Modeling Lost-Circulation in Fractured Media Using Physics-Based Machine Learning
Type
Conference PaperKAUST Department
King Abdullah University of Science and TechnologyEarth Science and Engineering Program
Physical Science and Engineering (PSE) Division
Energy Resources and Petroleum Engineering Program
Ali I. Al-Naimi Petroleum Engineering Research Center (ANPERC)
Date
2022Permanent link to this record
http://hdl.handle.net/10754/678305
Metadata
Show full item recordAbstract
This work provides a novel machine learning approach to model lost-circulation in a naturally fractured formation. As input, the modeling tool requires the observed mud rate, mud physical properties, pressure flowing bottom-hole condition, if available. The deep neural network tool is trained using a physics-based model based on full-physics Cauchy momentum equation for non-Newtonian fluid, which can serve an accurate and quick estimate of the effective hydraulic aperture of natural fracture, and predictions for cumulative mud loss volume, and final stopping time leakage behavior. Such information can help take the preventive/corrective decision, such as the optimum drilling additive design for the lost circulation material. To our best knowledge, the proposed machine learning workflow is applied for the first time for modeling lost circulation events in fractured formations.Citation
Albattat, R., He, X., AlSinan, M., Kwak, H., & Hoteit, H. (2022). Modeling Lost-Circulation in Fractured Media Using Physics-Based Machine Learning. 83rd EAGE Annual Conference & Exhibition. https://doi.org/10.3997/2214-4609.202210204Conference/Event name
83rd EAGE Annual Conference & ExhibitionAdditional Links
https://www.earthdoc.org/content/papers/10.3997/2214-4609.202210204ae974a485f413a2113503eed53cd6c53
10.3997/2214-4609.202210204