Laboratory Screening for Air Injection-Based IOR in Two Waterf looded Light Oil Reservoirs
Bibliographic record
Abstract
Abstract In the last two decades, air injection has proven to be an effective method for enhanced recovery from light oil reservoirs. This has led to a need for effective laboratory procedures for screening potential candidate reservoirs, many of which have been previously waterflooded. Experiments at the University of Calgary have shown the benefit of Accelerating Rate Calorimeter (ARC) tests in conjunction with traditional combustion tube testing at reservoir pressure for the planning of field scale implementations. In this study, four ARC tests and two combustion tube tests were performed on resaturated cores from each of the Avil? and Troncoso waterflooded light oil reservoirs of Argentina. The paper reviews the characteristics of the two reservoirs, describes the tests that were performed on the reservoir samples, and summarizes the results obtained. The ARC tests provided Arrhenius-type kinetic rate data for the ignition and combustion reactions. The combustion tube tests were used to evaluate the air requirements and overall burn stability. For each reservoir, one combustion tube test was performed at the original oil and water saturation while the second test was performed at the waterflooded saturation. The effect of initial oil saturation on the burning characteristics and liquid recoveries is of particular interests to operators considering air injection-based oil recovery projects. Background The application of air injection-based processes (or fireflooding) to deep, high pressure light oil reservoirs is a relatively new IOR process. While past applications of this process to low API oils have met with mixed success, many of the problems encountered in heavy oil reservoirs relating to mobility and unfavourable oxidation characteristics are not an issue in high API oils(1, 2, 3). The success of this process is evident from the recent start-up of a new project by Continental Resources, injecting 85 MMscfd of air into a field in the Williston Basin. Currently, several other companies are actively researching potential candidate reservoirs for air injection- based recovery processes. While air injection into light oil reservoirs is not a new process, more recent applications of air injection to light oil fields have targeted reserves that are deep and/or have had low permeability, rendering them poor candidates for waterflooding(4). However, the resulting successes and potential process benefits(5) have extended interest in this IOR process to all types of light oil reservoirs, including those that have undergone waterflooding as a secondary recovery mechanism. Air injection into previously waterflooded reservoirs entails additional considerations. From an operational viewpoint, extensive waterflooding can result in the lowering of the overall reservoir temperature, making the oil less reactive to the oxygen in the injected air. Air injection processes rely on combustion reactions between oxygen and a fraction of the oil in place to produce heat and carbon oxides, so it may be necessary to provide an artificial ignition source. The presence of large saturations of water in the reservoir constitutes a heat load to the combustion process, as it is vapourized by the advancing reaction front. Highly permeable flow channels may have very low residual oil saturations, particularly around water injection wells.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".