Bibliographic record
Abstract
This article, written by Senior Technology Editor Dennis Denney, contains highlights of paper SPE 150706, ’An Integrated Technology-Development Plan for Solvent- Based Recovery of Heavy Oil,’ by Thomas J. Boone, SPE, Imperial Oil Resources, and Chick Wattenbarger, SPE, Scott Clingman, SPE, and Jasper Dickson, SPE, ExxonMobil Upstream Research Company, prepared for the 2011 SPE Heavy Oil Conference and Exhibition, Kuwait City, Kuwait, 12-14 December. The paper has not been peer reviewed. ExxonMobil and its Canadian affiliate Imperial Oil Resources are pursuing an integrated research program to develop the next-generation heavy-oil-recovery processes that use hydrocarbon solvents as a mobilizing agent. Key benefits of solvent-based processes are improved environmental performance, improved economics, and recovery of a resource that is not achievable with thermal processes. Introduction As used herein, a solvent-assisted process is one in which solvent is added to steam whereas a solvent-based process is one in which solvent is substituted for steam. The term solvent process is used to refer to both solvent-assisted and solvent-based recovery processes. These processes use solvents—typically, light hydrocarbons (e.g., propane or butane) or mixtures of light hydrocarbons (e.g., gas condensates or diluents)—to mobilize the heavy oil in the reservoir. Thermal processes including steam-flooding, cyclic steam stimulation (CSS), and steam-assisted gravity drainage (SAGD) are highly effective recovery processes that enable economical recovery levels that equal or exceed recoveries achieved in most conventional oil fields. Thermal processes are very effective at recovering heavy oil from the best-quality resource: thick, clean sands with high bitumen saturation and high porosity and permeability. However, the efficiency of thermal processes degrades significantly and these processes become less economically attractive in lower-quality resources. Solvent processes have the potential to enable economical recovery of additional resource that cannot be recovered economically with thermal processes. Solvent-Recovery-Process Classification The technical success of thermal-recovery processes lies fundamentally in the three to five order-of-magnitude viscosity reductions that can be achieved when heavy oil is heated to temperatures that can be achieved practically with steam. Solvent/heavy-oil mixtures can achieve similar viscosity reductions in combination with heat or with only the addition of solvent. Fig. 1 shows the effect of temperature and solvent concentration on the resulting mixture viscosity. Typically, heavy-oil-recovery processes endeavor to reduce the flowing-mixture viscosity to 10 cp or lower. While there is a wide variety of solvents that can bring about similar viscosity reductions, the need for relatively low-cost, commercially available, large volumes with acceptable health and environmental characteristics tends to favor the use of relatively pure or mixed light alkanes, such as propane, butane, pentane, gas condensates, or pipeline-quality diluents.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".