Studies on natural gas production prediction and risk quantification of Sinian gas reservoir in Sichuan Basin

The Sinian gas reservoir in the Sichuan Basin has the potential for natural gas exploration and development. Production prediction and risk quantification are important in planning of natural gas resources. Ultimate recoverable reserves (URRs) of Sinian gas reservoir are estimated. Hubbert and Gauss models are used to predict the growth trend of production in the gas reservoir. Based on the prediction results, the Monte Carlo simulation is used to calculate the probability of production realization. The evaluation matrix of risk level is established by using indices of production realization probability and dispersion degree for assessing the risk level of natural gas production. The results show that: (1) compared to the Hubbert model, the production prediction results of the Gauss model have higher accuracy. The Sinian gas reservoir will reach peak production of $$\left( {140 - 285} \right) \times 10^{8} {\text{m}}^{3} /{\text{a}}$$ 140 - 285 × 10 8 m 3 / a in 2036 and will have stable production from 2032 to 2040. By the end of the stable production stage, the URR exploitation degree is about 60% and (2) the Monte Carlo method can be used to obtain the production realization probability for each year. The risk level evaluation matrix can be established by taking the probability of realization and the dispersion degree as evaluation indices, which can provide the systematization of the risk levels. The study can help to better understand the guiding significance for the natural gas exploration and development.

Fracturing Productivity Prediction Model and Optimization of the Operation Parameters of Shale Gas Well Based on Machine Learning

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.

A Semianalytical Production Prediction Model and Dynamics Performance Analysis for Shale Gas Wells

The shale gas productivity model based on shale gas nonlinear seepage mechanism is an effective way to reasonably predict productivity. The incomplete gas nonlinear effects considered in the current production prediction models can lead to inaccurate production prediction. Based on the conventional five-zone compound flow model, comprehensive gas nonlinearities were considered in the improved compound linear flow model proposed in the paper and a semianalytical solution for productivity was obtained. The reliability of the productivity model was verified by the field data, and then, the 20-year production performance analysis of the gas well was studied. Ultimately, the key influencing factors of the fracture control stage and matrix control stage have been analyzed. Research indicated the following: (1) the EUR predicted by the productivity model is higher than the EUR that the comprehensive nonlinear effects are not considered, which demonstrated that the various nonlinear effects cannot be neglected during the production prediction to ensure the greater calculation accuracy; (2) during the early production stage of shale reservoir, the adsorbed gas is basically not recovered, and the cumulative adsorption contribution rate does not exceed 10%. The final adsorption gas contribution rate is 23.28%, and the annual adsorption rate can exceed 50% in the 20th year, showing that free gas and adsorbed gas are, respectively, important sources of the early stage of production and long-term stable production; (3) the widely ranged three-dimensional fracturing reformation of shale reservoirs and reasonable bottom hole pressure in the later matrix development process should be implemented to increase the effective early production of the reservoir and ensure the earlier gas production process of the matrix development. The findings of this study can help for better ensuring the prediction accuracy of the estimated ultimate recovery and understanding the main influencing factors of the dynamic performance of gas wells so as to provide a theoretical reference for production optimization and development plan formulation of the shale gas reservoirs.