[URTeC 2023] Understanding the Drivers of Parent-child Depletion: A Machine Learning Approach​ (ID 3862321)

Abstract:

Every year, an increasingly larger fraction of drilling is done on pads or in zones where wells are already present. These existing wells have implications for the hydrocarbon production of any new developments, and therefore also the economic value of engaging in this activity. Here, we use a non-linear and multivariate machine learning approach to provide descriptive evidence of the effects of existing well production on infill wells and segregate that impact into the contributions of individual features. We find that the percentage of total reserves produced by existing wells before an infill well is brought online is the single strongest factor in determining the relative performance of the infill well as compared to the parent. Distance to the closest parent produces a nonlinear effect on child production, and one which is mediated by the percent of reserves already generated by the parent well. Finally, we observe mixed results for the influence of geology, which warrants further investigation.