Prescriptive analytics AIDS completion optimization in unconventionals

This paper discusses a prescriptive analytics framework to optimize completions in the Permian Basin. The methodology involves data processing, ingestion into databases, and data cleansing; application of automated machine learning (AutoML) to generate an accurate machine-learning model; and numerical optimization of decision parameters to minimize an economic objective. Constructing a Pareto front enabled decision makers to select a strategy that minimized cost without sacrificing too much of the initial 12-month oil production. © 2020 Society of Petroleum Engineers. All rights reserved.