Potential for <i>In Situ</i> Combustion in Depleted Conventional Oil Reservoirs
Bibliographic record
Abstract
Abstract In oil producing regions like the US Mid-Continent, there are a large number of mature conventional oil fields that have reached or are approaching their production limit by conventional techniques, however, current strong oil prices and security issues justify additional EOR/IOR efforts. Air injection-based techniques (fireflooding or in situ combustion) have been demonstrated to provide commercially successful recovery from medium and light oils reservoirs. While the history of air injection-based EOR is littered with the perception of failed projects, many of the failures were associated with low oil prices. In other cases, failures were due to compressor problems, or incorrect concepts of how air injection processes operate. Ineffective ignitions, failure to inject enough air, and applications in reservoirs that had no hope of success explain the trouble with many past projects. This paper reviews some of the successful air injection projects in higher gravity oil reservoirs and discusses the elements that are critical for success. These include the ability to ignite and continuously burn a fraction of the oil at reservoir conditions, the suitability of the reservoir for a gas-injection based recovery process, the availability and suitability of preexisting infrastructure, and a reasonable prediction of how much air should be injected and how much oil recovery could be expected. The paper also discusses possible options for taking advantage of the product gas stream. The purpose of this paper is to arm the petroleum engineer with the relevant information and the right set of questions to ask when considering the application of air injection in a given field.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".