Development of Solvent and Steam-Solvent Heavy Oil Recovery Processes Through an Integrated Program of Simulation, Laboratory Testing and Field Trials
Bibliographic record
Abstract
Abstract ExxonMobil and its Canadian affiliate Imperial Oil Resources are pursuing an integrated research program targeted at developing the next generation of heavy oil recovery processes which utilize light hydrocarbon solvents in conjunction with steam or as an alternative to steam-only processes to mobilize the in-situ heavy oil. The key benefits of employing solvent are improved economics and increased recovery from resource that is impractical with steam-only processes, improved environmental performance, particularly reduced greenhouse gas emissions and reduced water use. A suite of field trials, pilots and commercial applications have been operating over the past several years at Imperial Oil's Cold Lake field in Alberta, Canada. These have included both solvent-assisted and solvent-only field trials. Collectively, the results of these trials show that solvent recovery processes for heavy oil are technically viable and have considerable commercial potential. This paper summarizes the dimensions of the integrated research program that have been key to delivering the successful results to date. Simulation, laboratory testing and physical modelling with a focus on scaling to the field have been employed extensively prior to field testing. Short-term, relatively low cost field trials have been utilized to calibrate models prior to more costly, longer term pilots with dedicated facilities. Extensive field characterization has been conducted prior to final site selection and pilot operation. Integrated operational and surveillance plans have been employed to ensure measurable and reliable field performance data is acquired that can be used to calibrate and validate simulation performance. Finally, learnings from this integrated research program can be more broadly applied to the commercialization of other EOR processes and the research and development processes leading up to the decision to execute a major pilot. Introduction Thermal recovery processes including steam flooding, cyclic steam stimulation (CSS) and steam-assisted gravity drainage (SAGD) are among the most broadly applied and commercially successful enhanced oil recovery (EOR) processes. In recent years there has been considerable growth in production utilizing these methods in the Canadian oil sands deposits which are primarily located in the Cold Lake and Athabasca regions of Alberta. These processes can be very efficient and economic when applied in thick, high porosity, high permeability reservoir deposits. However, significant volumes of natural gas are required to generate the steam used in these processes and associated with the steam generation are greenhouse gas (GHG) emissions. There are strong economic, technical and environmental drivers to develop enhanced thermal recovery processes that reduce steam utilization, enable more efficient recovery from lower quality resources and improve environmental performance. The enhanced heavy oil recovery processes described in this paper target these challenges. An overview of this integrated technology development program was provided previously by Boone et al1. This paper reports on the continued successful development in subsequent years and expands on some of the key factors for success. Those factors include:A long term dedicated research program in the area of heavy oil recoveryDisciplined application of a gated research processA host field where pilots and field trials are strongly supported by management and competently supported by skilled and experienced operating personnel
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".