Impact of Slow and Fast Warm-Up Rates on Cement Sheath Integrity in SAGD Wells: Using Dynamic Flow Simulators for Early Time Analysis
Bibliographic record
Abstract
Abstract The original viscosity of the oil in place is greater than 1,000,000 centipoises in most cases in Western Canada. As a result, steam assisted gravity drainage (SAGD) is a widely used thermal recovery method to produce bitumen reserves for the last 10-15 years. SAGD involves drilling two horizontal wells (one on top of each other roughly 5 meters) apart and injecting steam from the top well (called "injector") to reduce the viscosity of oil in place by heating and produce the flowing oil from the bottom one (called "producer"). Initially, there is very little to no pressure communication between the injector and producer since the bitumen is in solid state. As a result, in order to initiate communication between the wells so steam injection can penetrate into the reservoir to heat the rock and the fluids, both wells were circulated with steam and this phase is called "circulation". During circulation, steam is injected to the wells through long tubing and returns are received back through short tubing. In addition to be quite critical to the overall production performance of the reservoir, circulation phase has a critical impact on the integrity of the well since during this phase the well was introduced to heating for the first time. Considering the fact that steam is being injected in a cold well, thermal loading impacts on casing and cement are expected to be the highest during earlier times of circulation. While injecting steam at higher rates as early as possible has operational benefits, effects of such practice on zonal isolation should be evaluated in detail. A well operated by Devon in Jackfish field was used for this purpose and a dynamic flow simulator was modelled to investigate the impact of slow (going from 1 ton/hr to 5 ton/hr in 5 days) versus fast warm-up during early stages of circulation phase on cement sheath. Field data captured by distributed temperature sensing system (DTS) was used to validate the model and comparison on cement heating rate at different depth intervals during slow and fast warm up cases were made to determine at what depths and durations the heating rates of cement are different between the two cases. The combination of the field results and simulation enabled to determine the critical time frame where different circulation strategies have the highest impact on cement heating rates. The simulation results also provided insight on transient temperature distribution across wellbore and near wellbore during early times of circulation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".