Design and Performance Analysis of Dry Gas Fishbone Wells for Lower Carbon Footprint

Author:

Ouadi Habib1ORCID,Laalam Aimen1,Hassan Amjed2ORCID,Chemmakh Abderraouf1ORCID,Rasouli Vamegh3,Mahmoud Mohamed2ORCID

Affiliation:

1. Department of Petroleum Engineering, College of Engineering and Mines, University of North Dakota, Grand Forks, ND 58202, USA

2. Petroleum Engineering Department, College of Petroleum Engineering & Geosciences, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia

3. Department of Energy & Petroleum Engineering, College of Engineering and Physical Sciences, University of Wyoming, Laramie, WY 82071, USA

Abstract

Multilateral well drilling technology has recently assisted the drilling industry in improving borehole contact area and reducing operation time, while maintaining a competitive cost. The most advanced multilateral well drilling method is Fishbone drilling (FbD). This method has been utilized in several hydrocarbon fields worldwide, resulting in high recovery enhancement and reduced carbon emissions from drilling. FbD involves drilling several branches from laterals and can be considered as an alternative method to hydraulic fracturing to increase the stimulated reservoir volume. However, the expected productivity of applying a Fishbone well from one field to another can vary due to various challenges such as Fishbone well design, reservoir lithology, and accessibility. Another challenge is the lack of existing analytical models and the effect of each Fishbone parameter on the cumulative production, as well as the interaction between them. In this paper, analytical and empirical productivity models were modified for FbD in a dry gas reservoir. The modified analytical model showed a higher accuracy with respect to the existing model. It was also compared with the modified empirical model, which proved its higher accuracy. Finally, machine learning algorithms were developed to predict FbD productivity, which showed close results with both analytical and empirical models.

Funder

North Dakota Industrial Commission

Publisher

MDPI AG

Subject

General Medicine

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