A Theoretical Model for Dynamic Performance Prediction of Air-Foam Flooding in Heterogeneous Reservoirs
Bibliographic record
Abstract
Abstract Air-foam flooding has already been pilot-tested and approved as a feasible and promising EOR method in tight oil reservoir. This study is to develop a simple but effective model to predict dynamic performance of air-foam flooding by considering main physical and chemical mechanisms in this process, such as gas channeling caused by mobility difference, flue gas driving and instability of foam. According to the instability of foam, a new model is proposed to estimate recovery factor, which separates foam flooding status into three areas, including gas, water and foam area. Prediction of breakthrough time is critical for this model, which is estimated according to relation between area being swept and area to be swept by cycle of injected slug. Once the breakthrough time of gas and surfactant solution are estimated, dynamic performance of every stage during air-foam flooding is predictable. Relation between recovery factor and production time or PV can be predicted, if essential reservoir, fluid and operational parameters are provided. Relative numerical simulation studies on homogeneous case and heterogeneous case are both introduced to validate proposed model. Results of comparison suggest this model is highly consistent with the numerical simulation results. The most extreme difference in recovery factor after ten years between proposed method and simulation is less than 6.2%, which is less than 2.5% in most case. Meanwhile, this model requires much less input data than numerical simulation for dynamic performance prediction, which makes it a really convenient tool to evaluate potential of an air-foam flooding project. Sensitivity analysis is introduced to study effects of variation in parameters on the performance of air-foam flooding project, including fluid injection rate, slug size, slug proportion and reservoir heterogeneity. The higher liquid ratio is injected in each slug, the better recovery factor is obtained. However, there isn't much difference once liquid ratio is higher than 50%. Recovery factor increases with higher fluid injection rate. Meanwhile, the increasing rate of recovery factor drops as fluid injection rate increases. Also, optimum slug size exists which means any slug size being higher or lower this value would result in lower recovery factor. These conclusions can help get optimized operational parameters once economic data are settled. This proposed model considered major mechanisms in air-foam flooding and related reservoir, fluid and operational parameters, and provided a fast approach to predict dynamic performance of air-foam flooding and can be used as a tool to optimize the operational parameters. The core idea of this method, such as the estimation of breakthrough time, also provides a new approach to estimate the performance of other immiscible flooding method.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".