Characterization of Wormhole Growth and Its Applications for CHOPS Wells Using History Matching
Bibliographic record
Abstract
Abstract A wormhole dynamic growth model has been developed and incorporated with a commercial reservoir simulator, i.e., CMG, to characterize wormhole growth for cold heavy oil production with sand (CHOPS) processes and extends its application to a field well. More specifically, geomechanics analysis associated with a collapsed pore and its throat structure has been performed to quantify the sand production. Then, a sand failure criterion and a four-direction pressure difference analysis are respectively proposed to determine the sand production rate and the potential direction of wormhole generation and growth. By considering the uncertainties associated with the parameters involved in the wormhole growth model, history matching is respectively conducted to estimate the critical breakdown pressure, superficial area of the collapsed throats, and coefficients of the permeability-porosity correlation for a field CHOPS well. Subsequently, the dynamic wormhole growth model has been validated with a synthetic model and then extended to a CHOPS well for determining its wormhole network. It is found from both the synthetic case and field application that the newly proposed technique can be used to determine the corresponding wormhole network as a function of time by history matching the production profile. Furthermore, the history matched models can also be utilized to optimize the following enhanced oil recovery processes such as cyclic solvent injection.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".