Coupled Reservoir-Geomechanics Model with Sand Erosion for Sand Rate and Enhanced Production Prediction
Bibliographic record
Abstract
Abstract A fully coupled reservoir-geomechanics model is developed to simulate the enhanced production phenomena both in heavy-oil reservoirs (i.e. Northwestern Canada) and conventional oil reservoirs (i.e. North Sea). The model is implemented numerically by fully coupling an extended geomechanics model to a two-phase reservoir flow model. A sand erosion model is postulated after the onset of sand production, which is determined based on the degree of plastic deformation inside the reservoir formation calculated by the coupled reservoir-geomechanics model. Both the enhanced production and the ranges of the enhanced or sanding zone are calculated, the effect of solid production on oil recovery and enhancement are analyzed. Field data for solid production and enhanced oil production from Frog Lake (Lloydminster, Canada) are used to validate the model for the sand rate and sand production. Our studies indicate that the enhanced oil production can be contributed by either (1) the a large-scale reservoir formation mobility improvement, (i.e. wormhole type model), by (2) a higher fluid velocity due to the movement of the sand particles according to the modified Darcy's flow, or by (3) an effective well radius increase or negative skin development due to sand erosion if formation does not permit an extensive erosional zone. Such an improvement on productivity reduces the near well pressure gradient so that the sanding potential is weakened, but permits an easier path for oil to flow into the well due to an enhanced permeability. Two-phase flow can affect pressure gradient and formation residual cohesion due to capillary pressure buildup. Indirectly, production enhancement strategy can be controlled by the water saturation distribution and development, as the success and economic value of a field operation can depend on if sand production can be induced or not. Such an analogy can also be used for a completion strategy by allowing a certain amount of sand production before gravel pack in high flow-rate reservoir.
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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 | 0.000 | 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) | 0.000 | 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".