Applications of Underbalanced Fishbone Drilling for Improved Recovery and Reduced Carbon Footprint in Unconventional Plays

Fishbone Drilling (FbD) consists of drilling several micro-holes in different directions from the main vertical or deviated wellbore. Similar to multilateral micro-hole drilling, FbD may be used to enhance hydrocarbon production in naturally fractured formations or in refracturing operations by interconnecting the existing natural fractures. When combined with underbalanced drilling using a coiled tubing rig, FbD enhances the production further by easing the natural flow of the hydrocarbon from the reservoir to the wellbore. The design aspects of the Fishbones include determining the number, length, distance between the branches, and the angle of sidetracking of the branches from the main borehole. In addition, the design of efficient drill string components to suit the FbD conditions are another important design aspect in FbD technology development. Examples of this include a high-performance small, diameter downhole motor and the use of High Voltage Pulsed Discharge (HVPD) plasma shock waves at different pulse frequencies and wave pressures to impose shear forces on the formation to break it more easily. This paper will present a comprehensive review of the FbD technology, including some of its current applications and design aspects. The possibility of using FbD in conjunction with hydraulic fracturing to boost production by creating a network of connected fractures will be discussed, and some of its technical and economic benefits and challenges will be compared

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

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

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

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