Feasibility of Wider Well Spacing With Solvent Aided Process: A Field Test Based Investigation
Bibliographic record
Abstract
Abstract The objective of the investigation was to establish from a field trial feasibility of employing fewer wells for SAGD projects using solvent co-injection. An implicit issue in in situ recovery process of bitumen or extra-heavy is the requirement of heat which in the case of SAGD translates into the use of expensive steam. Gupta and Gittins (2006) have previously described a solvent co-injection based improvement to SAGD called Solvent Aided Process (SAP) which combines the benefit of using steam with solvents. As Edmunds et al. (2010) describe, over the years, significant work has been done to show the effectiveness of SAP in improving the energy efficiency of SAGD. SAGD is carried out in repeated patterns of side-by-side horizontal well-pairs, injector and producer lying approximately in a vertical plane, with these planes spaced from each other at a distance known as well-spacing. An optimal well spacing balances well capital cost against steam related capital and operating costs. SAP, as previously postulated (Gupta et al., 2006), allows this spacing to be larger (as much as by 100%, depending on reservoir conditions) requiring fewer well pairs to drain a given reservoir pool. To test the hypothesis Cenovus Energy conducted a field test at its Christina Lake thermal project, starting in 2009. The test location was specifically chosen to be in an isolated region which allowed for a larger lateral development of the vapor chamber without being intersected by any of the neighboring SAGD patterns. Presented in this paper are the results of this field test showing the expected production enhancement and reduction of steam to oil ratio. Furthermore, the results are interpreted in light of the allowance of a larger well spacing. The field results presented in this work confirm the feasibility of employing wider well spacing with SAP compared with steam-alone process or SAGD. This finding will be of interest to the wider industrial community as it holds the promise to improve the economics of the oil sands and similar projects. The findings of this investigation add to the knowledge base information related to well-spacing that has a significant impact on the economics of SAGD/SAP based projects.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".