Characterization and Modeling of Enhanced Geothermal Systems Using Methods Developed for Unconventional Hydrocarbon Reservoirs
Bibliographic record
Abstract
Abstract Advanced drilling and completion technologies that have been applied to the development of unconventional hydrocarbon reservoirs are showing promise in their application to enhanced geothermal systems (EGS). Similarly, hydraulic fracture and reservoir characterization methods developed for the former could be adopted to the latter to aid with EGS pilot design and development optimization. This study explores the use of post-fracture pressure decay (PFPD) analysis to obtain fracture and reservoir properties from fracturing stages implemented in the injection and production wells at the Utah FORGE EGS site. A primary challenge in the application of PFPD to this dataset is the shortness of the PFPD shut-in times (generally < 30 minutes). In addition, one stage in the injection well, and effectively all stages in the production well, which were implemented after the fracturing stages in the injection well, are refracturing cases. All stages analyzed for the injection and production well exhibited a Zone 1 signature, which corresponds to leakoff from an open fracture prior to the fracture walls coming in contact. This zone was analyzed for several stages, when data quality allowed, using previously developed straight-line analysis methods, for estimates of effective ISIP, fracturing fluid efficiency, total fracture area, and reservoir permeability. However, because minimum in-situ stress and reservoir pressure could not be determined independently for each stage, the absolute values of each derived fracture and reservoir property are uncertain. Nonetheless relative property estimates can still be useful for evaluating stimulation effectiveness. Effective ISIP is the most easily derived value from Zone 1 analysis, but estimation for most stages was still challenged by the fact that the contact point at the end of Zone 1 (i.e., the point at which fracture walls come into contact) was not observed for any stage. While properties were also derived for the refracturing stages, confidence in the results are low, given that the current PFPD methods do not properly account for the physics of the refracturing process. This study demonstrates that PFPD analysis can be applied to EGS systems for obtaining information critical to hydraulic fracture design and development planning. However, shut-in times long enough to observe at least the first contact point (at the end of Zone 1) are generally considered desirable to obtain confident estimates of effective ISIP, and to constrain minimum in-situ stress estimates, on a per stage basis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".