Correlating Geologic and Seismic Data with Unconventional Resource Production Curves using Machine Learning

Abstract

Predicting well production in unconventional oil and gas settings is challenging due to the combined influence of engineering, geologic, and geophysical inputs on well productivity. We have developed a machine-learning workflow that incorporates geophysical and geologic data, as well as engineering completion parameters, into a model for predicting well production. The study area is in southwest Texas in the lower Eagle Ford Group. We make use of a time-series method known as functional principal component analysis to summarize the well-production time series. Next, we use random forests, a machine-learning regression technique, in combination with our summarized well data to predict the full time series of well production. The inputs to this model are geologic, geophysical, and engineering data.We are then able to predict the well-production time series, with 65%-76% accuracy. This method incorporates disparate data types into a robust, predictive model that predicts well production in unconventional resources.

Department(s)

Geosciences and Geological and Petroleum Engineering

Research Center/Lab(s)

Center for High Performance Computing Research

Keywords and Phrases

Decision trees; Forecasting; Geologic models; Machine learning; Principal component analysis; Resource valuation; Seismology; Time series, Functional principal component analysis; Predictive modeling; Production curve; Regression techniques; Time series method; Unconventional oil and gas; Unconventional resources; Well productivity, Time series analysis, correlation; machine learning; oil production; oil well; principal component analysis; regression analysis; seismic data; time series analysis

International Standard Serial Number (ISSN)

0016-8033;1942-2156

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2019 Society of Exploration Geophysicists (SEG), All rights reserved.

Publication Date

01 Mar 2019

Link to Full Text

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