Synthesizing Nuclear Magnetic Resonance (NMR) Outputs for Clastic Rocks Using Machine Learning Methods, Examples from North West Shelf and Perth Basin, Western Australia

نویسندگان

چکیده

A nuclear magnetic resonance (NMR) logging tool can provide important rock and fluid properties that are necessary for a reliable reservoir evaluation. Pore size distribution based on T2 relaxation time resulting permeability among those parameters cannot be provided by conventional tools. For wells drilled before the 1990s many recent there is no NMR data available due to availability cost, respectively. This study used large database of combinable (CMR) assess performance several well-known machine learning (ML) methods generate some tool’s outputs clastic rocks using typical well-logs as inputs. outputs, such clay bound water (CBW), irreducible pore (known bulk volume irreducible, BVI), producible free index, FFI), logarithmic mean (T2LM), saturation (Swirr), from Coates SDR models were generated in this study. The well logs collected 14 Western Australia (WA) within 3 offshore basins. About 80% points training validation purposes 20% whole was kept blind set with involvement process check validity ML methods. comparison results shows Adaptive Boosting, known AdaBoost model, has given most impressive predict CBW, FFI, permeability, T2LM, SWirr R2 more than 0.9. accuracy model dataset suggests approach high accuracy.

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ژورنال

عنوان ژورنال: Energies

سال: 2022

ISSN: ['1996-1073']

DOI: https://doi.org/10.3390/en15020518