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- W4312509304 abstract "A new physics-guided AI machine learning method for petrophysical interpretation model development is described. The workflow consists of the following five constituents: (1) statistical tools such as correlation heatmaps are employed to select the best candidate input variables for the target petrophysical equations; (2) genetic programming based symbolic regression approach is used to fuse multiphysics measurements data for training the petrophysical prediction equations; (3) an optional ensemble modeling procedure is applied for maximally utilizing all available training data by integrating multiple instances of prediction equations objectively, which is especially useful for a small training dataset; (4) a means of obtaining conditional branching in prediction equations is enabled in symbolic regression to handle certain formation heterogeneity; and (5)a model discrimination framework is introduced to finalize the model selection based on mathematical complexity, physics complexity, and model performance. The efficacy of the five-constituents petrophysical interpretation development process is demonstrated on a dataset collected from six wells for a goal of obtaining formation resistivity factor (F) and permeability (k) equations for heterogenous carbonate reservoirs. This study demonstrates that this new petrophysical model development process has many advantages over traditional empirical methods or other commonly used AI methods." @default.
- W4312509304 created "2023-01-05" @default.
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- W4312509304 date "2022-06-11" @default.
- W4312509304 modified "2023-10-18" @default.
- W4312509304 title "Use of Symbolic Regression for Developing Petrophysical Interpretation Models" @default.
- W4312509304 doi "https://doi.org/10.30632/spwla-2022-0113" @default.
- W4312509304 hasPublicationYear "2022" @default.
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