A Comprehensive Approach to Modeling Air Injection-Based Enhanced Oil Recovery Processes
Bibliographic record
Abstract
Summary Modeling of air-injection-based processes for enhanced oil recovery (EOR) is a challenging task, mostly due to the complexity of the chemical reactions taking place. Also, the applicability of currently available kinetic models is limited to the reservoir systems they were originally developed for. The objective of this study is to derive a general chemical reaction framework that could be used to develop a kinetic model for a variety of crude oils (i.e., light or heavy oils). The work is based on the modeling of high-pressure ramped temperature oxidation (HPRTO) experiments, and combustion tube (CT) tests, performed on two different oil systems: a volatile oil that is near critical at reservoir conditions (44 °API), and a bitumen sample (10 °API). The HPRTO test is a kinetic experiment that intends to mimic the flow conditions within the reservoir and allows the determination of kinetic parameters of the different reactions. On the other hand, the CT test is meant to provide quantitative information on the combustion performance that can be expected in the field. Therefore, a kinetic model was derived for each of the cases based on the history match of an HPRTO experiment. The resulting model was validated by history matching a CT test for each of the oils. An important feature of these experiments is that they were performed at representative reservoir pressure conditions. The modeling approach chosen is an extension of the methodology originally proposed by Belgrave et al. in 1993, which is arguably the most comprehensive kinetic model available in the air injection literature. However, their model was developed from experiments performed on Athabasca bitumen, and it fails to represent the high-pressure air injection process as it occurs in light oil reservoirs, which are typically encountered at higher pressure conditions. For example, Belgrave’s model is based on the deposition and combustion of semisolid residue commonly known as “coke,” which is rarely present during the combustion of light oils at high pressure. As in Belgrave’s model, this study also describes the original composition of the oil in terms of maltenes and asphaltenes. The main difference lies in the presence and importance of oxygen-induced cracking reactions, as well as the combustion of a liquid-vapor flammable hydrocarbon mixture that is generated by cracking and oxidation reactions, which take place in the gas phase. Also, a unique feature of these simulations is that, apart from history-matching traditional variables such as thermocouple temperatures, fluid recovery, and produced gas composition, they also capture changes in the physical properties of the produced oil, such as viscosity and density, as well as the amount of the residual phases in the post-test core. This enhancement to Belgrave’s reactions allows modeling the air injection process in cases where coke is not the main source of fuel, such as in high-pressure light oil reservoirs. This work changes a paradigm deeply rooted in the original in-situ combustion (ISC) theory, by deriving a general chemical reaction framework that is used to develop a kinetic model for two crude oils, which are at opposite ends of the density spectrum. This allows the consolidation of a new and comprehensive general theory for the description of the ISC process as applied to oil reservoirs. Moreover, as the pseudocomponents representing the fuel are not present in the original oil, the method is not limited to a fluid characterization in terms of maltenes and asphaltenes but could potentially be applied along with any type of characterization of the original oil.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".