Monitoring and Predicting CO2 Flooding Using Material Balance Equations
Bibliographic record
Abstract
Abstract In order to operate a CO2 flooding scheme successfully, it is necessary to get accurate information about the reservoir dynamic performance and the fluids injected. Although some numerical simulation studies have been conducted, the complicated drive mechanisms and actual reservoir performance have not been fully understood. Thus, there is a strong industrial need to develop models using different perspectives to provide valuable and complementary insights into the reservoir performance during the CO2 flooding process. The objective of this study is to develop models using material balance equations (MBE) to analyze the field data before and after CO2 injection. After matching the historical field data, the proposed model can be applied to evaluate, monitor and predict the overall reservoir dynamic performance during the CO2 flooding process. To accurately account for the complex displacement process involving compositional effect and multiphase flow, the PVT properties of reservoir fluids and the four-phase fluid relative permeability relationship are integrated into the model. This study has investigated the effects of a number of factors, such as the reservoir pressure, the amount of CO2 injected, the CO2 partition ratios in reservoir fluids, the possibility of the existence of a free CO2 gas cap, the proportion of reservoir fluids contacted by CO2, the oil swelling and the oil relative permeability improvement. The model has been applied to analyze the Weyburn CO2 flooding project. The study has shown that the proposed MBE model is an effective complementary tool to analyze overall reservoir performance in tertiary CO2 recovery processes. The results show that:there exists a free CO2 gas cap under reservoir conditions, even if the reservoir pressure is larger than MMP (minimum miscible pressure) in the Weyburn Field;the CO2 partition ratios in oil, water and gas phases and the proportions of reservoir fluids contacted by CO2 largely affect the drive mechanism and production performance; andthe effect of CO2 solubility in water under actual reservoir conditions cannot be neglected. The proposed new model is the first one in developing and applying MBE to evaluate the overall dynamic performance for the CO2 flooding process and a valuable insight into reservoir responses during this process has been achieved. Introduction CO2 flooding is considered one of the most effective tertiary recovery processes in light/medium oil reservoirs and has achieved widespread use in the petroleum industry. However, the complicated displacement mechanisms and reservoir performance involved in the CO2 injection process have not been completely understood. Monitoring reservoir performance and obtaining accurate information regarding reservoir fluid and injected fluid using field data will help understand the mechanisms and manage the CO2 injection project efficiently. There are two types of methods that monitor and evaluate reservoir performance: numerical simulation and MBE. MBE is a classic reservoir engineering tool. It is applied to analyze the reservoir performance based on the law of conservation of matter. Compared with MBE, reservoir numerical simulation is a more modern technique for modelling reservoir performance.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".