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- W3157196326 abstract "Data-driven machine learning (ML) approaches are becoming very popular in analyzing facies, fractures, faults, rock properties, and fluid flow in subsurface characterization and modeling. Our reservoirs are becoming more data-rich due to the advent of new drilling, completion, and sensor technologies. Modeling of these variables from such data-rich reservoirs is a complex multivariate, multiscale, and multidisciplinary problem that we can handle with ML algorithms. In this chapter, we will learn about the fundamental steps in deploying ML models to solve our problems. Although there are several ML models available, including both traditional algorithms and deep learning algorithms, some steps are very similar. The selection of a particular algorithm over other depends on the data and the problem itself, complexity, interpretability, time, and cost. In this chapter, we focus on the nature of the problems and provide a systematic guide to building, evaluating, and explaining these data-driven models, irrespective of the algorithms." @default.
- W3157196326 created "2021-05-10" @default.
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- W3157196326 date "2021-01-01" @default.
- W3157196326 modified "2023-09-27" @default.
- W3157196326 title "Basic Steps in Machine Learning-Based Modeling" @default.
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- W3157196326 doi "https://doi.org/10.1007/978-3-030-71768-1_3" @default.
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