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Open AccessArticle

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

1
Department of Petroleum Engineering, University of Louisiana at Lafayette, Lafayette, LA 70504, USA
2
Department of Mechanical Engineering, University of Louisiana at Lafayette, Lafayette, LA 70504, USA
*
Author to whom correspondence should be addressed.
Energies 2018, 11(3), 680; https://doi.org/10.3390/en11030680
Received: 4 February 2018 / Revised: 12 March 2018 / Accepted: 14 March 2018 / Published: 17 March 2018
(This article belongs to the Special Issue Unconventional Natural Gas (UNG) Recoveries 2018)
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. View Full-Text
Keywords: Bakken Formation; unconventional; geomechanics; shear wave; artificial intelligence; predictive modeling; supervised machine learning; artificial neural networks (ANN); regression; hypothesis testing; cost-savings Bakken Formation; unconventional; geomechanics; shear wave; artificial intelligence; predictive modeling; supervised machine learning; artificial neural networks (ANN); regression; hypothesis testing; cost-savings
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MDPI and ACS Style

Parapuram, G.; Mokhtari, M.; Ben Hmida, J. An Artificially Intelligent Technique to Generate Synthetic Geomechanical Well Logs for the Bakken Formation. Energies 2018, 11, 680.

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