Cargando…

A hybrid data-driven solution to facilitate safe mud window prediction

Safe mud window (SMW) defines the allowable limits of the mud weights that can be used while drilling O&G wells. Controlling the mud weight within the SMW limits would help avoid many serious problems such as wellbore instability issues, loss of circulation, etc. SMW can be defined by the minimu...

Descripción completa

Detalles Bibliográficos
Autores principales: Gowida, Ahmed, Ibrahim, Ahmed Farid, Elkatatny, Salaheldin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9492774/
https://www.ncbi.nlm.nih.gov/pubmed/36131092
http://dx.doi.org/10.1038/s41598-022-20195-7
_version_ 1784793553444012032
author Gowida, Ahmed
Ibrahim, Ahmed Farid
Elkatatny, Salaheldin
author_facet Gowida, Ahmed
Ibrahim, Ahmed Farid
Elkatatny, Salaheldin
author_sort Gowida, Ahmed
collection PubMed
description Safe mud window (SMW) defines the allowable limits of the mud weights that can be used while drilling O&G wells. Controlling the mud weight within the SMW limits would help avoid many serious problems such as wellbore instability issues, loss of circulation, etc. SMW can be defined by the minimum mud weight below which shear failure (breakout) may occur (MW(BO)) and the maximum mud weight above which tensile failure (breakdown) may occur (MW(BD)). These limits can be determined from the geomechanical analysis of downhole formations. However, such analysis is not always accessible for most drilled wells. Therefore, in this study, a new approach is introduced to develop a new data-driven model to estimate the safe mud weight range in no time and without additional cost. New models were developed using an artificial neural network (ANN) to estimate both MW(BO) and MW(BD) directly from the logging data that are usually available for most wells. The ANN-based models were trained using actual data from a Middle Eastern field before being tested by an unseen dataset. The models achieved high accuracy exceeding 92% upon comparing the predicted and observed output values. Additionally, new equations were established based on the optimized ANN models’ weights and biases whereby both MW(BO) and MW(BD) can be calculated without the need for any complicated codes. Finally, another dataset from the same field was then used to validate the new equations and the results demonstrated the high robustness of the new equations to estimate MW(BO) and MW(BD) with a low mean absolute percentage error of 0.60% at maximum. So, unlike the costly conventional approaches, the newly developed equations would facilitate determining the SMW limits in a timely and economically effective way, with high accuracy whenever the logging data are available.
format Online
Article
Text
id pubmed-9492774
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-94927742022-09-23 A hybrid data-driven solution to facilitate safe mud window prediction Gowida, Ahmed Ibrahim, Ahmed Farid Elkatatny, Salaheldin Sci Rep Article Safe mud window (SMW) defines the allowable limits of the mud weights that can be used while drilling O&G wells. Controlling the mud weight within the SMW limits would help avoid many serious problems such as wellbore instability issues, loss of circulation, etc. SMW can be defined by the minimum mud weight below which shear failure (breakout) may occur (MW(BO)) and the maximum mud weight above which tensile failure (breakdown) may occur (MW(BD)). These limits can be determined from the geomechanical analysis of downhole formations. However, such analysis is not always accessible for most drilled wells. Therefore, in this study, a new approach is introduced to develop a new data-driven model to estimate the safe mud weight range in no time and without additional cost. New models were developed using an artificial neural network (ANN) to estimate both MW(BO) and MW(BD) directly from the logging data that are usually available for most wells. The ANN-based models were trained using actual data from a Middle Eastern field before being tested by an unseen dataset. The models achieved high accuracy exceeding 92% upon comparing the predicted and observed output values. Additionally, new equations were established based on the optimized ANN models’ weights and biases whereby both MW(BO) and MW(BD) can be calculated without the need for any complicated codes. Finally, another dataset from the same field was then used to validate the new equations and the results demonstrated the high robustness of the new equations to estimate MW(BO) and MW(BD) with a low mean absolute percentage error of 0.60% at maximum. So, unlike the costly conventional approaches, the newly developed equations would facilitate determining the SMW limits in a timely and economically effective way, with high accuracy whenever the logging data are available. Nature Publishing Group UK 2022-09-21 /pmc/articles/PMC9492774/ /pubmed/36131092 http://dx.doi.org/10.1038/s41598-022-20195-7 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
Gowida, Ahmed
Ibrahim, Ahmed Farid
Elkatatny, Salaheldin
A hybrid data-driven solution to facilitate safe mud window prediction
title A hybrid data-driven solution to facilitate safe mud window prediction
title_full A hybrid data-driven solution to facilitate safe mud window prediction
title_fullStr A hybrid data-driven solution to facilitate safe mud window prediction
title_full_unstemmed A hybrid data-driven solution to facilitate safe mud window prediction
title_short A hybrid data-driven solution to facilitate safe mud window prediction
title_sort hybrid data-driven solution to facilitate safe mud window prediction
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9492774/
https://www.ncbi.nlm.nih.gov/pubmed/36131092
http://dx.doi.org/10.1038/s41598-022-20195-7
work_keys_str_mv AT gowidaahmed ahybriddatadrivensolutiontofacilitatesafemudwindowprediction
AT ibrahimahmedfarid ahybriddatadrivensolutiontofacilitatesafemudwindowprediction
AT elkatatnysalaheldin ahybriddatadrivensolutiontofacilitatesafemudwindowprediction
AT gowidaahmed hybriddatadrivensolutiontofacilitatesafemudwindowprediction
AT ibrahimahmedfarid hybriddatadrivensolutiontofacilitatesafemudwindowprediction
AT elkatatnysalaheldin hybriddatadrivensolutiontofacilitatesafemudwindowprediction