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Application of machine learning in predicting oil rate decline for Bakken shale oil wells

Commercial reservoir simulators are required to solve discretized mass-balance equations. When the reservoir becomes heterogeneous and complex, more grid blocks can be used, which requires detailed and accurate reservoir information, for e.g. porosity, permeability, and other parameters that are not...

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Autores principales: Bhattacharyya, Subhrajyoti, Vyas, Aditya
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9519931/
https://www.ncbi.nlm.nih.gov/pubmed/36171237
http://dx.doi.org/10.1038/s41598-022-20401-6
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author Bhattacharyya, Subhrajyoti
Vyas, Aditya
author_facet Bhattacharyya, Subhrajyoti
Vyas, Aditya
author_sort Bhattacharyya, Subhrajyoti
collection PubMed
description Commercial reservoir simulators are required to solve discretized mass-balance equations. When the reservoir becomes heterogeneous and complex, more grid blocks can be used, which requires detailed and accurate reservoir information, for e.g. porosity, permeability, and other parameters that are not always available in the field. Predicting the EUR (Estimated Ultimate Recovery) and rate decline for a single well can therefore take hours or days, making them computationally expensive and time-consuming. In contrast, decline curve models are a simpler and speedier option because they only require a few variables in the equation that can be easily gathered from the wells' current data. The well data for this study was gathered from the Montana Board of Oil and Gas Conservation's publicly accessible databases. The SEDM (Stretched Exponential Decline Model) decline curve equation variables specifically designed for unconventional reservoirs variables were correlated to the predictor parameters in a random oil field well data set. The study examined the relative influences of several well parameters. The study's novelty comes from developing an innovative machine learning (ML) (random forest (RF)) based model for fast rate-decline and EUR prediction in Bakken Shale oil wells. The successful application of this study relies highly on the availability of good quality and quantity of the dataset.
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spelling pubmed-95199312022-09-30 Application of machine learning in predicting oil rate decline for Bakken shale oil wells Bhattacharyya, Subhrajyoti Vyas, Aditya Sci Rep Article Commercial reservoir simulators are required to solve discretized mass-balance equations. When the reservoir becomes heterogeneous and complex, more grid blocks can be used, which requires detailed and accurate reservoir information, for e.g. porosity, permeability, and other parameters that are not always available in the field. Predicting the EUR (Estimated Ultimate Recovery) and rate decline for a single well can therefore take hours or days, making them computationally expensive and time-consuming. In contrast, decline curve models are a simpler and speedier option because they only require a few variables in the equation that can be easily gathered from the wells' current data. The well data for this study was gathered from the Montana Board of Oil and Gas Conservation's publicly accessible databases. The SEDM (Stretched Exponential Decline Model) decline curve equation variables specifically designed for unconventional reservoirs variables were correlated to the predictor parameters in a random oil field well data set. The study examined the relative influences of several well parameters. The study's novelty comes from developing an innovative machine learning (ML) (random forest (RF)) based model for fast rate-decline and EUR prediction in Bakken Shale oil wells. The successful application of this study relies highly on the availability of good quality and quantity of the dataset. Nature Publishing Group UK 2022-09-28 /pmc/articles/PMC9519931/ /pubmed/36171237 http://dx.doi.org/10.1038/s41598-022-20401-6 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Bhattacharyya, Subhrajyoti
Vyas, Aditya
Application of machine learning in predicting oil rate decline for Bakken shale oil wells
title Application of machine learning in predicting oil rate decline for Bakken shale oil wells
title_full Application of machine learning in predicting oil rate decline for Bakken shale oil wells
title_fullStr Application of machine learning in predicting oil rate decline for Bakken shale oil wells
title_full_unstemmed Application of machine learning in predicting oil rate decline for Bakken shale oil wells
title_short Application of machine learning in predicting oil rate decline for Bakken shale oil wells
title_sort application of machine learning in predicting oil rate decline for bakken shale oil wells
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9519931/
https://www.ncbi.nlm.nih.gov/pubmed/36171237
http://dx.doi.org/10.1038/s41598-022-20401-6
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