Advanced Solvent-Additive Processes via Genetic Optimization
Bibliographic record
Abstract
Abstract This paper describes the application of a genetic algorithm to the development of a solvent-additive SAGD process. A review of related field projects and key simulation studies is provided, together with a discussion of the pros and cons of potential alkane solvents. Economics and the impact of dynamic and ultimate retention are discussed. A general conclusion drawn from literature is that optimal solvent application to SAGD will likely involve time variations in both rate and composition of the solvent. This results in an optimization problem that has a large number of dimensions, and is very nonlinear. Genetic algorithms, which mimic biological evolution, have been found by us to be extremely effective in addressing such problems. The general methodology of application to solvent additives by Laricina Energy.is described. A key product of this effort, optimized for a simple clastic reservoir, is presented. The genetic algorithm produced an operable process, which could be described as a new combination of pre-existing concepts. The process offers material improvements in thermal bitumen supply costs, as well as recovery factor. Major reductions in the physical steam/oil ratio (SOR), (and therefore) capital intensity and carbon emissions, are indicated. Introduction The addition of light hydrocarbon solvents to steam has long been regarded as the simplest and most important potential increase in SAGD performance. Recently, at least two commercial implementations of such processes have begun operation. In the current economic environment, advances with in situ technology are all the more important. Solvent Addition Goals The perceived benefits of solvent addition to SAGD include:reduced SORincreased well productivityreduced capital intensity to startupincreased recovery via reduced Sorincreased recovery via higher (economic)volumetric sweep In addition to the above, EnCana1 has recently indicated that their solvent-assisted process (SAP) allows for greater well spacing than with steam alone. There are as yet no published analyses of the detailed transport mechanisms for the increased oil rates observed with solvents. In general, solvent vapor accumulates ahead of the steam front, where it mobilizes and drains oil from regions that may be considerably cooler than the steam zone. Thus, the average temperature of the drained volume is much less than for the same recovery by steam, accounting for the SOR improvement. The oil rate increase is qualitatively explained by lower oil phase viscosities in the drainage zone.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".