The Challenge of Predicting Field Performance of Air Injection Projects Based on Laboratory and Numerical Modelling
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Bibliographic record
Abstract
Abstract Air injection-based enhanced oil recovery processes are receiving increased interest because of their high recovery potentials and applicability to a wide range of reservoirs. However, most operators require a certain level of confidence in the potential recoveries from these (or any) processes prior to committing resources. This paper addresses the challenges of predicting field performance of air injection projects using laboratory and numerical modelling. Laboratory testing, including combustion tube tests, ramped temperature oxidation and accelerating rate calorimeters can supply data for simple analytical models, as well as providing important insights into potential recovery-related behaviours. These tests are less suited to providing detailed kinetic data for direct and reliable use in numerical simulators. Indeed, the oxidation reactions are sufficiently complex that, regardless of how powerful the thermal reservoir simulator is, its predicting capability will strongly depend on the engineer's understanding of the process and ability to model the most relevant oxidation behaviours of the particular oil reservoir under study. It is proposed that the optimum design cycle for air injection-based processes is to perform laboratory testing that would aid in the understanding of the process and in the design and monitoring of a pilot-scale field operation. Analytical models and simplified, semi-quantitative reservoir simulation models would be employed at this stage. If this evaluation stage is successful, a pilot operation would be initiated and the data gathered during the pilot, as well as laboratory oil property and compositional data, would then be used to history match and tune a model for predictions of the full field operation. Introduction This paper has been written in response to questions which many reservoir engineers express when evaluating the feasibility of air injection as an enhanced oil recovery process for their fields. Questions such as, "What laboratory tests are available? What type of data is provided by each test? How do we use the lab results to predict field performance?" are not uncommon, and, although there are not straightforward answers, a discussion on the usefulness of different lab tests is presented to clarify some of the related concepts. This document has also been written in response to the concerns and comments expressed by many reservoir simulation practitioners when matching combustion tube tests and other supporting oxidation experiments, and trying to predict field performance of an air injection project based on kinetic parameters obtained from such tests. Questions such as, "How do we use the lab data in the reservoir simulator? What are the limitations of thermal reservoir simulation when predicting field performance of air injection projects?" are addressed to provide additional feedback and promote further discussion. Additionally, this manuscript describes some of the combustion behaviours which have been observed by the In Situ Combustion Research Group (ISCRG) at the University of Calgary while performing combustion tube tests and supporting cracking/oxidation experiments, and gives some recommendations to improve the modelling of the combustion process using thermal reservoir simulators.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it