Does Miscibility of In Situ Generated Flue Gases With Light Crude Oils Contribute to Oil Recovery Under High Pressure Air Injection?
Bibliographic record
Abstract
Abstract The improvement in recovery of light oil by high pressure air injection (HPAI) involves a combination of complex processes, each contributing to the overall recovery. One of such processes is the spontaneous oil ignition of the air-oil mixture with complete oxygen utilization. This process generates flue gases, which are in contact with the reservoir oil at the displacement front. An experimental study was carried out to investigate the mechanism and contribution of miscible displacement, by in situ generated flue gases, to the recovery of light oil in reservoirs undergoing HPAI. The flue gas displacements were carried out on recombined reservoir oil in a slim-tube apparatus at reservoir temperature of 116 °C and pressures ranging from 4028 psi (27.77 MPa) to 6680 psi (46.06 MPa). Results show that miscibility could not be achieved between the test oil and flue gases under the test conditions. Experiments conducted between 5987 psi (41.28 MPa) and 6532 psi (45.04 MPa), however, gave an indication of near-miscible displacement of the test oil. The flue gases displaced the oil in a forward contacting extraction process, resembling a multi-contact vapourizing gas drive mechanism. The relatively high recovery, high extraction of oil components, and the pattern of flow behind the displacement front, exhibited at high pressures, demonstrate that near-miscible displacement by in situ generated flue gases could significantly contribute to oil recovery in light oil reservoirs undergoing HPAI. Introduction Air injection into high pressure reservoirs is an emerging technology for the enhanced recovery of light oils. It is probably the best hope for improved recovery from the world's ever declining reserves of conventional oil and profitable enhanced recovery of the enormous quantities of residual oil trapped in depleted and matured waterflooded light oil reservoirs1, 2. Air is the most inexpensive, available gas that can be used to accelerate oil recovery. Air injection is especially applicable in low porosity and low permeability reservoirs where water injectivity is extremely low3. Apart from providing a better interfacial tension (IFT) response4, air injectivity is about ten times that of water in terms of reservoir volume5, making air injection more advantageous than water injection in deep, tight, high pressure reservoirs. A number of successful high pressure air injection projects in light oil reservoirs have been documented in the literature5 - 11. Most of these projects have been operating for many years, attesting to their technical and economic success. The improvement in recovery of light oil by HPAI involves a combination of complex processes, each contributing to the overall recovery. These processes include reservoir pressurization; oil swelling, immiscible gas displacement, and superextraction when operating above the critical point of water. In addition to this, spontaneous oil ignition with complete oxygen utilization2, 12, 13 and near-miscibility/miscibility of the in situ generated flue gases with the reservoir oil13 is a possible and advantageous process. Apart from reservoir pressurization, oil swelling, and immiscible gas displacement, the mechanism by which the other processes occur in the reservoir and their contribution to oil recovery have not been discussed in clear terms in the technical literature14.
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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 | 0.001 |
| 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".