Use of Acid Gas (CO2/H2S) for the Cyclic Solvent Injection (CSI) Process for Heavy Oil Reservoirs
Bibliographic record
Abstract
ABSTRACT Acid gas is a greenhouse gas (GHG) composed of (CO2/H2S). It is produced during oil, gas, and petrochemical operations. During the last several years, there has been an increasing pressure within the oil industry in Canada to further reduce GHG emissions. Another environmental problem within the oil industry comes from the elemental sulphur, which is converted from the H2S and is accumulated on the surface. Currently, there exist two technological solutions to deal with this challenge. The first one is acid gas injection into geological formations for sequestration purposes. This option has been developed in Canada over the last 22 years. The second one is the use of it for EOR operations in conventional oil reservoirs. This option has successfully been applied in Zama, a Canadian conventional oil reservoir. There is an interest within the oil industry again to evaluate the performance of CO2/H2S to also recover heavy oil during the Cyclic Solvent Injection (CSI) process. The CSI process has already shown success in improving heavy oil recovery after primary cold production (CHOPS) using pure solvents or (CO2 /propane or CH4/propane) mixtures. Consequently, if feasible, CO2/H2S injection for CSI could also contribute to increase the heavy oil recovery factor while eliminating the accumulation of sulphur on the surface and sequestering this greenhouse gas. The results of this work indeed demonstrated that the CO2/H2S mixture tested performs better for the CSI process than some of the conventional solvents previously tested i.e. pure CO2 or methane/propane mixture. Not only the recovery factor was better but also the oil recovery declined less with subsequent cycles by using CO2/H2S mixture rather than using pure CO2. It is important to mention though that the mixture of CO2/propane still shows the best performance in the series of solvents studied so far for the Cyclic Solvent Injection at Alberta Innovates Technology Futures.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".