Improving Subsurface Characterisation with ‘Big Data’ Mining and Machine Learning

Rachel E. Brackenridge*, Vasily Demyanov, Oleg Vashutin, Ruslan Nigmatullin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

Large databases of legacy hydrocarbon reservoir and well data provide an opportunity to use modern data mining techniques to improve our understanding of the subsurface in the presence of uncertainty and improve predictability of reservoir properties. A data mining approach provides a way to screen dependencies in reservoir and fluid data and enable subsurface specialists to estimate absent properties in partial or incomplete datasets. This allows for uncertainty to be managed and reduced. An improvement in reservoir characterisation using machine learning results from the capacity of machine learning methods to detect and model hidden dependencies in large multivariate datasets with noisy and missing data. This study presents a workflow applied to a large basin‐scale reservoir characterization database. The study aims to understand the dependencies between reservoir attributes in order to allow for predictions to be made to improve the data coverage. The machine learning workflow comprises the following steps: (i) exploratory data analysis; (ii) detection of outliers and data partitioning into groups showing similar trends using clustering; (iii) identification of dependencies within reservoir data in multivariate feature space with self‐organising maps; and (iv) feature selection using supervised learning to identify relevant properties to use for predictions where data are absent. This workflow provides an opportunity to reduce the cost and increase accuracy of hydrocarbon exploration and production in mature basins.

Original languageEnglish
Article number1070
JournalEnergies
Volume15
Issue number3
Early online date31 Jan 2022
DOIs
Publication statusPublished - 1 Feb 2022

Keywords

  • Big data
  • Hydrocarbon exploration
  • Machine learning
  • Multivariant analysis
  • Reservoir
  • Subsurface characterisation
  • Supervised learning
  • Unsupervised learning

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Fuel Technology
  • Energy Engineering and Power Technology
  • Energy (miscellaneous)
  • Control and Optimization
  • Electrical and Electronic Engineering

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