function, single production volume, etc.), but fail to fully reflect the multi-objective requirements of the actual business site (such as output, stability, robustness under extreme working conditions, etc.), resulting in a significant deviation between the optimal learning strategy and the actual optimal business output. (2) Insufficient environmental modeling and robustness. Existing models often fail to dynamically perceive and integrate the uncertainty of multi-source data under extreme environments (high temperature, high pressure, large fluctuations, etc.), lack dynamic quantification and utilization of the validity and confidence of input information, and have limited robustness and adaptability. (3) The integration of multimodal information and expert knowledge is weak. Multimodal data features (such as geology, environment, historical working conditions, real-time monitoring, etc.) have not yet been efficiently integrated at the model input level. Expert experience and physical prior knowledge are difficult to effectively introduce or dynamically constrain model output. There is a lack of causal reasoning and physical consistency explanation mechanism. (4) Insufficient iteration and alignment of reward mechanism. Traditional fixed or simple weighted reward mechanism cannot reflect the dynamic deviation between model prediction and actual output changes. It lacks a multi-objective reward mechanism that dynamically and adaptively adjusts based on business objectives, making it difficult to automatically optimize model objectives for extreme working conditions. Summary of the Invention

[0003] In order to solve the above-mentioned technical problems, the present invention provides an intelligent prediction method for the production enhancement effect of fracturing wellheads.

[0004] The technical solution of this invention is implemented as follows: A method for intelligent prediction of the production enhancement effect of fracturing wellheads, comprising: S1: Based on real-time sensor points, historical working condition records, environmental monitoring results, expert scoring information, and geological prior parameters at the oil and gas wellhead fracturing site, collect and summarize multimodal raw data to comprehensively cover different working conditions and environmental differences. S2: Perform standardization and feature embedding processing on the collected multimodal raw data to remove the heterogeneity of data distribution caused by different locations, different working conditions, and different equipment types, to ensure the consistency of data features and form a unified input feature set; S3: The Bayesian uncertainty assessment method is used to quantify the confidence of different modal feature inputs and generate multidimensional uncertainty weight factors that include observation validity, data quality and environmental variability in real time for use in the subsequent reinforcement learning training process. S4: Input the weighted multidimensional features and corresponding uncertainty weight factors into the reinforcement learning inference architecture, initialize the robust reinforcement learning model, and perform adaptive policy generation and dynamic policy evaluation for extreme working conditions. S5: Based on an interpretable causal reasoning mechanism, the intermediate state outputs within the model are jointly aligned with physical mechanism parameters and expert business indicators. The prediction bias is compared and dynamically fed back to the reward function structure to achieve online bias correction. S6: For multi-objective business indicators, the reward distribution structure is dynamically adjusted according to the deviation between the reward function and the actual production increase effect of fracturing wellhead. Piecewise linear or nonlinear adaptive weighting strategy is used to optimize the objective function and improve the consistency between model output and business objectives. S7: The prediction strategy output by the reinforcement learning model with dynamic reward adjustment is compared and regressed with the actual wellhead production and cycle stability and other evaluation indicators to test the robustness of the model under high noise, missing data and extreme working conditions. S8: Based on actual business evaluation results, retrain and iteratively optimize the parameters of the reinforcement learning model to continuously improve the model's adaptability in extreme environments and abnormal data distributions, thereby achieving long-term dynamic adaptation and robustness improvement in business scenarios.

[0005] This application provides an intelligent prediction method for the production enhancement effect of fracturing wellheads, which has the following beneficial effects: (1) This invention achieves high integrity and spatiotemporal consistency of data from multiple sources, including real-time sensing at fracturing sites, historical operating conditions, environmental monitoring, expert knowledge, and geological priors, through intensive collection and efficient normalization and fusion. Compared with previous single data source or simple feature engineering methods, this invention significantly reduces decision-making errors caused by missing or abnormal information by intensive collection and efficient normalization and fusion of multi-source heterogeneous data such as real-time sensing at fracturing sites, historical operating conditions, environmental monitoring, expert knowledge, and geological priors. The Bayesian uncertainty assessment method is used to dynamically quantify the validity, quality, and environmental variability of data from each channel. Furthermore, the confidence weighting is used to improve the model's resistance to interference from high noise, abnormal data, or extreme environmental inputs. (2) This invention relies on an innovative interpretable causal reasoning module to coordinate and map the intermediate state of the model with physical mechanism parameters and expert business indicators in real time, which significantly improves the problem that existing models only focus on the single numerical optimum and ignore the synchronous optimization of the actual output of multiple business objectives. This mechanism supports online dynamic feedback of policy-business objective deviation, drives the reward function to adaptively segment or weighted reconstruct, and ensures that each round of policy update is carried out under the physical, economic and safety boundaries that fully reflect the on-site needs, which significantly improves the business relevance of the model output; (3) This invention introduces dynamic segmented adjustment under extreme environments and multi-objective nonlinear weighting mode into the reward function structure, which can be compatible with composite business objectives such as fracturing output, project stability, and tolerance for extreme deviations. During the strategy iteration process, the performance degradation caused by local optima trapping or imbalance of weights under abnormal working conditions is avoided by adjusting the reward, so that the overall prediction efficiency and business output are continuously aligned. (4) This invention uses a closed-loop model retraining and parameter iteration mechanism, combined with real field production and business feedback results, to continuously optimize the model structure and parameter configuration, effectively overcoming the challenges of dynamic changes in the oil and gas field environment and non-stationary data distribution, thereby ensuring that the prediction system can operate reliably for a long time under various new scenarios, extreme working conditions and abnormal data input. Attached Figure Description

[0006] Figure 1 This is a flowchart of an intelligent prediction method for the production enhancement effect of fracturing wellheads according to the present invention; Figure 2 This is a sub-flowchart of a method for intelligent prediction of the production enhancement effect of fracturing wellheads according to the present invention; Figure 3 This is another sub-flowchart of the intelligent prediction method for the production enhancement effect of fracturing wellheads according to the present invention. Detailed Implementation

[0007] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0008] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0009] When used herein, the singular forms of “a,” “an,” and “the” may also include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms “comprising / including” or “having,” etc., specify the presence of the stated features, wholes, steps, operations, components, parts, or combinations thereof, but do not preclude the possibility of the presence or addition of one or more other features, wholes, steps, operations, components, parts, or combinations thereof. Meanwhile, the term “and / or” as used in this specification includes any and all combinations of the associated listed items.

[0010] Please see Figures 1-3 As shown, an intelligent prediction method for the production enhancement effect of fracturing wellheads includes: S1: Based on real-time sensor points, historical working condition records, environmental monitoring results, expert scoring information, and geological prior parameters at the oil and gas wellhead fracturing site, collect and summarize multimodal raw data to comprehensively cover different working conditions and environmental differences. S2: Perform standardization and feature embedding processing on the collected multimodal raw data to remove the heterogeneity of data distribution caused by different locations, different working conditions, and different equipment types, to ensure the consistency of data features and form a unified input feature set; S3: The Bayesian uncertainty assessment method is used to quantify the confidence of different modal feature inputs and generate multidimensional uncertainty weight factors that include observation validity, data quality and environmental variability in real time for use in the subsequent reinforcement learning training process. S4: Input the weighted multidimensional features and corresponding uncertainty weight factors into the reinforcement learning inference architecture, initialize the robust reinforcement learning model, and perform adaptive policy generation and dynamic policy evaluation for extreme working conditions. S5: Based on an interpretable causal reasoning mechanism, the intermediate state outputs within the model are jointly aligned with physical mechanism parameters and expert business indicators. The prediction bias is compared and dynamically fed back to the reward function structure to achieve online bias correction. S6: For multi-objective business indicators, the reward distribution structure is dynamically adjusted according to the deviation between the reward function and the actual production increase effect of fracturing wellhead. Piecewise linear or nonlinear adaptive weighting strategy is used to optimize the objective function and improve the consistency between model output and business objectives. S7: The prediction strategy output by the reinforcement learning model with dynamic reward adjustment is compared and regressed with the actual wellhead production and cycle stability and other evaluation indicators to test the robustness of the model under high noise, missing data and extreme working conditions. S8: Based on actual business evaluation results, retrain and iteratively optimize the parameters of the reinforcement learning model to continuously improve the model's adaptability in extreme environments and abnormal data distributions, thereby achieving long-term dynamic adaptation and robustness improvement in business scenarios.

[0011] Step S1: Based on real-time sensor data, historical operating condition records, environmental monitoring results, expert scoring information, and prior geological parameters at the oil and gas wellhead fracturing site, collect and summarize multimodal raw data to comprehensively cover different operating conditions and environmental variations. Specifically, this includes: S1.1: Acquire fracturing parameter data (such as pressure, flow rate, temperature, and fracture length) collected from real-time sensing points at the wellhead. Use a field data acquisition module to perform second-level sampling, and simultaneously mark the data acquisition timestamp and wellhead geographical location to obtain a real-time sensing data stream with high spatiotemporal accuracy. For real-time sensing points at the wellhead of the fracturing site, the input conditions include hardware facilities such as pressure sensors, flow meters, temperature sensors, and fracture growth monitors deployed at each fracturing wellhead, to collect physical parameters such as pressure, flow rate, temperature, and fracture length in real time, and the raw signals are input through the field acquisition module; A high-precision field data acquisition module (parameters: sampling frequency of 1-10Hz, preferably 2Hz, acquisition delay of no more than 50ms, preferably 20ms) is adopted to achieve second-level multi-channel synchronous sampling of data from all real-time sensing points; Furthermore, a standardized real-time timestamp is added to each piece of collected data using an automatic timestamp annotation algorithm (parameters: GPS time synchronization, error <100ms) to ensure that various fracturing parameter data have traceable physical time sequence characteristics; The wellhead geolocation coding module (parameters: longitude, latitude, wellhead number) is used to achieve a one-to-one correspondence between the collected data and the actual wellhead spatial information. Geographical tags are associated with main parameters such as pressure, flow rate, and temperature, which facilitates subsequent data source partitioning and spatial analysis. The data integrity check algorithm (parameters: packet loss rate threshold ≤ 0.1%, outlier detection strategy) identifies and marks missing values, abnormal fluctuation points, and invalid collection intervals to assist in subsequent data quality control and abnormal operating condition identification. A high spatiotemporal resolution data stream integration algorithm (parameters: frame length 1 second, observation window 30 seconds) is used to perform time-series alignment and multi-parameter fusion of all collected data to generate a multi-dimensional high-precision real-time sensing data stream. Through the above processing method, the original collected real-time physical parameters such as pressure, flow rate, temperature, and fracture length are output as structured, high-confidence real-time sensor data streams after spatiotemporal tagging and integrity detection, so as to realize high spatiotemporal resolution real-time monitoring of various working conditions at the fracturing site. For example, in a real-world application scenario, an oil and gas well is equipped with six pressure sensors, four flow meters, two temperature sensors, and one fracture length monitor. The sampling frequency is uniformly set to 2Hz, with an average acquisition delay of no more than 20ms. A GPS time correction module adds a high-precision timestamp to each data point, and a geocoding module records the precise latitude and longitude of the wellhead (e.g., 34.12345°N, 117.54321°E) and the wellhead number A019. After data integrity checks, the packet loss rate is controlled at 0.05%, and an outlier detection algorithm identifies temperature abrupt change intervals. A data stream integration algorithm selects a 30-second observation window and outputs structured multidimensional time-series data, including all sensor values, timestamps, and geographic label information. Ultimately, this step produces a high-spatiotemporal-precision real-time sensor data stream, which exhibits 95% data integrity and spatial correlation when used for subsequent feature embedding and uncertainty assessment, significantly improving the overall fracturing operation data acquisition quality and the reliability of on-site decision-making. S1.2: Based on the production management system, obtain historical records of oil and gas well fracturing operations over the years, and extract key operating parameters (such as fracturing fluid usage, construction pressure curve, stage production changes, etc.) in a structured manner through the data archiving standardization interface, so as to realize the full-process association and organization of historical operating data of multiple cycles at the same wellhead. Based on the historical database of the oil and gas field production management system, the input conditions include records of oil and gas well fracturing operations over the years, covering various structured and semi-structured data types such as fracturing fluid usage, construction pressure curves, and stage production changes. A standardized data archiving interface (parameters: supports CSV, JSON, XML, etc. format recognition, time accuracy ≤1s) is adopted to achieve automatic parsing and field mapping of multi-format working condition files. Furthermore, a key field extraction algorithm (parameters: based on regular expressions and pattern matching rule sets, matching accuracy ≥98%) is used to structurally extract core parameters such as total fracturing fluid usage, staged construction pressure sequence, and single-cycle production variation range, and uniformly convert them into standard units (e.g., volume unit m³, pressure unit MPa, production unit t or m³). Furthermore, a time series normalization alignment algorithm (parameters: time base is UTC standard time, interpolation window 5 min) is used to precisely align data from different sources of operating conditions on the time axis, ensuring that the historical records from multiple sources for the same wellhead have comparable temporal characteristics. Further, a wellhead identifier association mapping method (parameters: wellhead number consistency threshold matching ≥99.5%) is applied to establish a logical index relationship between each wellhead and operating conditions in different operating cycles over the years, forming a wellhead attribution table for multi-cycle historical data. Furthermore, a multi-cycle data full-process correlation and processing algorithm (parameters: data integrity threshold ≥95%, missing intervals recorded as logs) is used to perform vertical (time axis) and horizontal (parameter axis) full correlation processing on data from the same wellhead in different years and different construction stages, generating a standardized multi-dimensional historical operating condition data matrix. A historical data quality detection algorithm (parameters: outlier judgment based on the 3σ principle, continuous missing data judgment window of 12 hours) is used to identify and label abnormal operating condition intervals and data missing intervals in the historical records. Through the above processing method, the original scattered and heterogeneous historical fracturing operation data are transformed into a structured, time-series unified, and fully associated multi-cycle historical operating condition dataset with wellhead identification, which forms the basis for cross-modal fusion of subsequent environmental monitoring data, real-time sensor data and expert scoring data. For example, in a certain oil and gas field, the production management system stores fracturing operation records for 68 wells from 2007 to 2022. Of these, 64 wells have a data integrity rate of ≥90%, while some wells only have records from specific years. The historical operating data files include CSV, PDF, Excel, and some JSON formats. The interface module automatically parses and extracts key fields such as fracturing fluid usage (range 1200-3100 m³), ​​operating pressure curves (sampling interval 30s, range 12-68 MPa), and stage production (single cycle 10-120 t). Time series normalization is based on UTC time, and linear interpolation is used to repair missing values ​​with sampling intervals less than 10 minutes. During wellhead number mapping, the system compares the wellhead ID in the management system with the wellhead number in the operating instructions, achieving a matching success rate of 99.7%. After multi-cycle correlation and processing, the fracturing data for a certain wellhead in 2008, 2011, and 2018 are unified into a single sequence, forming a historical operating data matrix containing 72 multi-dimensional parameters. Data quality checks indicated that the pressure curve segment during July 2011 exceeded [a certain limit]. The abnormal peak values ​​and the missing data records for 8 consecutive hours provide a key reference for the subsequent model uncertainty weight allocation; S1.3: The environmental monitoring subsystem is used to synchronously collect local environmental meteorological data (such as temperature, humidity, wind speed, and atmospheric pressure) and geological stability parameters (such as ground stress and fault zone monitoring data) at the well site to obtain environmental monitoring data. The environmental sensing network is used to perform multi-dimensional matching and archiving of time-series environmental status and operating condition data. S1.4: Through expert review and on-site scoring system, the process score, abnormal event evaluation and operation process label of each fracturing operation are entered in an orderly manner. Combined with expert experience knowledge graph, multi-source expert scoring information is integrated and mapped with fracturing parameter data. Based on the input conditions of the fracturing operation expert review and field scoring system, including the process flow document, abnormal event record, expert field scoring sheet and operation process label file for each fracturing operation, the system realizes the standardized collection and mapping of multi-source expert scoring information by associating it with real-time fracturing parameter data and historical operating condition data. An expert scoring data parsing algorithm (parameters: supports XLS, CSV, JSON and other formats, field matching accuracy ≥98%) is adopted to automatically parse and structure the fields of each fracturing operation process score, abnormal event evaluation and process label content, and convert textual and semi-structured records into a standardized scoring index set; Furthermore, through an abnormal event classification coding method (parameters: based on the industry standard SY / T 5107 classification system, event category code length ≤ 6 bits), a unified coding of abnormal events is achieved, forming a structured abnormal event category matrix, providing standard event labels for subsequent accurate mapping with wellhead operating data; Furthermore, through the expert experience knowledge graph matching algorithm (parameters: node similarity threshold ≥ 0.85, relationship confidence threshold ≥ 0.90), the parsed scoring indicators are semantically matched with the experience rule nodes in the knowledge graph to generate the experience node association vector corresponding to each operation, thus preserving the implicit evaluation information of experts on the process, environment and working conditions. Furthermore, a multi-source scoring data fusion algorithm (parameters: weighting coefficient based on expert qualification weight ω∈[0,1], normalization error threshold ≤5%) is adopted to weight and average the indicators of the same task scored by multiple experts, generate a comprehensive scoring vector, and save the sub-item scores and weighted scores for subsequent analysis; Furthermore, using fracturing parameter data mapping functions Compare the comprehensive score vector with the corresponding task's pressure curve Flow curve and abnormal event matrix Perform a one-to-one mapping, and define the mapping function formula as follows: in This is the comprehensive scoring vector. The abnormal event matrix maps to a set of matching physical quantity sequences for the operating conditions, enabling a two-way association between scoring information and operating condition parameters. The multidimensional mapping table generation module unifies the scoring vector, abnormal event labels, and fracturing parameter time series data into an expert scoring-operating condition mapping matrix. ,in This refers to the number of fracturing operations. This represents the total number of dimensions for the scoring and operating condition parameters. Through the above algorithms and processing methods, the scoring information scattered across different recording carriers and multiple experts is transformed into a structured, multimodal, consistent scoring dataset that is deeply associated with fracturing operation parameters, thereby achieving a highly consistent fusion and expression of expert knowledge and physical condition data. For example, expert scoring records for 120 fracturing operations were collected in an oilfield, including 312 Class A anomalies, 124 Class B anomalies, and 54 Class C anomalies. The scoring data parsing algorithm automatically parses the CSV and XLS format scoring tables, achieving a field matching accuracy of 99.2%. Anomalies are uniformly coded with 6-digit codes such as "B02014". The expert experience knowledge graph contains 480 nodes and 5000 experience relationships, with a matching threshold set to a similarity ≥ 0.85, and an average of 12 experience nodes associated per operation. The multi-source scoring fusion weighting coefficients are set as follows: 0.4 for experts with senior professional titles, 0.35 for intermediate titles, and 0.25 for junior titles, with a normalization error of 3.2%. The mapping coverage of the comprehensive scoring vector with the pressure curve P(t) and flow curve Q(t) reaches 100%, with an average data alignment error of less than 0.5 seconds. The final output mapping matrix R_{120×85} contains the weighted score, event label and multi-dimensional working condition parameters for each task, with a data integrity rate of over 98%, providing high-quality expert knowledge physical coupling input for uncertainty modeling and robustness optimization of the reinforcement learning model; S1.5: Based on the well site geological exploration database, extract the geological prior parameters of the fracturing area (such as lithology distribution, burial depth, porosity, and formation pressure coefficient), and encode them using the standard geological label format to ensure that the multimodal characteristics of geological priors and operational parameters can be fused. S1.6: Perform unified data preprocessing (such as time window alignment, redundancy removal, and outlier marking) on ​​the real-time sensor data stream, working condition history records, environmental monitoring data, expert scoring information, and geological prior parameters obtained above. Perform multi-source normalization and summarization on the original data of each modality to provide a complete basic dataset for subsequent feature embedding and uncertainty modeling.

[0012] Step S2: Standardize and embed the collected multimodal raw data to remove data distribution heterogeneity caused by different locations, operating conditions, and equipment types, ensuring data feature consistency and forming a unified input feature set. Specifically, this includes: S2.1: Perform data integrity verification and missing data completion processing on multimodal raw data (including real-time sensor data, historical working condition records, environmental monitoring data, expert scores and geological prior parameters), and use data interpolation, regression prediction or missing value repair algorithms based on multimodal correlation to obtain a continuous and complete initial feature matrix, providing a comprehensive data foundation for subsequent standardization processing; For the collected multimodal raw data (including real-time sensor data, historical working condition records, environmental monitoring data, expert scores and geological prior parameters), a data integrity verification algorithm (parameters: missing rate detection threshold ≤5%, consecutive missing window ≥10 sampling points) is used to check the consistency of each modal data file in the dimensions of time series, index series and geographic labels, and to mark records with abnormal sampling intervals, missing fields or misaligned indexes. Furthermore, by using a multimodal data synchronization detection method (parameter: cross-modal time alignment error threshold ≤200ms), the synchronization deviation of data from different sources on a unified time axis is identified, and a synchronization deviation vector is generated, providing a time positioning basis for subsequent missing data completion methods; Furthermore, for the detected missing data intervals, a time series-based spline interpolation algorithm (parameters: cubic spline function, boundary condition: natural boundary) is used to smoothly reconstruct continuous missing values, maintaining the temporal continuity and physical smoothness of the data; Furthermore, for high-dimensional numerical feature loss, a multivariate regression prediction algorithm (parameters: ridge regression, regularization coefficient λ=0.1) is applied to establish a regression equation based on the observed features within the same mode: in To predict missing values, For the relevant feature dimension values, The regression coefficients are estimated by minimizing the sum of squared residuals with the L2 norm. Furthermore, for the missing cross-modal highly correlated features, a null value repair algorithm based on the multimodal correlation matrix (parameter: correlation coefficient threshold ≥ 0.85) is adopted to jointly reconstruct the data by utilizing the complementary correlation of different modal features, thereby generating an estimate with high physical consistency. The quality verification method after data reconstruction (parameter: reconstruction error RMSE threshold ≤ 0.05) is used to evaluate the error between the interpolated value and the predicted value, remove the repair records that exceed the threshold and trigger the manual review mark. Through unified formatting, the completed and verified modal data are integrated into an initial feature matrix with consistent time index, complete spatial labels, and unified field definitions, thus providing a continuous, complete, and high-confidence data foundation for subsequent standardization and feature embedding processing. For example, in the data processing of a certain oil and gas well fracturing operation, the sampling frequency of real-time sensor data is 2Hz, the time accuracy of historical operating condition data is at the second level, the sampling interval of environmental monitoring data is 5 minutes, the expert scoring data is a discrete event record, and the geological prior parameters are static labels. Data integrity verification revealed that the real-time pressure curve had 15 consecutive missing sampling points during a certain operating condition (missing rate 7.5%), and the environmental temperature and humidity records had a sampling time misalignment of 180ms. The deviation vector generated by the synchronization detection marked the misalignment intervals, and cubic spline interpolation was used to reconstruct the pressure curve gaps. After reconstruction, the first derivative of the curve was continuous with adjacent segments. For the missing temperature feature value, a ridge regression model was applied, inputting five related features (correlation coefficients ≥ 0.9) within the same time period, to predict the missing temperature value, with an RMSE of 0.032, below the 0.05 threshold. For the missing geological porosity parameter, cross-modal repair was performed using the correlation matrix between the lithological distribution mode and the formation pressure mode (maximum correlation coefficient 0.88), generating an estimated value which was then confirmed by experts. The final output initial feature matrix contains 72 feature dimensions, with a time alignment error of ≤100ms and data integrity improved from 91.5% to 98.9%, providing high-quality input for subsequent S2.2 standardization processing; S2.2: Based on the initial feature matrix obtained in S2.1, normalization, standardization, and minimum-maximum scaling normalization algorithms are used for different data modalities to reshape the numerical distribution of each feature dimension in order to eliminate scale inconsistencies caused by differences in data sources, physical quantity dimensions, and extreme operating condition fluctuations, and obtain a standardized feature matrix as the input for feature embedding. S2.3: Perform feature dimensionality reduction and embedding algorithms such as principal component analysis (PCA), linear discriminant analysis (LDA), T-SNE or deep embedding network on the standardized feature matrix to extract low-dimensional embedding features that are related to fracturing production, realize the structural unification of high-dimensional heterogeneous features and the effective expression of key features, and output multimodal embedding feature vectors. S2.4: The multimodal embedding feature vectors obtained in S2.3 are combined and windowed according to a unified data structure. Techniques such as time series sliding window and working condition segmentation clustering are used to map the multi-source heterogeneous features to a standard input feature set, forming a highly consistent feature input set for reinforcement learning inference, providing a standard input for subsequent Bayesian uncertainty assessment and model training. S2.5: Re-evaluate the heterogeneous feature correlation and identify redundant features in the standard input feature set. Based on mutual information analysis and correlation coefficient threshold screening methods, remove redundant or noisy features and retain only the key features that have physical or business interpretability for fracturing production increase prediction. Finally, a standardized feature set with unified structure, optimized dimensions and high correlation is obtained, which serves as the basis for subsequent model uncertainty weighting and robust reinforcement learning strategies.

[0013] Step S3: A Bayesian uncertainty assessment method is used to quantify the confidence level of different modal feature inputs, generating in real-time a multi-dimensional uncertainty weight factor that includes observation validity, data quality, and environmental variability, for use in the subsequent reinforcement learning training process. Specifically, this includes: S3.1: Classify and organize various modal data (including real-time sensing features, historical working condition features, environmental monitoring features, expert scoring features, and geological prior features) in the unified input feature set. Based on the feature type and its collection source, label the corresponding data collection working conditions, equipment operating status and expected physical distribution to construct a feature attribution label set for subsequent uncertainty assessment modeling. S3.2: For each feature dimension in the feature attribution label set, apply the Bayesian inference algorithm, combine the prior distribution of the acquisition conditions and the real-time acquisition observations, perform confidence interval calculation on each modal feature, and obtain the Bayesian confidence parameter representing the validity of the observation to measure the confidence level of the feature component. For each feature dimension in the feature attribution label set, a Bayesian inference algorithm is used (parameter: the prior distribution type is selected according to the properties of the physical quantities collected for feature acquisition, such as Gaussian prior, Beta prior, etc.) to realize the data fusion inference function that combines prior knowledge with on-site observation. Furthermore, through prior distribution With likelihood function Bayes' theorem for calculating the posterior distribution This enables posterior probability density estimation for each feature dimension. in, For the true value of the feature, For observational data. Furthermore, It is the probability normalization factor, used to ensure the consistency of the probability of the posterior distribution; Furthermore, after obtaining the posterior distribution of each feature dimension, based on the pre-set confidence level parameters... (Typical value is 0.95), calculate the posterior confidence interval using the quantile function. : in, The inverse cumulative distribution function of the posterior distribution is used to calculate the boundary of the Bayesian confidence interval representing the validity of the observation; Furthermore, define the Bayesian confidence parameter. Confidence interval width and feature acquisition range Inverse proportional value: This metric is used to standardize and measure the reliability of feature components. The closer the value is to 1, the more reliable the feature observation is; Through the above Bayesian inference and confidence interval calculation, each feature dimension in the previous feature attribution label set is converted into a quantitative Bayesian confidence parameter representing the validity of the observation, thereby achieving the comparability and stability of the input features in the uncertainty assessment process. For example, in oil and gas wellhead fracturing data processing, the prior distribution for the real-time pressure characteristic dimension is set to a Gaussian distribution. ,in MPa MPa; the mean of the 10 pressure observations collected was MPa, variance MPa². The posterior mean was calculated using a Bayesian inference Gaussian-Gaussian conjugate model. MPa, posterior variance MPa², at the confidence level When, look up the quantiles of the normal distribution. MPa MPa, pressure sensor range MPa, therefore the Bayes confidence parameter This indicates that the reliability of this feature acquisition is high; for the environmental temperature feature, the Beta prior distribution Beta( Describe its empirical distribution within the seasonal distribution, and the number of valid successful observations collected. Number of failures Then, Beta is obtained through Bayesian update. ) posterior distribution, in When, calculated according to quantiles Range (Normalized value), thus The reliability of the characterization observations is relatively low. The Bayesian confidence parameters obtained by this method will serve as the input basis for subsequent feature noise assessment and environmental variability weighting, significantly enhancing the quantitative assessment capability of feature effectiveness under extreme conditions. S3.3: Using the above Bayesian confidence parameters as input, a feature noise estimation algorithm and a missing data imputation strategy are further introduced. For each feature dimension, the volatility statistics and collection continuity index of its collected data are calculated separately to form a set of data quality assessment indicators to supplement the stability and integrity information of the observed data. S3.4: Based on the data quality assessment index set and the dynamic fluctuation analysis results of environmental monitoring characteristics, construct a working condition variability discrimination model, use Bayesian factor analysis to analyze the changing trend of each characteristic under extreme environmental conditions, output environmental variability weight parameters, and reflect the environmental robustness level of the characteristic input. Based on the dynamic fluctuation analysis results of the data quality assessment index set and environmental monitoring characteristics, an environmental fluctuation feature extraction method (parameters: fluctuation detection window length = 60 min, sampling step size = 5 min) is adopted to separate the short-term and long-term fluctuation trends of each environmental monitoring feature in the time domain. Furthermore, the multi-scale fluctuation signal decomposition of environmental variables is realized by using the wavelet transform decomposition algorithm (parameters: Daubechies-4 wavelet basis, number of decomposition layers = 4), and the standard deviation and coefficient of variation at each scale are extracted as fluctuation features. Furthermore, by utilizing the feature fusion mechanism in the construction process of the working condition variability discrimination model (parameter: fusion weight ratio = 0.4:0.3:0.3, corresponding to data quality assessment indicators, short-term fluctuation characteristics and long-term fluctuation characteristics respectively), fluctuation indicators from different sources are uniformly mapped to the feature space to form a multidimensional environmental variability vector; Furthermore, the Bayesian factor calculation method is used to apply the extreme environment assumption. Compared with normal environment assumptions The likelihood ratio of the environmental characteristics is calculated using the following formula: in, Bayesian factor, The observed environmental fluctuation feature vector, Assuming extreme environmental conditions, This assumes normal environmental conditions. Furthermore, by setting the Bayesian factor threshold (Typical value is 3), to identify extreme environmental conditions and calculate the environmental variability weight parameter. for: in, This is the weight smoothing coefficient (typically 2.0), used to map the Bayesian factor to a smooth curve in the 0-1 range, enabling continuous and differentiable adjustment of the weights; This processing method transforms the multidimensional fluctuation characteristics under different environmental conditions into quantitative environmental variability weight parameters, thereby achieving a standardized characterization of the environmental robustness level of the input features. For example, in monitoring extreme operating conditions at an oil and gas wellhead, environmental monitoring features include temperature, humidity, wind speed, and formation stress. The fluctuation detection window is set to 60 minutes, and the sampling interval is 5 minutes. Through wavelet transform decomposition, the standard deviation of temperature fluctuation at the second scale is 3.6℃, ​​and the coefficient of variation is 0.12; the standard deviation of wind speed fluctuation at the third scale is 1.8 m / s, and the coefficient of variation is 0.25. After setting the fusion weight ratio to 0.4:0.3:0.3, an environmental fluctuation feature vector [0.92, 0.36, 0.58] is formed. This is based on the extreme environment assumption. The lower feature likelihood is 0.018, under the normal environment assumption. The lower feature likelihood is 0.005. Calculate the Bayes factor. When the threshold When, smoothing coefficient Calculate the environmental variability weight parameters This result indicates that the current environment is highly volatile, requiring weighted adjustments to relevant features during subsequent reinforcement learning training to improve robustness. S3.5: By integrating Bayesian confidence parameters, data quality assessment indicators, and environmental variability weight parameters, a weighted normalization mechanism is adopted to generate a multidimensional uncertainty weight factor set. This weight factor set serves as the input to the weighted multimodal features, providing an adaptive uncertainty input guarantee for the subsequent reinforcement learning strategy generation module, thereby enhancing the fundamental robustness of the entire reinforcement learning system under extreme conditions.

[0014] Step S4: Input the weighted multidimensional features and corresponding uncertainty weight factors into the reinforcement learning inference architecture, initialize the robust reinforcement learning model, and perform adaptive policy generation and dynamic policy evaluation for extreme working conditions. Specifically, this includes: S4.1: Assemble and format the multidimensional feature vectors and Bayesian uncertainty weight factors output by the feature embedding module to construct a feature input data package that conforms to the interface specification of the reinforcement learning inference architecture, ensuring the consistency of the mapping between feature vectors and weight factors, and facilitating efficient data parsing and utilization in the subsequent policy generation process; S4.2: Based on the formatted feature input data package, an adaptive data normalization algorithm is adopted to perform uniform scale reduction and numerical stabilization processing on multi-dimensional feature vectors to eliminate the distribution deviation of different feature modes and weight factors at the numerical level, and improve the robustness of the reinforcement learning inference architecture to input data under extreme working conditions. Based on formatted feature input data packets, an adaptive data normalization algorithm is adopted (parameter: normalization method selection automatically matches Z-score standardization, Min-Max scaling and Robust Scaling strategy according to feature distribution type) to achieve unified scale reduction and numerical stabilization processing of multi-dimensional feature vectors; Furthermore, the distribution feature types of each feature mode are identified using feature distribution detection methods (parameters: distribution test methods are Shapiro-Wilk test and Kolmogorov-Smirnov test, significance level α=0.05), and an appropriate normalization formula is dynamically selected. For features that approximate a normal distribution, Z-score standardization is used for calculation. in, These are the original eigenvalues. This is the mean of the sample for this feature. The standard deviation is used to ensure that the mean of the normalized features is 0 and the variance is 1. Furthermore, for features that are not normally distributed and have extreme values, Robust Scaling normalization is used: in, and They are quartiles, The median is used to reduce the impact of extreme values ​​on the normalization results; Furthermore, for features whose value range is known and stable, Min-Max scaling is applied: in, and The minimum and maximum values ​​of the feature are respectively used to map the value range to the [0,1] interval, thereby achieving consistent numerical proportions of cross-modal features; Furthermore, a numerical stabilization process is introduced (parameter: stabilization threshold ε = 1e-5). In the normalized results, for values ​​with absolute values ​​less than ε, an offset adjustment is performed, calculated using the following formula: in, The sign function ensures that the normalized features avoid the gradient vanishing problem caused by excessively small values ​​when inputting into the reinforcement learning inference architecture; Through the chained processing of multi-strategy dynamic matching and numerical stabilization, the input features are unified to a standardized scale space under different modalities, distributions and weight factors, thereby improving the robustness of the reinforcement learning inference architecture in the extreme working condition data input stage. For example, in a certain oil and gas wellhead fracturing production enhancement prediction task, the formatted feature input data package contains 120-dimensional feature vectors of 5 feature modes after Bayesian weighting. The real-time pressure feature exhibits an approximately normal distribution (Shapiro-Wilk test p=0.42), with a mean of 50 MPa and a standard deviation of 5 MPa. After Z-score standardization, it is transformed into a value with a mean of 0 and a variance of 1. The fracture length feature is found to be non-normally distributed and has extreme values ​​(p<0.01). After RobustScaling processing, the fracture length is further reduced. rice, rice, The standardization effectively suppressed the influence of extreme values ​​on the model input. The ambient temperature feature range was stabilized in the range of [-20℃, 60℃], and directly mapped to the [0,1] interval using Min-Max scaling. During the normalization process, all eigenvalues ​​with absolute values ​​below 1e-5 were stabilized and shifted to obtain a numerical range conducive to gradient propagation. After implementation, the model was verified to significantly reduce the variance fluctuation of input features in extreme data distribution scenarios (such as sporadic sensor distortion and sudden changes in operating conditions), and the robustness evaluation index was improved by about 15%, effectively enhancing the adaptability and stability of the reinforcement learning inference architecture under extreme wellhead conditions. S4.3: Dynamic weight adjustment is performed on the normalized feature input using uncertainty weighting factors, and weighted linear fusion processing is performed on each feature component to form a weighted feature input set that reflects the changes in observation effectiveness and data quality, providing a comprehensive information foundation for robust reinforcement learning models that fully reflects multimodal confidence. S4.4: The weighted feature input set and its uncertainty weights are jointly input into the reinforcement learning inference architecture. Based on the characteristics of the current extreme working conditions, the parameters of the robust reinforcement learning model are initialized, and adaptive policy generation is performed according to the environmental representation so that the model can automatically adjust the policy output to cope with different extreme scenarios. Based on the weighted feature input set output by S4.3 and its associated uncertainty weight factors, a multi-input channel mapping algorithm (parameters: the number of channels corresponds to the number of feature modality categories, and the mapping method is fully connected embedding mapping) is adopted to realize the synchronous loading and channel mapping of the weighted feature matrix and the weight factor matrix in the input layer of the reinforcement learning inference architecture. Furthermore, a feature-weight joint encoding method (parameters: encoding structure is a weighted gating unit, activation function is sigmoid) is used to achieve weight modulation of the input stream for each feature component, as shown in the formula: in, These are the modulated feature values. The normalized feature input values, For the corresponding uncertainty weighting factor, It is a sigmoid gate function used to compress the weight range to a controllable interval, so as to achieve adaptive scaling of the importance of features under extreme conditions. Furthermore, using the environmental representation vector generation module (parameters: input includes environmental variability weights, current operating condition category labels, and fluctuation feature vectors; embedding dimension is set to 32), the operating condition features are mapped to the latent space representation. And through state enhancement functions Fusion with weighted feature encoding results: in, The set of vectors containing all modulated features is used to fuse the data by combining dimension-wise weighted addition with bidirectional residual connections, in order to preserve the complementarity between the original information and the environmental information. Furthermore, the fused features The initialization module for the robust reinforcement learning model is input, employing a dual-branch structure of a parameterized policy network and a value network (parameters: 128 / 64 hidden nodes in the policy network, 256 / 128 hidden nodes in the value network, and ReLU activation function). The weight matrix is ​​initialized using a pre-loaded extreme condition policy. Improve the matching degree between the initialization state and the target environment; Furthermore, an uncertainty-aware adaptive exploration coefficient is introduced into the strategy generation process. The calculation method is as follows: in, Based on the exploration rate, The overall average confidence level of the features. As environmental variability weights, and These are coefficient parameters used to dynamically adjust the balance between exploration and exploitation to improve the robustness of the strategy in high-uncertainty environments. Through the above joint input and model initialization processing, the weighted feature input set and the uncertainty weight factor are successfully transformed into context-aware input for reinforcement policy generation, thereby maximizing the adaptive policy generation capability of the reinforcement learning model under extreme conditions. For example, in an extreme fracturing prediction environment at an oil and gas wellhead, the input is a 120-dimensional weighted feature vector processed by S4.3 and the corresponding 120-dimensional uncertainty weight matrix. The channel mapping module maps three modes—real-time pressure, fracture length, and ambient temperature—to three input channels respectively. The gating unit selects high-confidence pressure features ( Maintaining values ​​close to the original values ​​for low-confidence temperature characteristics ( The amplitude reduction is performed. The environmental characterization module embeds the current operating condition label (high temperature and high pressure, large wind speed fluctuations) and the fluctuation feature vector into a 32-dimensional latent space vector. , and the feature vector set modulated by gating Generated by residual fusion Robust reinforcement learning models load pre-trained weights from similar environments during initialization. The policy network and value network are forward-propagated separately to obtain the initial policy distribution. Based on the average confidence level of the input data... and environmental variability weights Calculate the exploration coefficient This is used to control the exploration ratio during the policy generation process. After execution, the convergence speed of the policies generated by the model in a high volatility environment is significantly improved compared to the unweighted input scenario, and the prediction bias is reduced; S4.5: After initializing the robust reinforcement learning model, based on the current policy generation results and environmental state variables, a dynamic policy evaluation mechanism is adopted to measure the performance indicators of the policy output in real time, improve the model's ability to respond to extreme environmental changes such as high noise and missing data, and realize dynamic adjustment and robust optimization closed loop. Step S5: Based on an interpretable causal reasoning mechanism, the intermediate state outputs within the model are jointly aligned with physical mechanism parameters and expert business indicators. The prediction bias is compared and dynamically fed back to the reward function structure to achieve online bias correction. Specifically, this includes: S5.1: Obtain the intermediate state output of the reinforcement learning model, combine the physical mechanism parameters and expert business indicators as input conditions, analyze the causal relationship between each state variable based on the interpretable causal reasoning model, and output the causal mapping relationship between the intermediate state variables and the actual physical parameters and business indicators. S5.2: Using the above causal mapping relationship, calculate the prediction deviation between the intermediate state output of the reinforcement learning model and the physical mechanism parameters and expert business indicators. Use a discriminator network or error analysis algorithm to quantitatively measure the prediction accuracy of each intermediate state variable and output a multidimensional difference measurement index. Based on the causal mapping relationship between intermediate state variables, physical mechanism parameters, and expert business indicators obtained from S5.1, an error analysis algorithm (parameters: the mapping relationship is a multi-dimensional vector alignment mapping model, and the error analysis algorithm is a composite evaluation method combining root mean square error (RMSE) and mean absolute percentage error (MAPE)) is used to calculate the deviation between each state variable and its physical or business benchmark value. Furthermore, the prediction bias of continuous-valued state variables is calculated using the following root mean square error formula: in, For the first Each state variable in the sample The predicted value on For the corresponding actual physical or operational observations, The total number of samples is used, and the calculation result reflects the absolute dimension of the deviation. Furthermore, the mean absolute percentage error formula is used to measure the relative deviation of proportional or normalized state variables: The calculation result represents the percentage error of the predicted value relative to the true value, and is applicable to comparisons under different units of measurement. Furthermore, the RMSE and MAPE results are normalized according to feature categories (parameter: normalization method is Min-Max scaling to the [0,1] interval) to eliminate the differences in numerical distribution between different indicators, thereby constructing a multidimensional difference basic indicator set; Furthermore, a discriminator network (parameters: input layer node count equals the dimension of the differential basic indicator set, hidden layer structure is 64-32-16 nodes, activation function is ReLU, output layer is a Sigmoid mapping) is used to classify the differential basic indicator set, outputting a "prediction accuracy score" for each intermediate state variable. The score ranges from [0,1], and a higher score indicates a higher prediction accuracy. By combining the above error calculation with the discriminator evaluation, the causal mapping relationship from the previous step is transformed into a multidimensional difference measure. ,in It can be represented as: in, and These are weighting coefficients used to balance the contributions of RMSE, MAPE, and classification accuracy. (Set of difference metrics) This will serve as the direct input for generating the subsequent S5.3 deviation weighting factor, enabling online deviation quantification under physical and business consistency. For example, in a high-temperature, high-pressure oil and gas well fracturing and production enhancement prediction task, the intermediate state variables obtained include the estimated wellbore pressure, the predicted fracture propagation rate, and the predicted short-term production. The actual wellbore pressure in the sample set has an observed average of 52 MPa and a predicted average of 51.7 MPa. The RMSE formula is used to calculate... MPa, after normalization The actual crack propagation rate was 0.65 m / min, the predicted rate was 0.70 m / min, and the MAPE was 7.69%. After normalization... The discriminator network outputs the accuracy of pressure prediction. Output of accuracy for crack propagation rate prediction .set up , The difference index of wellbore pressure is The difference index of crack propagation rate is The difference measurement results clearly reveal that the prediction deviation of crack propagation rate in the current high-temperature and high-pressure environment is significantly greater than the deviation of pressure prediction, providing a precise quantitative basis for subsequent reward function structure adjustment and strategy optimization. This processing method effectively achieves a seamless transformation from causal mapping to multidimensional difference quantification, improving the accuracy of online deviation diagnosis under extreme conditions. S5.3: Based on the difference measurement index, evaluate the intermediate state output of the model item by item, determine the key influencing factors of physical mechanism parameters and expert business indicators under the current strategy, generate deviation weight factors and realize the root cause tracking of differences; S5.4: Input the deviation weight factor and causal mapping relationship into the dynamic reward function template, and adjust the reward function parameter weights in a structured manner to realize the online reward structure correction for the deviation between physical mechanism parameters and expert business indicators, and output the online corrected reward distribution; The set of deviation weight factors generated in S5.3 and the causal mapping relationship obtained in S5.1 are used as joint inputs. The algorithm module is called using a dynamic reward function template (parameter: the template structure includes a physical mechanism deviation correction submodule, a business indicator deviation correction submodule and a joint weight normalization submodule) to realize the structured adjustment of the weight of the reward function parameters. Furthermore, through the physical mechanism deviation correction submodule (parameters: physical deviation threshold θp=0.05, correction gain coefficient γp=0.8), the deviation weighting factor of the corresponding physical parameters is adjusted. Mapped to reward weight adjustment value The calculation formula is: in, To prevent division by zero of small constants; Furthermore, through the business indicator deviation correction submodule (parameters: business indicator deviation threshold θb=0.03, correction gain coefficient γb=0.9), the deviation weighting factor of the corresponding business indicator is adjusted. Mapped to reward weight adjustment value The calculation formula is: Furthermore, the two types of correction values ​​mentioned above are represented by a causal mapping matrix. To serve as a connecting link, normalization is performed through a joint weight normalization submodule, resulting in a normalized comprehensive correction vector. The calculation formula is: The above This is the index of the corresponding policy dimension in the reward function; Furthermore, Compared with the original reward function weight vector Linear fusion is performed using a scaling factor λr (ranging from [0,1]) to generate an online-corrected reward distribution. Its formula is: Through the above-mentioned structural adjustments and integration, the deviations of physical mechanism parameters and expert business indicators are mapped into the reward function structure in real time, thereby realizing the online adaptive correction of the reward function under extreme working conditions. This method transforms the bias weighting factor and causal mapping information from the previous step into a real-time corrected reward function parameter vector, enabling dynamic convergence of the objective function of the reinforcement learning model during training and inference, and improving the consistency between the prediction strategy and business objectives. For example, in the prediction of fracturing production enhancement under a certain high temperature and high pressure environment, the weighting factor of the physical deviation of wellbore pressure output by S5.3 Crack propagation rate physical deviation weighting factor Expert evaluation score business deviation weighting factor Using θp=0.05 and γp=0.8, the pressure correction value... Crack rate correction value Using θb=0.03 and γb=0.9, the adjusted values ​​for business indicators are... Causal mapping matrix Setting the correlation coefficient between pressure and business score to 0.65, and the correlation coefficient between crack rate and business score to 0.72, substituting these three values ​​into the normalization formula yields... Original reward vector With a fusion ratio of λr=0.6, the generated The verification results show that, based on After 20 iterations, the convergence speed of the strategy training was significantly improved, the prediction error was significantly reduced under extreme conditions, and the model’s business objective fit and environmental robustness were significantly improved. S5.5: Utilizing the corrected reward distribution, guide the reinforcement learning model to complete adaptive policy cyclic updates, dynamically adjust the policy generation direction, and monitor the influence weight of each intermediate state variable on the final production increase prediction in real time, so as to realize the model's continuous deviation correction of prediction results under extreme environments and improve business consistency output.

[0015] Step S6: For multi-objective business indicators, dynamically adjust the reward distribution structure based on the deviation between the reward function and the actual wellhead production enhancement effect indicators, and optimize the objective function using a piecewise linear or nonlinear adaptive weighting strategy to improve the consistency between model output and business objectives. Specifically, this includes: S6.1: Perform dynamic deviation evaluation on the aligned intermediate state output, physical mechanism parameters, and expert business indicators (i.e., the output of the previous step). Calculate the deviation of each business objective indicator (such as instantaneous oil and gas production increase, cycle production increase stability, and deviation tolerance under extreme conditions) from the mapping relationship with the reward function based on the feedforward neural network. Obtain the coupling deviation vector between the business indicators and the reward function to provide constraint factors for subsequent adjustment of the reward function distribution structure. S6.2: A multi-objective adaptive weighting mechanism is adopted to normalize the reward function coupling deviation vector and its related physical mechanism parameters obtained in S6.1. A dynamic reward distribution structure is constructed using weighted linear or weighted nonlinear functions to achieve dynamic adaptive adjustment of the proportion of each business objective in the reward function, and output the segmented weighted reward structure parameters. S6.3: Based on the above dynamic weighted reward structure parameters, the original reinforcement learning reward function is revised in real time using the dynamic reward recalibration algorithm. Reward gain compensation and penalty adjustment are implemented for abnormal working conditions to generate a multi-level segmented reward distribution, so that the reward function can accurately reflect the changing trend of instantaneous and long-term business goals. S6.4: Using the dynamically revised piecewise reward function as input, combined with the business indicators, physical mechanism parameters and expert business indicators from the previous step, the policy gradient optimization algorithm is used to perform multiple rounds of iterative optimization on the objective function of the robust reinforcement learning model, continuously reducing the relative error between the model output policy and the real business objective. S6.5: Output the final reward function parameters after adaptive segmented weighting adjustment, and solidify them in the reinforcement learning policy generation module as a benchmark input for continuous iterative optimization. This ensures that subsequent model inference and policy optimization can closely match the dynamic changes in on-site business objectives, thereby improving the overall system's environmental adaptability and business robustness.

[0016] Step S7: The prediction strategy output by the reinforcement learning model after dynamic reward adjustment is compared and regressed with evaluation indicators such as actual wellhead production and cycle stability to test the robustness of the model under high noise, missing data, and extreme operating conditions. Specifically, this includes: S7.1: The prediction strategy output by the reinforcement learning model is sampled and acquired over multiple time periods, including key information such as single-well production increase prediction and fluctuation prediction within the production cycle. A periodic prediction output sequence is formed with the evaluation cycle as the unit, which serves as the main input data for subsequent comparison and analysis. S7.2: Perform data preprocessing on the actual wellhead production data and measured periodic stability indicators collected on-site, including anomaly detection, missing value imputation and high noise interval filtering, to obtain a set of high-confidence actual evaluation indicators for regression analysis and model comparison. S7.3: Based on the predicted strategy output sequence after dynamic reward adjustment and the pre-processed actual wellhead production index, the partial least squares regression (PLSR) algorithm is used for regression modeling analysis to quantify the goodness of fit and correlation between the two under different operating conditions and different data anomaly types. Based on the dynamically reward-adjusted prediction strategy output sequence obtained through steps S7.1 and S7.2 and the high-confidence actual wellhead production index, a partial least squares regression (PLSR) algorithm is used (parameter: number of latent variables). As determined by cross-validation, the centralized and standardized preprocessing method is Z-score standardization, which enables multidimensional regression modeling of the predicted sequence and the actual observed indicators; Furthermore, by analyzing the input prediction policy matrix... With actual output index matrix Simultaneously, a centralized processing method is performed to construct the covariance matrix. This enables a preliminary linear correlation characterization between predicted and measured data; Furthermore, the NIPALS iterative method is employed to maximize the extraction process in each round. Vector and The covariance of the vectors, where the optimization objective is: To achieve consistency between the projection direction of predicted data and the projection direction of measured data, in order to obtain latent variables. and And the model convergence is achieved by updating the regression coefficients; Furthermore, by solving the regression coefficient matrix of PLSR : in , , These are the weight matrix, load matrix, and The end load matrix enables linear mapping modeling from predicted input to actual output. Furthermore, using the established PLSR model, regression fit values ​​are output for each sample time period. and with reality Calculate goodness of fit Mean square error ( The model prediction and actual output fit and correlation index are quantified under different working conditions and different anomaly types using Pearson correlation coefficient. By using PLSR regression modeling and index calculation, the relationship between the predicted strategy output and the actual wellhead production is transformed into a structured regression analysis parameter set, enabling quantitative measurement of the model's fitting performance and correlation performance under extreme working conditions and abnormal data characteristics. For example, in a single-well monitoring scenario, the prediction strategy produces a sequence. Includes 15 dimensions of features, including instantaneous output increase, cumulative output increase rate of change, and periodic stability across 24 time windows, based on measured data. It includes two-dimensional features, comprising real-time output and measured values ​​of cycle stability across 24 time windows concurrently. During implementation, [the following is likely a separate, unrelated sentence:] ... and Perform Z-score standardization and calculate The covariance matrix was calculated, and the NIPALS iterative method was used to extract it. There are several latent variables. The regression coefficient matrix is ​​calculated using the above formula. and generate predicted values. Statistical analysis shows that this PLSR model performs well in high-noise scenarios (signal-to-noise ratio below 10dB). The value is 0.88, the mean squared error is 0.07, and the probability that the Pearson correlation coefficient is lower than the minimum business requirement threshold of 0.85 for extreme conditions is 5%, which verifies the effectiveness of this modeling and analysis step in quantifying the correlation between prediction and actual measurement and in helping to determine the robustness of the model. S7.4: Label the classification information of high noise, missing data and extreme working conditions. Through hierarchical statistical analysis, label the extreme working condition attributes of each data interval, and provide professional label basis for sub-interval grouping analysis of subsequent robustness test. S7.5: Based on the regression model analysis results and operating condition labels, perform residual statistics and robustness quantification calculations on the prediction error distribution and model stability response in each extreme environment sub-interval, and generate a robustness evaluation parameter set; S7.6: Compare the obtained robustness evaluation parameter set with the original robustness threshold of the model, use the interval discrimination algorithm to identify the key working condition intervals with insufficient robustness, and record the corresponding dynamic reward modification suggestions to provide data backtracking and feedback basis for subsequent model iteration and optimization.

[0017] Step S8: Based on the actual business evaluation results, the reinforcement learning model parameters are retrained and iteratively optimized to continuously improve the model's adaptive capability under extreme environments and abnormal data distributions, achieving long-term dynamic adaptation and improved robustness to business scenarios. Specifically, this includes: S8.1: Obtain actual operational evaluation data of the reinforcement learning model output strategy adjusted by dynamic rewards at oil and gas wellheads, and use actual business evaluation indicators (including actual wellhead production, cycle stability and environmental operating condition statistics) to quantitatively analyze the prediction performance and obtain the model business performance deviation parameters. S8.2: Based on the model's business performance deviation parameters, an adaptive parameter adjustment strategy is adopted to perform hierarchical optimization on key hyperparameters such as network structure parameters, learning rate, and discount factor of the reinforcement learning model. An optimized model parameter set is generated through an automated model parameter resetting module to improve the model's adaptability to extreme environments and abnormal data distributions. S8.3: The optimized model parameter set and actual business evaluation indicators are used as inputs. The robust reinforcement learning model is retrained using the incremental backpropagation training mechanism to generate updated model weight parameters, thereby achieving automatic adaptive optimization for the latest extreme working conditions and multimodal input changes. S8.4: For the model weight parameters generated by retraining, combine the Bayesian uncertainty weight factor to conduct uncertainty-driven testing and regression analysis, calculate the adaptive robustness index of the model under new high noise, missing data and atypical environmental data, and form a model adaptability evaluation report. S8.5: Based on the robustness index feedback from the model adaptability evaluation report, construct a continuous model iteration optimization process. Through iterative automated model retraining and parameter optimization, dynamically improve the long-term adaptive capability of the reinforcement learning model to extreme working conditions in business scenarios, and achieve continuous alignment of the production increase effect prediction system with business objectives.

[0018] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0019] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and rules of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for intelligently predicting the stimulation effect of fracturing a wellhead, characterized in that, The method comprises the following steps: S1: based on the real-time sensing points of oil and gas wellhead fracturing site, historical working condition records, environmental monitoring results, expert scoring information and geological prior parameters, collecting and summarizing multi-modal original data; S2: performing standardization and feature embedding processing on the multi-modal original data to form a unified input feature set; S3: using a Bayesian uncertainty evaluation method to quantize the confidence of different modal feature inputs, generating multi-dimensional uncertainty weight factors including observation effectiveness, data quality and environmental variability; S4: inputting the weighted multi-dimensional feature input and the corresponding uncertainty weight factor into the enhanced learning reasoning architecture, initializing the robust enhanced learning model, generating adaptive strategy and dynamic strategy evaluation for extreme working condition environmental changes; S5: based on the interpretable causal reasoning mechanism, the model internal intermediate state output is aligned with the physical mechanism parameters and expert business indicators, the prediction bias is calculated and dynamically fed back to the reward function structure; S6: for multi-objective business indicators, dynamically adjust the reward distribution structure according to the deviation of the reward function and the real fracturing wellhead stimulation effect index, and use piecewise linear or nonlinear adaptive weighting strategy to optimize the objective function; S7: comparing and regression analyzing the output prediction strategy of the enhanced learning model adjusted by the dynamic reward with the actual wellhead production and cycle stability evaluation indicators, detecting the robustness of the model in high noise, data missing and extreme working condition scenarios.

2. The intelligent prediction method for fracturing wellhead stimulation effect according to claim 1, characterized in that, The step S7 further comprises: S8: based on the actual business evaluation results, retraining and iterative optimization of the enhanced learning model parameters, continuously improving the adaptive ability of the model in extreme environment and abnormal data distribution.

3. The intelligent prediction method for fracturing wellhead stimulation effect according to claim 1, characterized in that, The step S1 specifically comprises: The fracturing parameter data collected by the wellhead real-time sensing points are acquired, and the field data acquisition module is used for second-level sampling, while the data acquisition timestamp and wellhead geographical position are labeled, obtaining real-time sensing data stream with high spatio-temporal accuracy; Based on the production management system, the historical record files of oil and gas well fracturing operation working conditions in previous years are obtained, and the key working condition parameters are extracted through the data archiving standardization interface; Using the environmental monitoring subsystem, the local environmental meteorological data and geological stability parameters of the operation well site are synchronously collected to obtain environmental monitoring data, and the environmental perception network is used to perform multi-dimensional matching and archiving on the time series environmental state and working condition data; Through the expert review and field scoring system, the process score, abnormal event evaluation and operation process label of each fracturing operation are sequentially entered, combined with the expert experience knowledge graph, the multi-source expert scoring information is integrated and mapped with the fracturing parameter data; Based on the well site geological exploration database, the geological prior parameters of the fracturing area are extracted, and the standard geological label format coding is used; The real-time sensing data stream, the working condition history record, the environmental monitoring data, the expert scoring information and the geological prior parameters are preprocessed in a unified format, and the multi-source normalized original data is summarized.

4. The intelligent prediction method for fracturing wellhead stimulation effect according to claim 3, characterized in that, The data collected by the wellhead real-time sensing point in step S1 includes pressure, flow rate, temperature, and fracture length. The field data acquisition module has a sampling frequency of 1-10 Hz, an acquisition delay of no more than 50 ms, and a time stamp error of less than 100 ms after GPS time calibration.

5. The intelligent prediction method for fracturing wellhead stimulation effect according to claim 1, characterized in that, Step S2 specifically includes: Data integrity checking and missing data filling are performed on the multi-modal raw data to obtain an initial feature matrix; Based on the initial feature matrix, a standardization algorithm is used to reorganize the numerical distribution of each feature dimension for different data modalities, and a standardized feature matrix is obtained; Feature dimension reduction and embedding algorithm processing are performed on the standardized feature matrix to extract low-dimensional embedding features related to fracturing stimulation, and a multi-modal embedding feature vector is output; The multi-modal embedding feature vector is combined and windowed according to a unified data structure, and the multi-source heterogeneous features are uniformly mapped to a standard input feature set to form a high-consistency feature input set; Heterogeneous feature correlation re-evaluation and redundant feature identification are performed on the high-consistency feature input set, and a standardized feature set is obtained based on mutual information analysis and correlation coefficient threshold screening methods.

6. The intelligent prediction method for fracturing wellhead stimulation effect according to claim 5, characterized in that, The standardization algorithm in step S2 includes normalization, standardization, and min-max scaling. The feature dimension reduction and embedding algorithm processing includes principal component analysis (PCA), linear discriminant analysis (LDA), T-sne, or deep embedding network.

7. The intelligent prediction method for fracturing wellhead stimulation effect according to claim 1, characterized in that, Step S3 specifically includes: Classify and organize each type of modal data in the unified input feature set, label the corresponding data acquisition conditions, equipment operating states, and expected physical distributions based on feature types and their sources of acquisition, and construct a feature attribution label set; For each feature dimension in the feature attribution label set, apply a Bayesian inference algorithm to calculate the confidence interval of each modal feature based on the prior distribution of the acquisition conditions and real-time acquisition observations, and obtain a Bayesian confidence parameter; Take the Bayesian confidence parameter as input, further introduce a feature noise estimation algorithm and a missing data imputation strategy, and calculate the volatility statistics and acquisition continuity index of each feature dimension to form a data quality evaluation index set; Based on the data quality evaluation index set, construct a condition variability discrimination model by combining the dynamic volatility analysis results of the environmental monitoring features, analyze the change trend of each feature under extreme environmental conditions using Bayesian factor analysis, and output an environmental variability weight parameter; Fuse the Bayesian confidence parameter, the data quality evaluation index, and the environmental variability weight parameter, and generate a multi-dimensional uncertainty weight factor set using a weighted normalization mechanism.

8. The intelligent prediction method for fracturing wellhead stimulation effect according to claim 1, characterized in that, Step S4 specifically includes: Assemble and format the multi-dimensional feature vector and Bayesian uncertainty weight factor output by the feature embedding module to construct a feature input data package that meets the interface specification of the enhanced learning reasoning architecture; Based on the formatted feature input data package, use an adaptive data normalization algorithm to implement unified scale reduction and numerical stabilization processing on the multi-dimensional feature vector; The uncertainty weight factor is used to perform dynamic weight adjustment on the normalized feature input, and weighted linear fusion processing is performed on each feature component to form a weighted feature input set; The weighted feature input set and its uncertainty weight are jointly input into the enhanced learning inference architecture, and the robust enhanced learning model parameters are initialized for the current extreme working condition environment feature, and the adaptive strategy generation is performed according to the environment representation; After initializing the robust enhanced learning model, based on the current strategy generation result and the environment state variable, a dynamic strategy evaluation mechanism is used to real-time measure the performance index of the strategy output.

9. The intelligent prediction method for fracturing wellhead stimulation effect according to claim 1, characterized in that, The step S5 specifically includes: Obtain the intermediate state output of the enhanced learning model, combine the physical mechanism parameters and expert business indicators as input conditions, analyze the causal relationship between each state variable based on the interpretable causal inference model, and output the causal mapping relationship between the intermediate state variable and the actual physical parameter and business indicator; Using the causal mapping relationship, calculate the prediction bias between the intermediate state output of the enhanced learning model and the physical mechanism parameters and expert business indicators, use the discriminator network or error analysis algorithm to quantitatively measure the prediction accuracy of each intermediate state variable, and output a multi-dimensional difference measurement index; Based on the multi-dimensional difference measurement index, the model intermediate state output is evaluated item by item to determine the key influence factors of the physical mechanism parameters and the expert business indicators under the current strategy, and a bias weight factor is generated; The bias weight factor and the causal mapping relationship are jointly input into the dynamic reward function template, and the reward function parameter weight is adjusted through structuring to output the online corrected reward distribution; Using the corrected reward distribution, guide the enhanced learning model to complete the adaptive strategy cycle update, dynamically adjust the strategy generation direction, and real-time monitor the influence weight of each intermediate state variable on the final yield prediction effect.

10. The intelligent fracturing wellhead stimulation effect prediction method of claim 2, wherein, The retraining process includes obtaining actual business yield and periodic evaluation data, using back propagation and adaptive parameter adjustment to optimize model hyperparameters, and combining Bayesian uncertainty factors for new round of model training and robustness verification.

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