Representative Geological Heterogeneity Indicators for Geothermal Reservoir Simulation

Conference Paper (2026)
Author(s)

Y. Chen (TU Delft - Civil Engineering & Geosciences)

G. Rongier (TU Delft - Civil Engineering & Geosciences)

D. Voskov (Stanford University, TU Delft - Civil Engineering & Geosciences)

A. Daniilidis (TU Delft - Civil Engineering & Geosciences)

Research Group
Applied Geology
URL related publication
https://www.earthdoc.org/content/papers/10.3997/2214-4609.2026101293 Final published version
More Info
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Publication Year
2026
Language
English
Research Group
Applied Geology
Volume number
2026
Article number
1293
Publisher
European Association of Geoscientists & Engineers
Event
87th EAGE Annual Conference & Exhibition (2026-06-08 - 2026-06-11), Aberdeen, United Kingdom
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Abstract

Fluvial clastic sequence is the typical reservoir rock to develop the project of Direct Use Geothermal Systems (DUGS). However, limited and unknown subsurface data bring challenges to accurately characterize the geology of the fluvial depositional environment and to capture the details of the sand body distribution and connectivity. In this work, we utilize the process-based modeling (PBM) approach (Flumy) and object-based modeling approach (OBM) (Fluvsim) to develop a robust framework to evaluate the heterogeneity representation of the fluvial depositional environment. An ensemble of geological models with different global net-to-gross ratios (N/G) is created using PBM and OBM. The generated high-resolution models aim to cover a wide range of N/G from 30% to 80% which is typical for Dutch geothermal sediments. For the given models, a static analysis using the Lorenz coefficient shows a large range of variability in heterogeneity levels for both PBM and OBM models. This wide range of heterogeneity levels leads to a broad variation in thermal breakthrough time during 100 years of thermal production. We also find that the OBM and PBM converge to nearly the same distribution of predicted thermal breakthrough time when the overall N/G is 80%.

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