The ABCs of In-Situ Combustion Simulations: From Laboratory Experiments to the Field Scale
Bibliographic record
Abstract
Abstract Air-injection-based recovery processes are receiving increased interest due to their high recovery potentials and applicability to a wide range of reservoirs. However, most operators require a certain level of confidence in the potential recovery from these (or any) processes prior to committing resources, which can be achieved with the use of numerical reservoir simulation. In a previous paper (JCPT, April 2009, pp. 23–34) it was proposed that, after successful laboratory testing, analytical calculations and semi-quantitative simulation models would be used for pilot design and further optimization of the actual operation. However, the specific steps for building the field-scale simulation models were not explicitly addressed. This paper discusses a detailed workflow which could be followed to engineer an air injection project using thermal reservoir simulation. The first step of the simulation study involves the selection of a kinetic model which could be either developed specifically for the reservoir in question or taken from public literature. Second, the oil would be characterized in terms of the same pseudo-components employed by the kinetic model and relevant PVT data would be matched to develop a fluid model for the thermal simulator. This new fluid model is used in the field-scale simulation model to history match the production history (i.e. prior to air injection) of the field. Third, relevant combustion tube tests would be history matched to validate the kinetic model and refine the thermal data that would go into the field-scale model. Finally, the results and knowledge gained from the combustion tube match(es) are applied to the field-scale model with the proper upscaling of some parameters. This simulation model would aid in selecting optimum well locations and operating strategies of the pilot. It would then be refined as the actual operation progresses to enhance its predictability and allow further optimization of the project. Technical considerations, advantages, and limitations of each step of the workflow are discussed in detail. This paper also presents workflow variations and recommendations applicable to new and already mature air injection projects for which simulation models are being developed.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".