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

George Parapuram, Mehdi Mokhtari and Jalel Ben Hmida
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George Parapuram: Department of Petroleum Engineering, University of Louisiana at Lafayette, Lafayette, LA 70504, USA
Mehdi Mokhtari: Department of Petroleum Engineering, University of Louisiana at Lafayette, Lafayette, LA 70504, USA
Jalel Ben Hmida: Department of Mechanical Engineering, University of Louisiana at Lafayette, Lafayette, LA 70504, USA

Energies, 2018, vol. 11, issue 3, 1-26

Abstract: 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.

Keywords: Bakken Formation; unconventional; geomechanics; shear wave; artificial intelligence; predictive modeling; supervised machine learning; artificial neural networks (ANN); regression; hypothesis testing; cost-savings (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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