Reservoir and Fracture Characterization for Enhanced Geothermal Systems: A Case Study Using Multifractured Wells at the Utah Frontier Observatory for Research in Geothermal Energy Site
Bibliographic record
Abstract
Summary For enhanced geothermal systems (EGS), multistage hydraulic fracturing along a deviated or horizontal well is a key technology used to create a high-conductivity fracture network between injection and production wells in deep, low-permeability geothermal reservoirs. The purpose of the created fracture network is to allow for the efficient transfer of fluid, heated by the geothermal reservoir, from the injection to the production well; therefore, well spacing (between injection and production wells) and hydraulic fracturing must be designed not only to promote connectivity between well pairs but also to mitigate thermal short-circuiting and thermal breakthrough. Analysis of post-fracture pressure decay (PFPD) data measured after each stage of a hydraulic fracturing treatment can be used to provide critical reservoir and fracture parameters required for well and hydraulic fracturing design optimization; this method provides a low-cost alternative and complementary approach to in-situ observation techniques, such as core-through experiments, fiber optics, or image logs in offset wells. Until now, PFPD has primarily been applied to multifractured horizontal wells (MFHWs) completed in low-permeability hydrocarbon reservoirs. The goal of this study is therefore to develop a methodology to estimate fracture and reservoir parameters using stage-by-stage PFPD data associated with EGS projects. An analytical model is proposed herein to estimate fracturing fluid efficiency, fracture length, average fracture aperture, average fracture conductivity, and reservoir permeability for different possible fracture geometries in EGS reservoirs. PFPD data collected for three hydraulic fracture stages in the injection well at the Utah Frontier Observatory for Research in Geothermal Energy (FORGE) site were analyzed to demonstrate the practical application of the proposed method. The results of this study indicate that, due to the presence of natural fractures in the target (granitic) reservoir, the hydraulic fracturing treatment (using slickwater) in the openhole section resulted in low fracturing fluid efficiency and small hydraulic fractures. In contrast, hydraulic fracturing treatments conducted in the perforated casedhole wellbore resulted in higher fracturing fluid efficiency and created larger hydraulic fractures even with smaller injected volumes. The results of the PFPD analysis were confirmed using a Formation MicroScanner image log and microseismic data collected during each stage of hydraulic fracturing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".