Reservoir Simulation of Steam Fracturing in Early-Cycle Cyclic Steam Stimulation
Bibliographic record
Abstract
Summary 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 that 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 bottomhole-pressure (BHP) behavior 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 approximately 1.3 million B/D 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. Because 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, with the use of a commercially available thermal reservoir simulator, to understand their effects on BHP and injection rate. The results demonstrate that geomechanics must be included to accurately model CSS. The results also suggest that the reservoir dilates during steam injection as the result of increases in reservoir temperature, which lead to thermal dilation and higher pore pressure.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".