Optimizing Economic Number of Transverse Fractures in Horizontal Well: A Systematic Design for Maximum Tight Gas Recovery
Bibliographic record
Abstract
Over the last two decades, the worldwide demand for energy has been met with substantial increase in the oil supply which causes fall in oil price. However, the price of gas has been stabled despite increased demand for gas as a major source of energy. The production from gigantic conventional reservoirs has also reduced which has led to the dependence on current reserves to meet the demand. This increase in demand for gas has led to the increase in activities of research and development with an objective to explore and exploit unconventional resources as an economic and effective cost. Hydraulic fracturing has been proven to be one of the most viable means used to exploit the unconventional resources (tight gas and shale gas formations). Thus, evaluating the performance of the well post-fracturing is necessary to determine the economic viability of the recovery. Inaccurate evaluation of the post-fracturing can lead to either overestimation or underestimation of the design performance particularly from transversely fractured horizontal well. This work includes convergence skin effect that occurs at every intersection of fractures and horizontal section of the well which can account for wide variation of the post treatment in the field from the simulation model. The variation in the skin is a function of fracture conductivity and the number of transverse fractures. This work has developed a hydraulic fracture optimization model which shows the optimal design point, that is, the optimal number of transverse fractures estimated from the economic analysis and gives optimal production rate. This optimal number of transverse fractures estimated from this work is cost effective. This model can lead to an increase in accuracy of optimum design.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".