Intelligent data-driven decision-making to mitigate or stop lost circulation

<p>”Lost circulation is a challenging problem in the oil and gas industry. Each year, millions of dollars are spent to mitigate or stop this problem. The aim of this work is to utilize machine learning and other intelligent solutions to help to make better decision to mitigate or stop lost circulation. A detailed literature review on the applications of decision tree analysis, expected monetary value, and artificial neural networks in the oil and gas industry was provided. Data for more than 3000 wells were gathered from many sources around the world. Detailed economics and probability analyses for lost circulation treatments’ strategies were conducted for three formations in southern Iraq which are the Dammam, Hartha, and Shuaiba formations.</p> <p>Multiple machine learning methods such as support vector machine, decision trees, logistic regression, artificial neural networks, and ensemble trees were used to create models that can predict lost circulation and recommend the best lost circulation treatment based on the type of loss and reason of loss. The results showed that the created models can predict lost circulation and recommend the best lost circulation strategy within a reasonable margin of error. The created models can be used globally which avoids the shortcoming in the literature. Intelligence solutions and machine learning have proven their applicability to solve complicated problems and make better future decisions. With the large data available in the oil and gas industry, these methods can help the decision-makers to make better future decisions that will save time and money”--Abstract, page iv.</p>

Name thesis:degree_name

Ph. D. in Petroleum Engineering

Grantor

Missouri University of Science and Technology

Author dc:creator

• Alkinani, Husam Hasan

• Artificial Intelligence
• Drilling Fluid
• Lost Circulation
• Mud Loss
• Petroleum Engineering

Repository record dc:identifier

https://scholarsmine.mst.edu/doctoral_dissertations/3122

OAI identifier oai:identifier

oai:scholarsmine.mst.edu:doctoral_dissertations-4127