A Comprehensive Approach to Modelling Air Injection Based Enhanced Oil Recovery Processes
Bibliographic record
Abstract
Abstract Modelling of air-injection-based processes for enhanced oil recovery 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 modelling of high-pressure ramped temperature oxidation (HPRTO) experiments, and combustion tube (CT) tests, performed on three different oil systems: a volatile oil which is near critical at reservoir conditions (44°API), a low-shrinkage light oil (35°API), and a bitumen sample (10°API). A kinetic model was derived for each of the cases based on the history match of a HPRTO experiment. The resulting model was validated by history matching a CT test for each of the oils. An important feature of all these experiments is that they were performed at representative reservoir pressure conditions. The modelling 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 air injection process as it occurs in light oil reservoirs encountered at high pressure. For example, Belgrave's model is based on the deposition and combustion of semi-solid 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 oil in terms of maltenes and asphaltenes. The main difference lies on the presence and importance of oxygen-induced cracking reactions, as well as the combustion of a flammable mixture, which takes 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 gas composition, they also capture changes in the physical properties of the produced oil, such as viscosity and density, which enhances the robustness of the approach and represents an important step towards the development of predictive simulation models. This work is unique as it is the first time a single kinetic modelling approach is capable of modelling the in situ combustion of different oil types, which allows the consolidation of a general theory for air injection processes. Moreover, since the pseudo-components 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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".