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.

Details

Title
Mud loss estimation using machine learning approach
Author
Abo Taleb T Al-Hameedi 1 ; Alkinani, Husam H 1 ; Dunn-Norman, Shari 1 ; Flori, Ralph E 1 ; Hilgedick, Steven A 1 ; Amer, Ahmed S 2 ; Alsaba, Mortadha 3 

 Missouri University of Science and Technology, Rolla, Missouri, USA 
 Newpark Technology Center, Katy, Texas, USA 
 Australian College of Kuwait, Kuwait City, Kuwait 
Pages
1339-1354
Publication year
2019
Publication date
Jun 2019
Publisher
Springer Nature B.V.
ISSN
21900558
e-ISSN
21900566
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2138908360
Copyright
Journal of Petroleum Exploration and Production Technology is a copyright of Springer, (2018). All Rights Reserved., © 2018. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.