Reservoir Simulation Assessment of the Oil Recovery Mechanisms in High-Pressure Air Injection (HPAI)
Bibliographic record
Abstract
Abstract It has been speculated that oil recovery by High-Pressure Air Injection (HPAI) is mainly attributable to in situ generated flue gas displacement. Several published field scale simulations have been based on this assumption, focusing on the compositional modeling of flue gas/oil interaction. Experimental observations lead to the conclusion that the combustion front leaves behind a zero oil saturation zone (100% microscopic efficiency). Additionally, the self-correcting nature of the combustion zone redirects the air flow, promoting a high macroscopic (volumetric) efficiency. This work is aimed to quantify the contribution of flue gas displacement to oil recovery under HPAI by combining valuable experimental information and numerical reservoir simulation. Compositional (GEM) and thermal (STARS) simulators from CMG are used in this study to match slim-tube and, core flood flue gas displacements for two light crude oils and a combustion tube test. PVT data is matched and resulting parameters are incorporated into the compositional simulation. By using interfacial tension dependent relative permeability curves, it is demonstrated that the mere displacement capability of flue gas cannot account for the oil recovery in the combustion tube test. Introduction Tingas, Greaves and Young1 developed a HPAI simulation model based on typical North Sea oil reservoir conditions. A detailed analysis of phase behavior, chemical reactions and numerical stability is presented. They recommended at least two hydrocarbon liquid components should be defined for a proper description of oil-flue gas phase behavior. Kuhlman2 compared the performance of black oil, EOS-based and thermal simulators in predicting the production behavior of HPAI using data from Coral Creek (viscous-dominated) and Hackberry (gravitystable) reservoirs. The author points out that a minimum of six hydrocarbon components are needed in a thermal model in order to properly describe the incremental oil due to flue gas drive. A number of HPAI field simulation studies have been reported, providing enough details on the methodology employed3–5. None of the studies have properly incorporated the minimal compositional detail to simultaneously account for phase behavior and combustion reactions. As a result, the real contribution of flue gas drive to HPAI oil recovery is still a matter of speculation. Additionally, the existence of three-phase flow and re-saturation phenomena is a characteristic that needs attention in order to appropriately describe the HPAI process and to assess the contribution of each mechanism to the final oil recovery. The wide variation in pressure, temperature and phase composition in HPAI affects not only the phase behavior and chemical reactions involved, but also influences the relative permeability, and thus the mobility of each phase. Oil-gas interfacial tension (IFT) variation with pressure, temperature and composition is used in this work to establish a relationship between flue gas/light oil relative permeability and IFT, making use of Shokoya et al. 6,7 experimental work. This correlation is applied to the simulation of a combustion tube test (CT), carefully considering three-phase flow, to assess the oil recovery potential of flue gas drive in a HPAI process. Methodology Two light oils were used in this work: Oil 1 and Oil 2 are Oil A and Oil B correspondingly from Shokoya7.
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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".