Using Machine Learning to Predict Lost Circulation in the Rumaila Field, Iraq
Lost circulation costs are a significant expense in drilling oil and gas wells. Drilling anywhere in the Rumaila field, one the world's largest oilfields, requires penetrating the Dammam formation, which is notorious for lost circulation issues and thus a great source of information on lost circulation events. This paper presents a new, more precise model to predict lost circulation volumes, ECD and ROP in the Dammam formation. A larger data set, more systematic statistical approach, and a machine learning algorithm have produced statistical models that give a better prediction of the lost circulation volumes, ECD, and ROP than the previous models for events. This paper presents the new model, validates the key elements impacting lost circulation in the Dammam formation, and compares the predicted outcomes to those from the older model. The work previously in the literature provided a platform for predicting the severity of lost circulation incidents in the Dammam formation. Using the new models, the predictions closely track actual field incidents of lost circulation. When new lost circulation events were compared with predictions from the old and new models, the new model presented a much tighter prediction of events. Three equations for optimizing operations were developed from these models focusing on the elements that have the highest degree of impact. The total flow area of the nozzles was determined to be a significant factor in the ROP model indicating that nozzle size should be chosen carefully to achieve optimal ROP. Good modeling of projected lost circulation events can assist in evaluating the effectiveness of new treatments for lost circulation. The Dammam formation is a significant source of lost circulation in a major oilfield and warrants evaluation of the effectiveness of lost circulation treatments. These techniques can be applied to other fields and formations to better understand the economic impact of lost circulation and evaluate the effectiveness of various lost circulation mitigation efforts.
A. T. Al-Hameedi et al., "Using Machine Learning to Predict Lost Circulation in the Rumaila Field, Iraq," Proceedings of the SPE Asia Pacific Oil and Gas Conference and Exhibition (2018, Brisbane, Australia), Society of Petroleum Engineers (SPE), Oct 2018.
SPE Asia Pacific Oil and Gas Conference and Exhibition 2018, APOGCE 2018 (2018: Oct. 23-25, Brisbane, Australia)
Geosciences and Geological and Petroleum Engineering
Keywords and Phrases
Artificial intelligence; Forecasting; Infill drilling; Learning algorithms; Learning systems; Nozzles, Economic impacts; Flow area; Key elements; Lost circulation; Lost circulation treatment; Oil and gas well; Precise modeling; Statistical approach, Oil fields
International Standard Book Number (ISBN)
Article - Conference proceedings
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01 Oct 2018