Enhanced Oil Production Owing to Sand Flow in Conventional and Heavy-Oil Reservoirs
Bibliographic record
Abstract
Summary A coupled reservoir-geomechanics model is developed to simulate the enhanced production phenomena in both heavy-oil reservoirs (northwestern Canada) and conventional oil reservoirs (i.e., North Sea). The model is developed and implemented numerically by fully coupling an extended geomechanics model to a two-phase reservoir flow model. Both the enhanced production and the ranges of the enhanced zone are calculated, and the effects of solid production on oil recovery are analyzed. Field data for solid production and enhanced oil production, collected from about 40 wells in the Frog Lake area (Lloydminster, Canada), are used to validate the model for the cumulative sand and oil production. Our studies indicate that the enhanced oil production is mainly contributed (1) by the reservoir porosity and permeability improvement after a large amount of sand is produced, (2) by higher mobility of the fluid caused by the movement of the sand particles, and (3) by foamy oil flow. A relative permeability reduction after a certain period of production may result in a pressure-gradient increase, which can promote further sand flow. This process can further improve the absolute permeability and the overall sand/fluid slurry production. Our numerical results simulate the fact that sand production can reach up to 40% of total fluid production at the early production period and decline to a minimum level after the peak, generating a high-mobility zone with a negative skin near the wellbore. Such an improvement reduces the near-well pressure gradient so that the sanding potential is weakened, and it permits an easier path for the viscous oil to flow into the well. Our studies also suggest that the residual formation cement is a key factor for controlling the cumulative sand production, a crucial factor that determines the success of a cold production operation and improved well completion.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".