Accounting for subsurface uncertainty in enhanced geothermal systems to make more robust techno-economic decisions

Ahinoam Pollack and Tapan Mukerji

Applied Energy, 2019, vol. 254, issue C

Abstract: It has been estimated that Enhanced Geothermal Systems could supply 100 GWe (10%) of total electric capacity in the U.S. An Enhanced Geothermal System (EGS) is created by stimulating an impermeable hot rock, injecting cold water into the hot reservoir, and extracting the heated water to generate electricity. EGS projects are still not commercially feasible, however, due to many challenges, including subsurface uncertainty. There are many uncertain structural and geological features when creating an EGS. With uncertain reservoir properties, it is difficult to optimize decisions that will greatly improve EGS profitability. Currently, a common method of optimizing an EGS is choosing the most representative subsurface reservoir model and optimizing the engineering parameters for this single reservoir model, or Single-Model Optimization (SM-Opt). Due to availability of larger computational power, another feasible option is accounting for subsurface uncertainty by optimizing an EGS given an ensemble of reservoir models, or Multiple Model Optimization (MM-Opt). This option is less common in practice within the geothermal industry since it lags in harnessing computational power. This study compares these two methods for optimizing eight common EGS engineering decisions, including well configuration and fracture spacing. The decisions were optimized to maximize the Net Present Value (NPV) of an EGS. We have found that using SM-Opt, the optimal engineering decisions led to an EGS with a NPV estimate of $32.7 million. This contrasts with the MM-Opt results where the optimal engineering decisions led to a median NPV value of $11 million and a standard deviation of $15 million. This comparison illustrates how ignoring subsurface uncertainty and heterogeneity leads to over-optimistic NPV forecasts. For this study, the SM-Opt optimum decisions were similar to the robust decisions identified using MM-Opt. Yet, in contrast to SM-Opt, the MM-Opt workflow provided an analysis of the influential engineering parameters and a NPV uncertainty range, which was used to ensure decision robustness.

Keywords: Enhanced geothermal system; Sensitivity analysis; Hydraulic fracturing; Uncertainty quantification (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261919313534
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:254:y:2019:i:c:s0306261919313534

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2019.113666

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().