Enhanced Production in Horizontal Wells by the Cavity Failure Well Completion
Bibliographic record
Abstract
Abstract High viscosity and weak cementation existing in heavy oil reservoirs present great challenge to us in rich heavy oil reservoirs in Northwestern Canada for production and well completion. On one hand, we attempt to increase the exposure of the reservoir to the well so that we can maximize the production potential. On the other, we must select an adequate strategy for well completion in order to maintain the integrity of the well during production. A well completion strategy by cavity-failure mechanism has been used for coalbed methane and heavy oil production during SAGD. The success of their previous operations leads us to investigate the feasibility of such a well completion strategy in both cold production and other enhanced production process in poorly consolidated heavy oil reservoirs. A coupled reservoir-geomechanics model is developed. A black-oil model is fully coupled to a Mohr-Coulomb type elastoplastic geomechanics model. An open hole condition with slotted liner is simulated. The wellbore pressure is rapidly reduced to create a massive dilation zone near the well so that the intact porosity can be mobilized. Consequently, an enhanced zone near the slotted liner with a higher permeability can be generated, leading to negative skin factor during the production. The field conditions reported in Cold Lake are used to evaluate our study and simulations.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".