Insights to fracture stimulation design in unconventional reservoirs based on machine learning modeling

Abstract With rapid development of unconventional tight and shale reservoirs, considerable amounts of data sets are increasing rapidly. Data mining techniques are becoming attractive alternatives for well performance evaluation and optimization. This paper develops a comprehensive data mining process to evaluate well production performance in Montney Formations in western Canadian sedimentary basin. The general data visualization and statistical data evaluation are used to qualitatively and quantitatively evaluate the relationships between the stimulation parameters and first-year oil production. Then, the recursive feature elimination with cross validation (RFECV) is used to identify the most important factors on the first-year oil production in unconventional reservoirs. In addition, four commonly used supervised learning approaches including random forest (RF), adaptive boosting (AdaBoost), support vector machine (SVM), and neural network (NN) are compared to estimate the first-year well production. The results show that 6 features are the most important variables for constructing an accurate prediction model: well latitude, longitude, well true vertical depth (TVD), proppant pumped per well, well lateral length, and fluid injected per well. Compared to other algorithms, RF has the best prediction performance for the first-year oil production. Furthermore, such data-driven models are found to be very useful for reservoir engineers when designing hydraulic fracture treatments in Montney tight reservoirs.