Application of Integrated Advanced Diagnostics and Modeling to Improve Hydraulic Fracture Stimulation Analysis and Optimization
Bibliographic record
Abstract
Abstract Economic development of unconventional resources relies heavily on the effectiveness of propped hydraulic fracture stimulation treatments (HFS or "fracs"). Non-stimulated and/or under-stimulated reservoir continues to be a critical industry concern. Mitigation is expensive and may require refracturing and/or additional wells to be drilled. Techniques to monitor and diagnose the geometry of HFS are limited and analysis typically has large uncertainties. This paper summarizes multiple datasets to demonstrate how complementary diagnostics significantly reduce uncertainties in their analysis, help to calibrate frac models and improve completion design of multi-stage wells. Diagnostics utilized in the datasets include: fiber optic distributed sensing (acoustic & temperature), non-radioactive tracers and production logs. We found that integrating these complementary diagnostics with other subsurface and well information not only confirmed that actual frac heights were different than intended in about half of the monitored stages, but also provided new insights that allow us to modify the HFS treatment design to better match the desired geometries. These diagnostics were used to history match and calibrate our frac models, allowing us to extrapolate results from the few wells with diagnostics to additional wells in the field. Statistics are also provided for the datasets including: percentages of perforation clusters and net sand treated to demonstrate the potential opportunity for improved stimulations and reserves recovery.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".