Solvent-Assisted Start-up of SAGD Wells in Long Lake Project
Bibliographic record
Abstract
Abstract In oil sands in situ operations using steam-assisted gravity drainage (SAGD), achieving effective communication between the injector and producer with a reasonable conformance is crucial for the success of SAGD conversion and the following ramp-up phase. The start-up operation normally relies on heat conduction phenomena for establishing communication between the wells. For oil sands reservoirs containing extremely high viscosity bitumen, establishing the temperature profile required for SAGD conversion using conduction as the only heating mechanism is not efficient and can take 90 to 120 days to achieve. Start-up operation can be accelerated by enhancing the rate of convective heat transfer to the formation by techniques such as bullheading and cold/hot dilation. At Nexen's Long Lake in-situ SAGD project, the use of higher injection pressures to enhance start-up is limited by the presence of high water saturation zones within the bitumen pay zone ("lean zones"), an adjacent Quaternary-age fresh water-bearing channel, and shallow formation depth. In order to overcome these constraints, an approach using solvent injection in a warm system with enhanced injectivity was successfully designed and implemented. In this approach, the solvent was injected in a high bitumen saturation system after circulating the well for about 70 days. Then, the solvent was chased with hot water into the formation to deliver the solvent deeper into the formation and enhance the rate of solvent-bitumen mixing during the soaking time. This paper reviews the design criteria, well selection process, and implementation of warm solvent injection in the conducted pilot in the start-up phase of Pad 13 at Long Lake. It also compares production responses of solvent-treated and control wells with comparable reservoir properties within the same pad to evaluate the performance of the designed pilot. Review of the production data shows that the solvent-treated well pair has outperformed all the other well pairs of the pad with no apparent conformance issues. This well had a quick ramp-up which is considerably faster than the average ramp up time at the Long Lake project. The collected data suggests that applying solvent-assisted start-up in systems that have enhanced mobility by pre-circulation of steam can shorten the circulation time and accelerate the ramp-up phase after SAGD conversion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.001 | 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".