Prediction of Drilling Fluid Temperature Window and Formation of Gas Hydrates During Drilling in Deep Water Using Machine Learning Algorithms

Document Type : Original Article

Authors
Mohammad Dousti 1
Elnaz Khodapanah 2
1 M.Sc., Faculty of Petroleum and Natural Gas Engineering, Sahand University of Technology, Tabriz, PO. Box: 5331817634, Iran
2 Associate Professor, Faculty of Petroleum and Natural Gas Engineering, Sahand University of Technology, Tabriz, PO. Box: 5331817634, Iran
Abstract
Drilling in gas hydrate formations in deep water poses significant risks and challenges. This research aims to determine the temperature window for drilling fluid in deep water drilling, after examining the relations of predicting the temperature of gas hydrate formation, using two machine learning algorithms, "polynomial regression in Python and multilayer perceptron in MATLAB". The predictions are used to establish the required temperature range for the drilling fluid. Among the examined methods for predicting gas hydrate formation temperatures, the Saharkhizan relationship, with an average relative error of 0.49, demonstrated the highest accuracy. Furthermore, for six gas compounds with varying specific gravities, the gas compound with a specific gravity of 0.61 yielded an R² value of 0.9743 using the polynomial regression algorithm. Similarly, the gas compound with a specific gravity of 0.65 showed the highest accuracy when using the multilayer perceptron algorithm.

Keywords

Subjects

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Iranian Journal of Gas Engineering
Volume 11, Issue 1 - Serial Number 19
September 2024
Pages 71-86

  • Receive Date 05 June 2024
  • Revise Date 20 July 2024
  • Accept Date 10 August 2024