Based on the massive static and dynamic data of 137 fractured wells in WY shale gas block in Sichuan, China, this paper carried out the analysis of shale gas fracturing production influencing factors, production prediction model, and fracturing parameter optimization model research. Taking geological, engineering, fracturing operation, and production data of fractured wells in WY block as data set, the main control analysis method is used to construct the shale gas fracturing production influencing factors as the sample set. A production prediction model based on six machine learning (ML) algorithms including random forest (RF), back propagation (BP) neural network, support vector regression (SVR), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and multivariable linear regression (LR) has been established; the evaluation results show that the XGBoost model has the best performance on this sample set. The selection method of shale gas well fracturing operation scheme set is studied; the production rate and the ratio of cost and profit (ROCP) are comprehensively considered to select the final fracturing operation scheme. Research result shows that the data-driven production prediction model and fracturing parameter optimization model can not only be used to predict the production of shale gas fracturing and optimize operation parameters but also realize the sensitivity analysis of fracturing parameters and the effect comparison of fracturing operation schemes, which has good field application value.

The tight gas field is greatly affected by pressure in the development process. Due to the different production time and formation pressure of each well in the gas field, the production characteristics of the gas well are obviously different. After the gas well sees water, it is impossible to formulate production measures efficiently and accurately. Therefore, by analyzing the production performance characteristics of gas wells, this paper carries out the classification research of tight gas wells, and formulates the corresponding production measures according to the classification results. Taking gas well energy and liquid production intensity as the reference standard of gas well classification, the dynamic parameter indexes characterizing gas well energy and liquid production intensity are established. Gas wells with different production characteristics are divided into six categories by clustering algorithm: high energy-low liquid, high energy-high liquid, medium energy-low liquid, medium energy high-liquid, low energy-low liquid, low energy-high liquid. Then the classification method of tight gas well is formed. In this paper, 50 wells in Linxing block are selected as the research object. The research results show that most of the wells in Linxing block are located in area V, belonging to low energy and low liquid wells. It is recommended to implement intermittent production. The classification based on gas well energy and liquid production intensity are of guiding significance for the formulation of production measures in the Linxing block.

Aiming at the problems of the current production and operation status of the progressive cavity pump (PCP) in coalbed methane (CBM) wells which cannot be timely monitored, quantitatively evaluated, and accurately predicted, a five-step method for evaluating and predicting the health status of PCP wells is proposed: data preprocessing, principal parameter optimization, health index construction, health degree division, and health index prediction. Therein, a health index (HI) formulation was made based on deep learning, and a statistical method was used to define the health status of PCP wells as being healthy, subhealthy, or faulty. This allowed further research on the HI prediction model of PCP wells based on the long short-term memory (LSTM) network. As demonstrated in the study, they can reflect both the change trend and the contextual relevance of the health status of PCP wells with high accuracy to achieve real-time, quantitative, and accurate assessment and prediction. At the same time, the conclusion gives good guidance on the production performance analysis and failure warning of the PCP wells and suggests a new direction for the health status assessment and warning of other artificial lift equipment.

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Evaluating the productivity potential of shale gas well before fracturing reformation is imperative due to the complex fracturing mechanism and high operation investment. However, conventional single-factor analysis method has been unable to meet the demand of productivity potential evaluation due to the numerous and intricate influencing factors. In this paper, a data-driven-based approach is proposed based on the data of 282 shale gas wells in WY block. LightGBM is used to conduct feature ranking, K-means is utilized to classify wells and evaluate gas productivity according to geological features and fracturing operating parameters, and production optimization is realized through random forest. The experimental results show that shale gas productivity potential is basically determined by geological condition for the total influence weights of geologic properties take the proportion of 0.64 and that of engineering attributes is 0.36. The difference between each category of well is more obvious when the cluster number of well is four. Meanwhile, those low production wells with good geological conditions but unreasonable fracturing schemes have the greatest optimization space. The model constructed in this paper can classify shale gas wells according to their productivity differences, help providing suggestions for engineers on productivity evaluation and the design of fracturing operating parameters of shale gas well.