Impact of Formation Dilation–Recompaction on Cyclic Steam Stimulation
Bibliographic record
Abstract
Summary An integrated modeling of the cyclic steam stimulation (CSS) at the Peace River heavy-oil/oil-sand deposits in Alberta, Canada, is challenging because of the presence of compositional gradient, faulting, and bottomwater pockets, and the variations in the oil viscosity, rock dilation, fracturing, and the pay-zone-thickness variation. Both gravity and viscosity are marked by declining quality with depth, biodegradation, and compartmentalization. The high oil viscosity and low water mobility at Peace River cause low initial injectivity. High injectivity during the CSS is achieved by high-pressure injection to fail the formation mechanically and trigger fracturing and pore deformation. Moreover, the pore dilation/recompaction triggers relative permeability hysteresis. History matching of high steam injectivities is challenging when reasonable fracture lengths and rock compressibilities are used. To match injectivities, most reservoir simulations have used either a larger compressibility (“spongy-rock” approach) or long fractures. The spongy-rock approach predicts a steady increase in injection pressure, whereas during the early time of the injection cycles, injection pressures increase and then level off for most of the cycle. We describe the enhancements made in an iteratively coupled geomechanical–flow model to incorporate the modeling of both pore deformation and relative permeability hysteresis to match the injection pressures, steam injectivity, and oil/water productions from CSS at Peace River that are otherwise difficult to reproduce. The geomechanical model explains surface heave and high injectivity caused by dilation attributable to shear failure, increase in pore pressure/formation compressibility, and decrease in effective stress. A dilation pressure is specified, below which the behavior is elastic and above which a higher compressibility is used. Above a maximum porosity, further dilation is not permitted. Also, the hysteresis model calculates gridblock relative permeabilities that lie on or between the imbibition/drainage curves, making it possible to use the laboratory-derived two-phase oil–water relative permeabilities and still match the field-measured water- and oil-production volumes. By combining an iteratively coupled reservoir–geomechanical model for the CSS with stochastic workflows, including the Latin-hypercube design (LHD) and response-surface methodology (RSM), the impacts of dilation/recompaction factors (fracturing pressure, maximum injection pressure, dilation pressure, recompaction pressure, and formation compressibility) are quantified through history matching the field results and automated stochastic sensitivity analysis and uncertainty assessment.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".