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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".