Semi-Supervised Deep-Learning Applied To UK North Sea Well And Seismic Data

Yohei Nishitsuji, Russell Exley, Jalil Nasseri

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Semi-supervised deep-learning architectures provide a multi-layer, pattern recognition, approach that is powerful and ideally suited to the data rich environment that exists at the heart of the oil and gas industry. In this study we apply this technology in order to classify facies using elastic impedances from UK North Sea well and seismic data. The semi-supervised deep-learning method in this study uses a self-training strategy that combines both labelled and unlabelled data during the training phase so that classified data subsequently becomes part of the training dataset in the next iteration. This approach is ideal when the availability of labelled data is limited by practical constraints, which is often the case in subsurface geoscience. The resulting outputs of classified facies were visualised using elastic impedance cross-plots after application to a single training well from a North Sea oil discovery. To validate the result we upscaled the classification model to equivalent seismic data in order to compare the learning from the training well with two blind wells. The results indicate that semi-supervised deep-learning has the potential to accurately determine facies, including hydrocarbon distributions, in subsurface data at a field scale.
Original languageEnglish
Title of host publication1st EAGE/PESGB Workshop Machine Learning, 29-30 November, London, United Kingdom
Number of pages3
Publication statusPublished - 30 Nov 2018
Event1st EAGE/PESGB Workshop Machine Learning - London, United Kingdom
Duration: 29 Nov 201830 Nov 2018
Conference number: 1


Workshop1st EAGE/PESGB Workshop Machine Learning
Abbreviated titleML 2018
Country/TerritoryUnited Kingdom
Internet address


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