Coupled Wellbore/Near-Well Flow and Geomechanical Thermal Simulation of Cyclic Steam Stimulation with Different Geometric Fractures
Bibliographic record
Abstract
Abstract Cyclic steam stimulation (CSS) is a commercial in situ recovery method that involves injecting steam into formations at high pressure to reduce the viscosity of heavy oil and bitumen. The injection pressure is typically above the reservoir fracture pressure to induce fractures, which can improve reservoir permeability and fluid mobility. Geomechanics and heterogeneities near a well significantly affect reservoir fracturing, particularly under thermal conditions. In addition, oversimplified descriptions of fracture geometry may fail to represent actual reservoir performance. Thus modeling near-well flow effects coupled with geomechanics is essential for detailed large-scale thermal simulations with fractures. Conventional methods have exhibited limitations in capturing interactions among wellbore/near-well flow, geomechanics, and fluid flow during CSS development. Therefore, flow/stress/near-well flow coupling under different fracture geometries is investigated in this study. A facies-controlled geostatistical model is first constructed for a Cold Lake reservoir to obtain more reliable results. A fully coupled model is further generated from this geological model. After local grids near wells are refined, we construct different fracture geometries by changing the fracture length and direction. In this study, uncertainty analyses on fracture geometry near the vertical wells are performed. The simulation results show that omitting geomechanics and wellbore modeling increases oil recovery. Moreover, oversimplified fracture geometry with simple planes overestimates an oil rate. Furthermore, fractures with complex geometries and geomechanics exhibit high conductivity and provides effective channels for steam and bitumen but at the same time, it may cause steam channeling and decrease the efficiency of steam 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".