An Experimental Study on Three-Phase Flow in High Pressure Air Injection (HPAI)
Bibliographic record
Abstract
Abstract High Pressure Air Injection (HPAI) is an improved oil recovery process in which compressed air is injected into typically deep, light oil reservoirs. Part of the oil reacts exothermically with the oxygen in the air to produce flue gas (mainly composed of nitrogen, carbon dioxide and water). Literature explaining the reaction mechanisms and phase interactions is available. Nevertheless, little effort has been devoted to describing gas, oil and water three-phase flow behaviour under HPAI reservoir conditions. Three coreflood experiments were conducted on Berea sandstone core. The first experiment consisted of injecting flue gas into core at initial oil and connate water saturations to obtain liquid-gas relative permeability data. The second experiment was designed to evaluate oil re-saturation, after gas sweep, simulating an HPAI thermal front. The third experiment consisted of gas displacing both oil and water completing the data necessary to plot the three-phase relative permeability curves. Reservoir simulation was used to adjust relative permeability curves and hysteresis parameters by matching the pressure drop and production data. Introduction It is well-known that the oil recovery mechanisms in HPAI are a combination of highly efficient displacement by the reaction front and light oil/flue gas compositional interactions, such as oil swelling and/or vapourization and near-miscible behaviour(1). However, the contribution of each of these recovery mechanisms has not been properly assessed(2). Although significant effort has been devoted to the characterization of oxidation kinetics(3–5) and flue gas/light oil compositional interactions(6, 7), the process remains challenging to simulate even under controlled and ideal conditions, i.e., a combustion tube test. Some of the difficulties include the limited availability of experimental data to feed the numerical simulators with the required parameters, as well as the interdependence of these parameters and their variation with temperature. Assuming that these difficulties can be overcome by carrying out a study that allows a judicious analysis of experimental information and a careful treatment of the matched parameters in a numerical simulator, there is still a piece of information that has a strong influence on the simulation results and cannot be defaulted or left as a final matching tool: relative permeability. In an earlier study(2), it was suggested that for a combustion tube match, the steps previous to air injection (waterflood and inert gas flood) could be used to find a full set of relative permeability curves for the run. It was also pointed out that the rock-fluid dataset should include a variation of relative permeability data with interfacial tension to account for changes in pressure, composition, and most importantly, temperature. While this being necessary, it is still insufficient to ensure a correct representation of the flow of phases in a porous medium subjected to HPAI. Ahead of the reaction front, the high mobility flue gas (mainly composed of nitrogen and carbon oxides) displaces oil and water at nearly reservoir temperature, while in the high temperature zone, oil and water are evapourated to later condense downstream. In both zones, three-phase flow is occurring.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".