Forecasting the performance of shale gas wells using machine learning

Abstract

Utilization of real data to develop data-driven models in the petroleum industry has gained momentum in the past decade. The challenges related to modeling unconventional reservoirs has been recognized as the driving force behind this change in approach. Data-driven models help to enhance operations, increase efficiency and save time. In the meantime, several numerical reservoir simulators are used for modeling and forecasting the performance of shale gas wells. However, these models are computationally expensive and the simulators could indirectly face with difficulties in forecasting performance for the unconventional shale reservoirs comparing to conventional ones. This study employs a data analytics approach to investigate and gain understanding into the main driver parameters that influence the gas production performance in unconventional reservoirs (i.e. cumulative gas production after one year). The dataset utilized in this study is acquired from SPE Data Repository and consist of 53 wells (SPE, 2021). The study essentially utilized two primary methods, namely exploratory data analysis (EDA) and predictive data analytics modeling. Through the utilization of exploratory data analysis (EDA), the correlation between each reservoir and operational parameter with the cumulative gas production (Gp) is clearly identified. A number of reservoir and operational parameters display a strictly monotonic relationship with the gas production. Out of all variables, gas saturation was the variable, which demonstrated the strongest correlation. Furthermore, predictive data-analytics models based on statistical and machine learning algorithms were developed to forecast the cumulative gas production after 1 year. Among the five conducted models, extreme gradient boosting machine (XGBoost) proved to be the optimal technique for forecasting gas production, as it yielded the highest Coefficient of Determination (R2) and the lowest Root Mean Square Error (RMSE). Finally, an analysis of variable importance was conducted to determine the key variables, which have the highest predictive power in forecasting gas production performance in unconventional shale reservoirs. The operational parameters such as the number of stages, lateral length and bottom perforation along with reservoir properties such as gas saturation, porosity and thickness are more dominant than the other reservoir and operational parameters. Gas saturation is the most critical parameter, which is considered as the key driver of forecasting the gas production. The findings of this study will be beneficial in the design and development similar forecasting modelling projects.