Predicting the Flow Distribution on Total E&P Canada's Joslyn Project Horizontal SAGD Producing Wells Using Permanently Installed Fiber-Optic Monitoring
Bibliographic record
Abstract
Abstract During the start-up and early operation of horizontal steam assisted gravity drained (SAGD) wells, it is important to understand the flow distribution of bitumen and water along the horizontal reservoir interval. If this distribution is understood, the distribution of steam, injected either at the heel or toe of the steam injector, can be adjusted to optimize the startup and early operation of the SAGD pair. Total E&P Canada permanently installed optical fiber along their first Joslyn SAGD production well to monitor the temperature profile continuously during startup and production. Initial steam circulation and production occurred in 2004. The acquired data shows that large temperature gradients exist across the wellbore during startup and early production, which is consistent with data from the observation wells. The injector-producer interval between SAGD wells was modeled with a thermal reservoir model to understand the influence of fluid viscosity, water cut, and permeability on fluid flow and the fiber optic measured temperature response. By varying the injector-producer reservoir temperatures until the model temperature matches the measured distributed fiber optic temperature, it is possible to calculate the fluid viscosity in the inter-well region and consequently the flow distribution along the producing well. Injector-producer temperature, which dictates the bitumen viscosity, was found to be the main parameter controlling flow in the injector-producer region. The results highlight the need for distributed temperature measurements in SAGD wells to facilitate understanding of the temperature response over time. The analysis demonstrates that it is possible to determine the flow profile from the distributed temperature measurement and thus optimize the injection of steam into the heel or toe of the injector well.
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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) | 0.000 | 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".