Feasibility Study of Using Transient Temperature Analysis to Evaluate Fracture Length in Cyclic Steam Stimulation Process
Bibliographic record
Abstract
Abstract In Cyclic Steam Stimulation (CSS), steam is usually injected above fracturing pressure into oil sands to achieve desired injectivity; thus fractures are generally induced. The induced fractures will affect heating and flow patterns and thus affect the choice of well spacing and steam injection strategies. Therefore, it is critical to evaluate fracture length for successful CSS projects. Transient Temperature Analysis (TTA) is a technique which uses well-bore transient temperature data to estimate reservoir/wellbore characteristics. This paper discusses the feasibility of using TTA to evaluate the lengths of steam-induced fractures in cold lake oil sands by using wellbore transient temperature data in the soaking period of CSS. Numerical simulation model was first established based on history matching production data of a typical well in cold lake oil sands. Then the effect of different fracture lengths on temperature response in soaking phase was examined. Simulation results shows, in the middle and later period of soaking phase, linear relationships are found when temperature drop versus square root time are plotted. However, the correlation between fracture length and the slope is not clear. Based on the heated zone size and heated zone shape analysis at the very first moment of soaking phase, it is found the combined impact of reservoir permeability, reservoir thermal conductivity and fracture length determines the shut-in temperature response, which can be reflected on the slopes of the plots, with certain injection volume. But the qualitative representation of slope using fracture length, permeability and thermal conductivity cannot be found based on the results of finite difference based simulator. Aside from Finite difference method, simulation tools based on other numerical methods can be attempted to conduct TTA to evaluate the lengths of steam-induced fractures in CSS process.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".