Application of Thermal Recovery Processes in Heavy Oil Carbonate Reservoirs
Bibliographic record
Abstract
Abstract As the demand for oil grows, the petroleum industry is expanding the technology envelope to access and exploit many unconventional resources. The current focus of all major oil companies is heavy oil in highly porous and permeable sandstone reservoirs (oil sand), which presents a significant opportunity. However, viscous oil trapped in carbonates (over 1.6 trillion bbl)1, potentially a huge resource for future, needs application of new technologies to be exploited economically. At present thermal processes like steam flooding and cyclic steam stimulation (CSS) are being used extensively for the recovery of moderately viscous heavy oil from sand stone reservoirs. Another thermal process, SAGD (steam assisted gravity drainage) is being used for the recovery of higher viscosity heavy oil and bitumen from oil sand. Some of these processes are apparently very successful with ultimate recovery over 80%. Application of thermal processes to the carbonates poses a different challenge. In general, thermal recovery in carbonates is highly energy intensive and hence, economically challenged. Due to adverse wettability (generally mixed or oil wet), lower matrix permeability, the anticipated recovery using thermal processes is much lower compared to sand stone reservoirs. With increased access of the reservoir through horizontal wells there is a possibility that these resources can be exploited economically. A simulation study has been undertaken to explore the possibility of application of thermal processes in carbonate reservoirs. Based on the results of this work, this paper presents different possible recovery options and examines the sensitivities of reservoir parameters on thermal recovery processes like CSS and SAGD in fractured carbonate heavy oil reservoirs. The results suggest that SAGD may be viable in some of these reservoirs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".