Shale gas production evaluation framework based on data-driven models

Abstract

Increasing the production and utilization of shale gas is of great significance for building a clean and low-carbon energy system. Sharp decline of gas production has been widely observed in shale gas reservoirs. How to forecast shale gas production is still challenging due to complex fracture networks, dynamic fracture properties, frac hits, complicated multiphase flow, and multi-scale flow as well as data quality and uncertainty. This work develops an integrated framework for evaluating shale gas well production based on data-driven models. Firstly, a comprehensive dominated-factor system has been established, including geological, drilling, fracturing, and production factors. Data processing and visualization are required to ensure data quality and determine final data set. A shale gas production evaluation model is developed to evaluate shale gas production levels. Finally, the random forest algorithm is used to forecast shale gas production. The prediction accuracy of shale gas production level is higher than 95% based on the shale gas reservoirs in China. Forty-one wells are randomly selected to predict cumulative gas production using the optimal regression model. The proposed shale gas production evaluation framework overcomes too many assumptions of analytical or semi-analytical models and avoids huge computation cost and poor generalization for numerical modelling.