A Novel Multi-Phase Strategy for Optimizing CO2 Utilization and Storage in an Oil Reservoir
Bibliographic record
Abstract
In this paper, an innovative multi-phase strategy is developed and numerically tested to optimize CO2 utilization and storage in an oil reservoir to support low carbon transition. In the first phase, the water-alternating-gas (WAG) injection is conducted to simultaneously store CO2 and produce crude oil in the reservoir from the respective injection and production wells. In the second phase, the injection and production wells are both shut in for some time to allow CO2 and water to be stratigraphically separated. In the third phase, CO2 is injected from the upper part of the reservoir above the separated water layer to displace water downwards, while fluids continue to be produced in the water-dominated zone from the lower part of the production well. Lastly, the production well is finally shut in when the produced gas–water ratio (GWR) reaches 95%, but CO2 injection is kept until the reservoir pressure is close to the fracture pressure of its caprocks. The numerical simulations show that implementing the proposed multi-phase strategy doubles CO2 storage in comparison to applying the WAG injection alone. In particular, 80% of the increased CO2 is stored in the third phase due to the optimized perforation. In addition, the CO2 injection rate in the last phase does not appear to affect the amount of CO2 storage, while a higher CO2 injection rate can reduce the CO2 injection time and accelerate the CO2 storage process. In the proposed strategy, we assume that the geothermal energy resources from the produced fluids can be utilized to offset some energy needs for the operation. The analysis of energy gain and consumption from the simulation found that at the early stage of the CO2-WAG phase, the energy gain mostly comes from the produced oil. At the late stage of the CO2-WAG phase and the subsequent phases, there is very little or even no energy gain from the produced oil. However, the geothermal energy of the produced water and CO2 substantially compensate for the energy loss due to decreasing oil production. As a result, a net energy gain can be achieved from the proposed multi-phase strategy when geothermal energy extraction is incorporated. The new multi-phase strategy and numerical simulation provide insights for practical energy transition and CO2 storage by converting a “to be depleted” oil reservoir to a CO2 storage site and a geothermal energy producer while enhancing oil recovery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.000 | 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".