Machine Learning Clustering of Reservoir Heterogeneity with Petrophysical and Production Data

Dmitry Konoshonkin, Gleb Shishaev, Ivan Matveev, Aleksandra Volkova, Valeriy Rukavishnikov, Vasily Demyanov, Boris Belozerov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Reservoir development decisions strongly depend on our understanding on reservoir heterogeneity, which is often subject to sparse and conflicting data, interpretational bias and constraints imposed by the modelling assumptions. The work tackles a challenging task of accurately and quickly identifying and describing uncertainty in the spatial distribution of reservoir heterogeneity derived from geological well data and with respect to a geological concept. We propose a metric based machine-learning approach to identify and describe spatial trends in reservoir heterogeneity/facies property distribution using wireline and production data.
We demonstrate how the proposed method can help to partition reservoir heterogeneity and discover and verify spatial trends for a real mature producing field in the Western Siberia. The obtained clustering of reservoir facies based on the wireline logs (alpha-SP) demonstrated a good agreement with the reservoir zonation based on manual log interpretation and the geological concept. Clustering based on individual well production profiles has confirmed the reservoir partitioning and matched some of the reservoir features aligned with the prevailing geological concept. The outcome of the proposed method helps to improve the facies distribution model by integrating the discovered spatial trends into a geostatistical model and account for uncertainty in the depositional scenario that is difficult to quantify based on manual interpretation.
Original languageEnglish
Title of host publicationSPE Europec Featured at 82nd EAGE Conference and Exhibition
PublisherSociety of Petroleum Engineers
ISBN (Electronic)9781613997123
DOIs
Publication statusPublished - 2020
EventSPE Europec Featured at 82nd EAGE Conference and Exhibition - Amsterdam, Netherlands
Duration: 8 Dec 202011 Dec 2020

Conference

ConferenceSPE Europec Featured at 82nd EAGE Conference and Exhibition
CountryNetherlands
CityAmsterdam
Period8/12/2011/12/20

ASJC Scopus subject areas

  • Geophysics
  • Geochemistry and Petrology

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    Konoshonkin, D., Shishaev, G., Matveev, I., Volkova, A., Rukavishnikov, V., Demyanov, V., & Belozerov, B. (2020). Machine Learning Clustering of Reservoir Heterogeneity with Petrophysical and Production Data. In SPE Europec Featured at 82nd EAGE Conference and Exhibition [SPE-200614-MS] Society of Petroleum Engineers. https://doi.org/10.2118/200614-MS