Control of Reservoir Heterogeneity in SAGD Bitumen Processes
Bibliographic record
Abstract
Abstract Uniform steam chamber growth (conformance) in a Steam Assisted Gravity Drainage (SAGD) process promotes enhanced bitumen recovery, project economics and environmental benefits. In the past, operators have implemented numerous alternative strategies to improve steam conformance in the SAGD process. Simultaneous injection in the inner tubing and annular space or use of dual-tubing completions are commonly used to provide some degree of injection control at the heel- and toe-regions of the horizontal well pair. However, this does not necessarily guarantee the uniformity and performance efficiencies sought. Distributed flow control devices (FCDs) can also contribute to more uniform production and injection. They can be incorporated in the horizontal production completion as restrictive elements to modify the pressure distribution along the length of the wellbore. A hybrid of these two technologies is proposed to provide superior (uniform and efficient) steam chamber development that benefits asset performance in highly heterogeneous bitumen reservoirs. It has been shown that using Proportional-Integral-Derivative (PID) feedback to control the steam injection can be beneficial. The feedback control is applied to each steam injection point in the horizontal well pair. Injection at these control points is regulated by a PID feedback controller which monitors temperature differences between injected and produced fluids in order to both enforce a specified subcool and to achieve uniform production along the entire length of the producer. This paper examines detailed wellbore simulations of a SAGD well pair with an FCD completion in the producer and PID controlled steam injection with dual tubing strings. A synthetic reservoir model, based on logs from the Athabasca region of Alberta, is employed and represents a highly heterogeneous formation with properties typical of a bitumen resource. Feedback controlled steam injection (FCSI) can be dynamically configured to target the worst-offending regions of the well pair in order to (i) start the PID control as early as possible after switchover, (ii) achieve a specified subcool target in those regions that would benefit most from the subcool and (iii) temporarily ignore regions which are bypassed and difficult to produce, but which may be dynamically included at a later time. Practical algorithms are presented to achieve these goals, based on existing technology.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".