CO<sub>2</sub> Geological Storage Coupled with Water Alternating Gas for Enhanced Oil Recovery
Bibliographic record
Abstract
Due to the climate change and global warming, the concern about CO2 emission into the atmosphere has been raised in many industries such as oil and gas business. The sources of CO2 include natural gas processing and other processes. The current technology to mitigate CO2 is carbon capture, storage and utilisation. At the same time, oil production is expected to increase due to the higher consumption. CO2 geological storage in depleting oilfield coupled with enhanced oil recovery can be a better technology to meet both requirements. The excellent example of this technology is the Weyburn Project in Canada that can store CO2 and produce more oil as well as prolong the reservoir life. From this successful project, the technology is studied by applying it with the depleting oilfield for both oil production and CO2 storage in the North of Thailand by using simulation model. It becomes the objective of this research, which is to evaluate CO2 geological storage coupled with water alternating gas (WAG) for enhanced oil recovery as well as to study the effects the parameters, such as total hydrocarbon pore volume (HCPV) injection and WAG ratio, on oil production and CO2 consumption and sequestration for enhanced oil recovery, with the added benefit of carbon sequestration. CMG software from Computer Modeling Group Ltd. is used to create the 3D simulation model to predict the CO2 storage in the geological formation. From the simulation, the results reported that oil can be produced up to 125,976 m3 of oil or 57 % recovery, CO2 consumption is 66,261 m3 of gas and CO2 utilisation is approximately 0.53 m3 of gas per m3 of oil. The main parameters for WAG process is WAG ratio in that oil production increases as WAG ratio increases. CO2 consumption increases with total HCPV injection. The results of this study can be applied to develop the CO2 enhanced oil recovery (EOR) in the depleting oilfield in the North of Thailand for both oil production and CO2 storage.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".