Enhancing Tight Gas Recovery Through Hydraulic Fracture Treatment Design Optimization
Bibliographic record
Abstract
Tight and deep unconventional gas reservoirs are becoming targets for development but the conventional approach to develop them is not feasible. In most cases, they cannot be produced economically without hydraulic fracturing. There have been much progress in the overall hydraulic fracturing procedures to stimulate tight formation, but there is still a lack in treatment-design optimization. Some of the currently available commercial software do not take into consideration several key parameters and associated realistic constraints. An integrated model to optimize hydraulic fracture treatment has been developed to enhance gas production and net present value with minimum treatment cost. This model couples with the industry experience with hydraulic fracture mechanics. Unlike commercial software, important design parameters are included. The free design variables are randomly varied during optimization. The overall hydraulic fracturing design problem is viewed as a multi-objective and multivariate system design problem, which recognizes complex interactions between a hydraulically coupled fracture geometry model, a hydrocarbon production model and an investment-return cash flow model. The integrated model has been successfully applied to a hypothetical deeper tight gas formation to demonstrate its merits. The optimum treatment design indicates a 300% increment in production over 10 years at a lower cost compared to production from non-fractured tight gas sand. This optimization scheme presents a decision support system, which provides a goal-oriented optimum design in a conflicting environment. Key words : Stimulation; Enhanced recovery; Fracture optimization; Tight gas; Treatment design
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".