Maximizing the Effective Fracture Half-Length to Influence Well Spacing
Bibliographic record
Abstract
Abstract The key to the success of a tight-gas field development program in a fluvial environment is to understand the reservoir's deliverability and what the optimum fracture half-length is as a function of geological setting and stress state. The application and appropriate modification of basin best practices and the application of technology for reservoir characterization can shorten the learning curve of an operator in the development of a basin. Numerous completion strategies (Limited Entry, high rate limited entry, and various Pin-point Stimulation Techniques) were implemented with an appropriate data collection strategy to evaluate and compare well performance. Micro seismic data, tracer logs, and pump-in data were used to calibrate and constrain appropriate fracture evaluation models (P3D and 3D). Rate-transient production analysis techniques, together with statistical data techniques were incorporated to evaluate stimulation techniques (proppant & fluid volumes) and to validate the differences/ similarities observed between micro-seismic and fracture-propagation model predicted lengths. This paper demonstrates how reservoir characterization and completion understanding via the use of calibrated fracture propagation models and production analysis tools have enabled the evaluation of the technology used and the acceleration of the learning curve to achieve significant impact on gas production rates and downhole flowing pressures.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".