Estimation of Drilling Mud Weight for Iranian Wells Using Deep-Learning Techniques

Khazaei, Aref; Radfar, Reza; Toloie Eshlaghy, Abbas

doi:10.22050/ijogst.2021.138844

Document Type : Research Paper

Authors

¹ Ph.D. Candidate, Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran.

² Professor, Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran.

³ Professor, Industrial Management, Islamic Azad University, Science and Research Branch, Tehran, Iran.

https://doi.org/10.22050/ijogst.2021.138844

Abstract

Iran is one of the largest oil and gas producers in the world. Intelligent manufacturing approaches can lead to better performance and lower costs of the well drilling process. One of the most critical issues during the drilling operation is the wellbore stability. Instability of wellbore can occur at different stages of a well life and inflict heavy financial and time damage on companies. A controllable factor can prevent these damages by selecting a proper drilling mud weight. This research presents a drilling mud weight estimator for Iranian wells using deep-learning techniques. Our Iranian data set only contains 900 samples, but efficient deep-learning models usually need large amounts of data to obtain acceptable performance. Therefore, the samples of two data sets related to the United Kingdom and Norway fields are also used to extend our data set. Our final data set has contained more than half-million samples that have been compiled from 132 wells of three fields. Our presented mud weight estimator is an artificial neural network with 5 hidden layers and 256 nodes in each layer that can estimate the mud weight for new wells and depths with the mean absolute error (MAE) of smaller than ±0.039 pound per gallon (ppg). In this research, the presented model is challenged in real-world conditions, and the results show that our model can be reliable and efficient in the real world.

Highlights

Collecting an extensive data set with various features of wells obtained by merging the data of the United Kingdom, Norway, and Iran fields;
Presenting an accurate mud weight estimator using a deep-learning model;
Challenging the presented model in real-world conditions and providing solutions to improving its performance.

Keywords

20.1001.1.23452412.2021.10.3.6.9

Main Subjects

Petroleum Engineering – Drilling

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Estimation of Drilling Mud Weight for Iranian Wells Using Deep-Learning Techniques

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Volume 10, Issue 3 - Serial Number 36July 2021Pages 83-98

Volume 10, Issue 3 - Serial Number 36
July 2021
Pages 83-98