Real-time prediction of Poisson’s ratio from drilling parameters using machine learning tools

Abstract Rock elastic properties such as Poisson’s ratio influence wellbore stability, in-situ stresses estimation, drilling performance, and hydraulic fracturing design. Conventionally, Poisson’s ratio estimation requires either laboratory experiments or derived from sonic logs, the main concerns o...

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Main Authors:	Osama Siddig, Hany Gamal, Salaheldin Elkatatny, Abdulazeez Abdulraheem
Format:	article
Language:	EN
Published:	Nature Portfolio 2021
Subjects:	Medicine R Science Q
Online Access:	https://doaj.org/article/bbc7573bbca84d8eb4cad94631d87cc1
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spelling	oai:doaj.org-article:bbc7573bbca84d8eb4cad94631d87cc12021-12-02T17:41:29ZReal-time prediction of Poisson’s ratio from drilling parameters using machine learning tools10.1038/s41598-021-92082-62045-2322https://doaj.org/article/bbc7573bbca84d8eb4cad94631d87cc12021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-92082-6https://doaj.org/toc/2045-2322Abstract Rock elastic properties such as Poisson’s ratio influence wellbore stability, in-situ stresses estimation, drilling performance, and hydraulic fracturing design. Conventionally, Poisson’s ratio estimation requires either laboratory experiments or derived from sonic logs, the main concerns of these methods are the data and samples availability, costs, and time-consumption. In this paper, an alternative real-time technique utilizing drilling parameters and machine learning was presented. The main added value of this approach is that the drilling parameters are more likely to be available and could be collected in real-time during drilling operation without additional cost. These parameters include weight on bit, penetration rate, pump rate, standpipe pressure, and torque. Two machine learning algorithms were used, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). To train and test the models, 2905 data points from one well were used, while 2912 data points from a different well were used for model validation. The lithology of both wells contains carbonate, sandstone, and shale. Optimization on different tuning parameters in the algorithm was conducted to ensure the best prediction was achieved. A good match between the actual and predicted Poisson’s ratio was achieved in both methods with correlation coefficients between 0.98 and 0.99 using ANN and between 0.97 and 0.98 using ANFIS. The average absolute percentage error values were between 1 and 2% in ANN predictions and around 2% when ANFIS was used. Based on these results, the employment of drilling data and machine learning is a strong tool for real-time prediction of geomechanical properties without additional cost.Osama SiddigHany GamalSalaheldin ElkatatnyAbdulazeez AbdulraheemNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Osama Siddig Hany Gamal Salaheldin Elkatatny Abdulazeez Abdulraheem Real-time prediction of Poisson’s ratio from drilling parameters using machine learning tools
description	Abstract Rock elastic properties such as Poisson’s ratio influence wellbore stability, in-situ stresses estimation, drilling performance, and hydraulic fracturing design. Conventionally, Poisson’s ratio estimation requires either laboratory experiments or derived from sonic logs, the main concerns of these methods are the data and samples availability, costs, and time-consumption. In this paper, an alternative real-time technique utilizing drilling parameters and machine learning was presented. The main added value of this approach is that the drilling parameters are more likely to be available and could be collected in real-time during drilling operation without additional cost. These parameters include weight on bit, penetration rate, pump rate, standpipe pressure, and torque. Two machine learning algorithms were used, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). To train and test the models, 2905 data points from one well were used, while 2912 data points from a different well were used for model validation. The lithology of both wells contains carbonate, sandstone, and shale. Optimization on different tuning parameters in the algorithm was conducted to ensure the best prediction was achieved. A good match between the actual and predicted Poisson’s ratio was achieved in both methods with correlation coefficients between 0.98 and 0.99 using ANN and between 0.97 and 0.98 using ANFIS. The average absolute percentage error values were between 1 and 2% in ANN predictions and around 2% when ANFIS was used. Based on these results, the employment of drilling data and machine learning is a strong tool for real-time prediction of geomechanical properties without additional cost.
format	article
author	Osama Siddig Hany Gamal Salaheldin Elkatatny Abdulazeez Abdulraheem
author_facet	Osama Siddig Hany Gamal Salaheldin Elkatatny Abdulazeez Abdulraheem
author_sort	Osama Siddig
title	Real-time prediction of Poisson’s ratio from drilling parameters using machine learning tools
title_short	Real-time prediction of Poisson’s ratio from drilling parameters using machine learning tools
title_full	Real-time prediction of Poisson’s ratio from drilling parameters using machine learning tools
title_fullStr	Real-time prediction of Poisson’s ratio from drilling parameters using machine learning tools
title_full_unstemmed	Real-time prediction of Poisson’s ratio from drilling parameters using machine learning tools
title_sort	real-time prediction of poisson’s ratio from drilling parameters using machine learning tools
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/bbc7573bbca84d8eb4cad94631d87cc1
work_keys_str_mv	AT osamasiddig realtimepredictionofpoissonsratiofromdrillingparametersusingmachinelearningtools AT hanygamal realtimepredictionofpoissonsratiofromdrillingparametersusingmachinelearningtools AT salaheldinelkatatny realtimepredictionofpoissonsratiofromdrillingparametersusingmachinelearningtools AT abdulazeezabdulraheem realtimepredictionofpoissonsratiofromdrillingparametersusingmachinelearningtools
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Real-time prediction of Poisson’s ratio from drilling parameters using machine learning tools

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