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.
Overview
A typical way to assess the performance of an oil well is to compute the total cost per barrel of oil produced at ultimate recovery. However, in cash-constrained operating environments, including many unconventional plays, other measures, such as $/bbl of oil produced in the first year, may also be used.
