Small-Scale or Large-Scale Machine Learning Proxy Models? a Real Field Case Study

R. Amiri Kolajoobi, C. MacBeth, J. Landa

Research output: Contribution to conferencePaperpeer-review


This study explores the potential of machine learning, specifically artificial neural networks (ANN), for proxy modelling in geoscience and reservoir engineering. It highlights the limitations of large-scale ANN models and emphasizes the preference for small, fit-for-purpose proxy models in these fields. The study introduces a step-by-step workflow for constructing efficient small-scale ANN proxy models and demonstrates its practical application in estimating water saturation for a North Sea oil field, achieving high accuracy and generalization through comprehensive feature engineering and data analysis. It is a two-layer ANN with just forty input features estimating the water saturation in all 123,000 active grid cells of the 3D simulation model ten times faster than the numerical flow simulator.
Original languageEnglish
Number of pages5
Publication statusPublished - Mar 2024
Event4th EAGE Digitalization Conference & Exhibition 2024 - Paris, France
Duration: 25 Mar 202427 Mar 2024


Conference4th EAGE Digitalization Conference & Exhibition 2024
Abbreviated titleEAGE Digital 2024


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