Evaluating proxies for the drivers of natural gas productivity using machine-learning models

Abstract

The extensive development of unconventional reservoirs using horizontal drilling and multistage hydraulic fracturing has generated large volumes of reservoir characterization and production data. The analysis of this abundant data using statistical methods and advanced machine learning techniques can provide data-driven insights into well performance. Most predictive modelling studies have focused on the impact that different well completion and stimulation strategies have on well production but have not fully exploited the available in-situ rock property data to determine its role in reservoir productivity. In this study, we utilized machine learning techniques to rank rock mechanical properties, microseismic attributes, and stimulation parameters in the order of their significance for predicting natural gas production from an unconventional reservoir. The data for this study came from a hydraulically fractured well in the Marcellus Shale in Monongalia County, West Virginia. The data classes included measurements aggregated by well completion stage that included: 1) gas production; 2) well-log-derived measurements including bulk density, elastic moduli, shear impedance, compressional impedance, brittleness, and gamma measurements; 3) microseismic attributes; 4) Long Period Long Duration (LPLD) event counts; 5) fracture counts; and 6) stimulation parameters that included fluid injection volume and average pumping pressure. In this study to identify observable proxies for the drivers of gas production we evaluated five commonly used machine learning approaches including Multivariate adaptive regression spline (MARS), Gaussian mixture model (GMM), Random forest (RF), Gradient boosting (GB), and Neural network (NN). We selected five variables including LPLD event count, seismogenic b-value, hydraulic diffusivity, cumulative moment, and fluid volume as the features most likely to impact gas productivity at the stage level in the study area. The data-driven selection of these parameters for their importance in determining gas production can help reservoir engineers design more effective hydraulic fracture treatments in the Marcellus Shale and other similar unconventional reservoirs.