Type:
Research Report, Student Research
Link:
Authors:
Aleksei Titov | Jack Norbeck | Sireesh Dadi | Katharine Voller | Steve Fercho | Emma McConville | Mark Woitt | Camden Lang | Saurabh Agarwal | Christian Gradl | Timothy Latimer
Citation:
Titov, A., Norbeck, J., Dadi, S., Voller, K., Woitt, M., Fercho, S., McConville, E., Lang, C., Agarwal, S., Gradl, C., & Latimer, T. (2023). Case Study: Completion and Well Placement Optimization Using Distributed Fiber Optic Sensing in Next-Generation Geothermal Projects. Proceedings of the 11th Unconventional Resources Technology Conference.
Abstract:
Fervo Energy is developing a commercial next-generation geothermal project in northern Nevada, adopting many unconventional technologies, such as horizontal drilling, plug-and-perf stimulation, and reservoir diagnostics with distributed fiber optic sensing (DFOS). We successfully installed permanent fiber optic cables cemented behind casing in three wells. The recorded DFOS data include in-well and cross-well distributed temperature (DTS), acoustic (DAS), and strain (DSS) sensing data. We evaluated the adaptability of DFOS to geothermal applications and showcased that DFOS is a beneficial tool for optimizing multi-stage completions, characterizing the stimulated reservoir volume, and determining well placement in geothermal reservoirs.
Beginning in January 2022, we executed a three-well drilling campaign, including a vertical monitoring well and a pair of horizontal wells that form a geothermal injection and production well doublet system. The wells targeted a high-temperature (350 °F to 375 °F), low-permeability geothermal reservoir in a mixed metasedimentary and granitic formation. In-well and cross-well (in vertical well) DFOS data were acquired during the stimulation treatment performed on the first horizontal well. These data allowed for characterizing the plug-and-perf completion design, evaluating fracture initiation and flow allocation, and guided the decision on the second horizontal well placement (producer) to establish a flow path between the wells via an induced fracture network.
First, DTS data from the vertical well were used to validate a thermal model to place the first horizontal well. During stimulation of the first horizontal well, in-well DAS and DTS data provided valuable insights into fracture initiation, slurry and proppant distributions at the cluster level, and thermal warmback behavior. This information helped validate and improve our completion design for the following stimulation. Integrated analysis of cross-well strain data recorded in a vertical well and two- well DAS-based microseismic constrained stimulated reservoir volume (SRV) height and length and guided our decision on the second horizontal (producer) well placement. Finally, cross-well DSS data recorded in the producer was used to understand the flow path between two wells during an injection test of the injector.
Keywords:
unconventional, optimization, induced seismicity, azimuth, sampling