Correlating Geological and Seismic Data with Unconventional Resource Production Curves using Machine Learning
We present a machine learning workflow that incorporates geophysical (seismic attributes) and geological data, as well as engineering completion parameters, into a model for predicting well production curves. The study area is in southwest Texas in the lower Eagle Ford formation. We make use of functional principal component analysis to summarize the well production time series, and random forest models to predict the functional principal components with geological, geophysical and engineering data as predictors. 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 Geological and Seismic Data with Unconventional Resource Production Curves using Machine Learning," Proceedings of the 88th SEG International Exposition and Annual Meeting (2018, Anaheim, CA), pp. 2111 - 2115, Society of Exploration Geophysicists (SEG), Oct 2019.
The definitive version is available at https://doi.org/10.1190/segam2018-2993476.1
88th Society of Exploration Geophysicists International Exposition and Annual Meeting, SEG 2018 (Oct. 14-19, Anaheim, CA)
Geosciences and Geological and Petroleum Engineering
Center for Research in Energy and Environment (CREE)
Second Research Center/Lab
Center for High Performance Computing Research
Keywords and Phrases
Artificial intelligence; Decision trees; Forecasting; Geology; Geophysical prospecting; Learning systems; Resource valuation; Seismology; Time series; Time series analysis, Engineering data; Functional principal component analysis; Predictive modeling; Principal Components; Production curve; Seismic attributes; Unconventional resources; Well production, Principal component analysis
Article - Conference proceedings
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01 Oct 2019