Improving Subsurface Characterisation with ‘Big Data’ Mining and Machine Learning
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...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-01-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/15/3/1070 |
| _version_ | 1797487966285725696 |
|---|---|
| author | Rachel E. Brackenridge Vasily Demyanov Oleg Vashutin Ruslan Nigmatullin |
| author_facet | Rachel E. Brackenridge Vasily Demyanov Oleg Vashutin Ruslan Nigmatullin |
| author_sort | Rachel E. Brackenridge |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T23:56:20Z |
| format | Article |
| id | doaj.art-4c09eaf2bfcd4a13b78e8374f4b70f16 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T23:56:20Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-4c09eaf2bfcd4a13b78e8374f4b70f162023-11-23T16:24:39ZengMDPI AGEnergies1996-10732022-01-01153107010.3390/en15031070Improving Subsurface Characterisation with ‘Big Data’ Mining and Machine LearningRachel E. Brackenridge0Vasily Demyanov1Oleg Vashutin2Ruslan Nigmatullin3School of Geosciences, University of Aberdeen, Aberdeen AB24 3FX, UKSchool of Energy, Geoscience, Infrastructure and Society, Heriot Watt University, Riccarton, Edinburgh EH14 4AS, UKWood Mackenzie, 125009 Moscow, RussiaWood Mackenzie, 125009 Moscow, RussiaLarge 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.https://www.mdpi.com/1996-1073/15/3/1070big dataunsupervised learningsupervised learningmultivariant analysismachine learninghydrocarbon exploration |
| spellingShingle | Rachel E. Brackenridge Vasily Demyanov Oleg Vashutin Ruslan Nigmatullin Improving Subsurface Characterisation with ‘Big Data’ Mining and Machine Learning Energies big data unsupervised learning supervised learning multivariant analysis machine learning hydrocarbon exploration |
| title | Improving Subsurface Characterisation with ‘Big Data’ Mining and Machine Learning |
| title_full | Improving Subsurface Characterisation with ‘Big Data’ Mining and Machine Learning |
| title_fullStr | Improving Subsurface Characterisation with ‘Big Data’ Mining and Machine Learning |
| title_full_unstemmed | Improving Subsurface Characterisation with ‘Big Data’ Mining and Machine Learning |
| title_short | Improving Subsurface Characterisation with ‘Big Data’ Mining and Machine Learning |
| title_sort | improving subsurface characterisation with big data mining and machine learning |
| topic | big data unsupervised learning supervised learning multivariant analysis machine learning hydrocarbon exploration |
| url | https://www.mdpi.com/1996-1073/15/3/1070 |
| work_keys_str_mv | AT rachelebrackenridge improvingsubsurfacecharacterisationwithbigdataminingandmachinelearning AT vasilydemyanov improvingsubsurfacecharacterisationwithbigdataminingandmachinelearning AT olegvashutin improvingsubsurfacecharacterisationwithbigdataminingandmachinelearning AT ruslannigmatullin improvingsubsurfacecharacterisationwithbigdataminingandmachinelearning |