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.
Key Words
long-short-term memory; machine learning; minimum horizontal stress; optimization; well logging
Address
Arsalan Mahmoodzadeh — Department of Civil Engineering, University of Halabja, Halabja, Kurdistan Region, Iraq
Seyed Mehdi Seyed Alizadeh — Petroleum Engineering Department, Australian University, West Mishref, Kuwait
Adil Hussein Mohammed — Department of Communication and Computer Engineering, Faculty of Engineering, Cihan University-Erbil, Kurdistan Region, Iraq
Ahmed Babeker Elhag — Department of Civil Engineering, College of Engineering, King Khalid University, Abha 61413, Saudi Arabia
Hawkar Hashim Ibrahim — Department of Civil Engineering, College of Engineering, Salahaddin University-Erbil, 44002 Erbil, Kurdistan Region, Iraq
Shima Rashidi — Department of Computer Science, College of Science and Technology, University of Human Development, Sulaymaniyah, Kurdistan Region, Iraq
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