Preventing Heel Dominated Fractures in Horizontal Well Refracturing
Bibliographic record
Abstract
Abstract Horizontal-well refracturing is important in the development of unconventional plays to improve the productivity of the refraced well and to protect parent wells from the fractures propagating from the nearby child wells. In the field, heel-biased fracture propagation is frequently observed in refracturing treatments, leaving a large fraction the wellbore understimulated. In this paper, we explain the mechanisms of heel-biased fracture propagation and suggest strategies to mitigate such behavior. We model refracturing operations accounting for reservoir geomechanics, wellbore hydrodynamics, completion strategies, as well as near-wellbore effects. A coupled reservoir geomechanics simulator is used to calculate the poroelastic stress changes in the reservoir due to production. Then, a multi-fracture simulator with newly developed wellbore fluid and proppant transport model is applied to simulate the refracturing process. A field refrac case is simulated and the simulation results show good agreement with field diagnostics including proppant distribution among multiple fractures. The simulation results help us understand the mechanisms behind heel-biased fracture propagation in refractured wells and allow us to investigate strategies to improve refrac treatment efficiency. The mechanisms affecting treatment distribution in refracs are investigated from two perspectives: the non-uniform pore pressure and stress profile in the reservoir, and the wellbore hydrodynamics. The latter perspective studies the fluid and proppant transport and separation in the wellbore. With the long wellbore length and the large number of open fractures, the impact of wellbore hydrodynamics on the refrac treatment distribution is very important. Wellbore friction is believed to be a minor reason for the heel-biased treatment distribution compared to proppant inertia, which could lead to the pre-mature screen out of the toe-side clusters. Simulation results show that using small refrac stages with more frequent diverting agent application can help avoid over-stimulating the dominant fractures and promote more uniform fracture propagation. Strategies that moderate the proppant inertia could also help mitigate the heel-biased trend of refrac treatment distribution.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".