Determination of Performance of Multiple-Fracture Horizontal Well by Incorporating Fracture-Fluid Leakoff
Bibliographic record
Abstract
Summary Multiple-fracture-horizontal-well (MFHW) technology plays a crucial role in production from less economically attractive reservoirs, through enhancing the well productivity. The formation around the fracture might be damaged considerably during fracturing processes because of the fracture-fluid leakoff into the reservoir. Different attempts have been made to achieve an optimal design for MFHWs; however, the effect of fracture-fluid leakoff has been neglected in most of these research investigations, leading to unrealistic and inaccurate results. This study aims to fill this knowledge gap. A new mathematical approach is introduced to evaluate the effect of the fracture-fluid-leakoff phenomenon on the fracture characteristics during hydraulic fracturing. The unified-fracture-design (UFD) concept is used in this research work to optimize the productivity of MFHWs where the direct boundary-element method (DBEM) is applied. The distributed-volumetric-sources (DVS) method, which offers a semianalytical response of a reservoir to closed outer boundaries with respect to a source, is also extended, and the results obtained from these two different techniques are compared. Then, the proposed methodology is applied to a synthetic case study to evaluate the influence of fracture-fluid leakoff on the productivity index (PI) and to obtain the fracture dimensions that result in the optimal productivity. It is concluded that leakoff leads to influx-pattern variation. Also, it is found that the optimal fracture for the leakoff case is shorter and wider at a constant proppant number, in contrast to the case without a leakoff event. This study proposes an accurate and reliable approach for productivity determination of MFHWs that can assist the petroleum industry to optimize hydraulic-fracturing operations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".