Correlating Geologic and Seismic Data with Unconventional Resource Production Curves using Machine Learning
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.
R. G. Smith et al., "Correlating Geologic and Seismic Data with Unconventional Resource Production Curves using Machine Learning," Geophysics, vol. 84, no. 2, pp. O39-O47, Society of Exploration Geophysicists (SEG), Mar 2019.
The definitive version is available at https://doi.org/10.1190/GEO2018-0202.1
Geosciences and Geological and Petroleum Engineering
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)
Article - Journal
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01 Mar 2019