Geomechanics and Engineering

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LSTM algorithm to determine the state of minimum horizontal stress during well logging operation

  • Arsalan Mahmoodzadeh (Department of Civil Engineering, University of Halabja) ;
  • Seyed Mehdi Seyed Alizadeh (Petroleum Engineering Department, Australian University) ;
  • Adil Hussein Mohammed (Department of Communication and Computer Engineering, Faculty of Engineering, Cihan University-Erbil) ;
  • Ahmed Babeker Elhag (Department of Civil Engineering, College of Engineering, King Khalid University) ;
  • Hawkar Hashim Ibrahim (Department of Civil Engineering, College of Engineering, Salahaddin University-Erbil) ;
  • Shima Rashidi (Department of Computer Science, College of Science and Technology, University of Human Development)
  • Received : 2022.10.16
  • Accepted : 2023.05.10
  • Published : 2023.07.10
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Abstract

Knowledge of minimum horizontal stress (Shmin) is a significant step in determining full stress tensor. It provides crucial information for the production of sand, hydraulic fracturing, determination of safe mud weight window, reservoir production behavior, and wellbore stability. Calculating the Shmin using indirect methods has been proved to be awkward because a lot of data are required in all of these models. Also, direct techniques such as hydraulic fracturing are costly and time-consuming. To figure these problems out, this work aims to apply the long-short-term memory (LSTM) algorithm to Shmin time-series prediction. 13956 datasets obtained from an oil well logging operation were applied in the models. 80% of the data were used for training, and 20% of the data were used for testing. In order to achieve the maximum accuracy of the LSTM model, its hyper-parameters were optimized significantly. Through different statistical indices, the LSTM model's performance was compared with with other machine learning methods. Finally, the optimized LSTM model was recommended for Shmin prediction in the well logging operation.

Keywords

Acknowledgement

The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through large group Research Project under grant number RGP2/357/44.

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