Abstract
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, equivalent circulation density (ECD), and rate of penetration (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 presented by Al-Hameedi et al. (http://www.onepetro.org, 2017a; http://www.AADE.org, 2017b) 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.
Recommended Citation
A. T. Al-Hameedi et al., "Mud Loss Estimation using Machine Learning Approach," Journal of Petroleum Exploration and Production Technology, vol. 9, no. 2, pp. 1339 - 1354, Springer Verlag, Jun 2019.
The definitive version is available at https://doi.org/10.1007/s13202-018-0581-x
Department(s)
Geosciences and Geological and Petroleum Engineering
Keywords and Phrases
Iraq; Lost circulation; Machine learning; Partial least squares
International Standard Serial Number (ISSN)
2190-0558; 2190-0566
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2019 Springer Verlag, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 Jun 2019
