An Artificially Intelligent Technique to Generate Synthetic Geomechanical Well Logs for the Bakken Formation

Artificially intelligent and predictive modelling of geomechanical properties is performed by creating supervised machine learning data models utilizing artificial neural networks (ANN) and will predict geomechanical properties from basic and commonly used conventional well logs such as gamma ray, a...

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Main Authors: George Parapuram, Mehdi Mokhtari, Jalel Ben Hmida
Format: Article
Language:English
Published: MDPI AG 2018-03-01
Series:Energies
Subjects:
Online Access:http://www.mdpi.com/1996-1073/11/3/680
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author George Parapuram
Mehdi Mokhtari
Jalel Ben Hmida
author_facet George Parapuram
Mehdi Mokhtari
Jalel Ben Hmida
author_sort George Parapuram
collection DOAJ
description Artificially intelligent and predictive modelling of geomechanical properties is performed by creating supervised machine learning data models utilizing artificial neural networks (ANN) and will predict geomechanical properties from basic and commonly used conventional well logs such as gamma ray, and bulk density. The predictive models were created by following the approach on a large volume of data acquired from 112 wells containing the Bakken Formation in North Dakota. The studied wells cover a large surface area of the formation containing the five main producing counties in North Dakota: Burke, Mountrail, McKenzie, Dunn, and Williams. Thus, with a large surface area being analyzed in this research, there is confidence with a high degree of certainty that an extensive representation of the Bakken Formation is modelled, by training neural networks to work on varying properties from the different counties containing the Bakken Formation in North Dakota. Shear wave velocity of 112 wells is also analyzed by regression methods and neural networks, and a new correlation is proposed for the Bakken Formation. The final goal of the research is to achieve supervised artificial neural network models that predict geomechanical properties of future wells with an accuracy of at least 90% for the Upper and Middle Bakken Formation. Thus, obtaining these logs by generating it from statistical and artificially intelligent methods shows a potential for significant improvements in performance, efficiency, and profitability for oil and gas operators.
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spelling doaj.art-4522d6902e2540559a8d87836aee18702022-12-22T01:58:21ZengMDPI AGEnergies1996-10732018-03-0111368010.3390/en11030680en11030680An Artificially Intelligent Technique to Generate Synthetic Geomechanical Well Logs for the Bakken FormationGeorge Parapuram0Mehdi Mokhtari1Jalel Ben Hmida2Department of Petroleum Engineering, University of Louisiana at Lafayette, Lafayette, LA 70504, USADepartment of Petroleum Engineering, University of Louisiana at Lafayette, Lafayette, LA 70504, USADepartment of Mechanical Engineering, University of Louisiana at Lafayette, Lafayette, LA 70504, USAArtificially intelligent and predictive modelling of geomechanical properties is performed by creating supervised machine learning data models utilizing artificial neural networks (ANN) and will predict geomechanical properties from basic and commonly used conventional well logs such as gamma ray, and bulk density. The predictive models were created by following the approach on a large volume of data acquired from 112 wells containing the Bakken Formation in North Dakota. The studied wells cover a large surface area of the formation containing the five main producing counties in North Dakota: Burke, Mountrail, McKenzie, Dunn, and Williams. Thus, with a large surface area being analyzed in this research, there is confidence with a high degree of certainty that an extensive representation of the Bakken Formation is modelled, by training neural networks to work on varying properties from the different counties containing the Bakken Formation in North Dakota. Shear wave velocity of 112 wells is also analyzed by regression methods and neural networks, and a new correlation is proposed for the Bakken Formation. The final goal of the research is to achieve supervised artificial neural network models that predict geomechanical properties of future wells with an accuracy of at least 90% for the Upper and Middle Bakken Formation. Thus, obtaining these logs by generating it from statistical and artificially intelligent methods shows a potential for significant improvements in performance, efficiency, and profitability for oil and gas operators.http://www.mdpi.com/1996-1073/11/3/680Bakken Formationunconventionalgeomechanicsshear waveartificial intelligencepredictive modelingsupervised machine learningartificial neural networks (ANN)regressionhypothesis testingcost-savings
spellingShingle George Parapuram
Mehdi Mokhtari
Jalel Ben Hmida
An Artificially Intelligent Technique to Generate Synthetic Geomechanical Well Logs for the Bakken Formation
Energies
Bakken Formation
unconventional
geomechanics
shear wave
artificial intelligence
predictive modeling
supervised machine learning
artificial neural networks (ANN)
regression
hypothesis testing
cost-savings
title An Artificially Intelligent Technique to Generate Synthetic Geomechanical Well Logs for the Bakken Formation
title_full An Artificially Intelligent Technique to Generate Synthetic Geomechanical Well Logs for the Bakken Formation
title_fullStr An Artificially Intelligent Technique to Generate Synthetic Geomechanical Well Logs for the Bakken Formation
title_full_unstemmed An Artificially Intelligent Technique to Generate Synthetic Geomechanical Well Logs for the Bakken Formation
title_short An Artificially Intelligent Technique to Generate Synthetic Geomechanical Well Logs for the Bakken Formation
title_sort artificially intelligent technique to generate synthetic geomechanical well logs for the bakken formation
topic Bakken Formation
unconventional
geomechanics
shear wave
artificial intelligence
predictive modeling
supervised machine learning
artificial neural networks (ANN)
regression
hypothesis testing
cost-savings
url http://www.mdpi.com/1996-1073/11/3/680
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