Spatio-temporal Models For Big Data And Applications On Unconventional Production Evaluation

Marshal Wigwe, E. S. Bougre, Marshall Watson, Alberto Giussani

Research output: Contribution to conferencePaper

Abstract

With the abundance of big data in the oil and gas industry, it can be sufficient to treat and solve petroleum engineering problems using data analytics. Modern data analytic techniques, statistical and machine learning algorithms have received widespread applications for solving such problems, particularly in unconventional formations. As we face the problem of parent-child well interactions, well spacing, and depletion concerns, it becomes necessary to model the effect of geology, completion design, and well parameters on production using models that can capture both spatial and temporal variability of the covariates on the response variable. We can accomplish this idea using well-formulated spatio-temporal (ST) models.<br>In this paper, we present a multi-basin study of production performance evaluation and applications of spatio-temporal (ST) models for oil and gas data. We analyzed dataset from 10,077 horizontal wells in five unconventional formations in the US: Bakken, Marcellus,
Original languageEnglish
DOIs
StatePublished - Jul 20 2020

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