Reservoir Simulation of Steam Fracturing in Early Cycle Cyclic Steam Stimulation
Bibliographic record
Abstract
ABSTRACT In Cyclic Steam Stimulation (CSS), steam is injected above the fracture pressure into the oil sands reservoir. In early cycles, the injected steam fractures the reservoir creating a relatively thin dilated zone which allows rapid distribution of heat within the reservoir without excessive displacement of oil from the neighborhood of the wellbore. Numerical reservoir simulation models of CSS that deal with the fracturing process have difficulty simultaneously capturing flowing bottom hole pressure behaviour and steam injection rate. In this research, coupled reservoir simulation (flow and heat transfer) and geomechanics models are investigated to model dynamic fracturing during the first cycle of CSS in an oil sands reservoir. In Alberta, Canada, in terms of volumetric production rate, CSS is the largest thermal recovery technology for bitumen production with production rates equal to about 1.3 million bbl/day in 2008. The average recovery factor from CSS is between 25 and 28% at the economic end of the process. This implies that the majority of bitumen remains in the ground. Since the mobility of the bitumen depends strongly on temperature, the performance of CSS is intimately linked to steam conformance in the reservoir which is largely established during steam fracturing of the reservoir in the early cycles of the process. Thus, a fundamental understanding of the flow and geomechanical aspects of early cycle CSS is critical. A detailed, thermal reservoir simulation model, including dilation and dynamic fracturing, was developed to understand their effects on bottom hole pressure and injection rate. The results demonstrate that geomechanics must be included to accurately model CSS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".