Pressure Maintenance and Improving Oil Recovery via Water-Alternating-CO2 Process in Thin Heavy Oil Reservoirs
Bibliographic record
Abstract
Abstract Techniques have been developed to experimentally and numerically evaluate performance of water-alternating-CO2 processes in thin heavy oil reservoirs for the purpose of pressure maintenance and improving oil recovery. Experimentally, a three-dimensional (3D) physical model consisting of three horizontal wells and five vertical wells is used to evaluate the performance of water-alternating-CO2 processes. Two well configurations have been designed to examine their effects on heavy oil recovery. The corresponding initial oil saturation, oil production rate, water cut, oil recovery and residual oil saturation distribution are examined under various operating conditions. Subsequently, numerical simulation is performed to match the experimental measurements and optimize the operating parameters (e.g., slug size and water-CO2 ratio). It has been shown that water-alternating-CO2 processes implemented with horizontal wells can be optimized to significantly improve performance of pressure maintenance and improving oil recovery in thin heavy oil reservoirs. The incremental oil recovery of 12.4% and 8.9% through three water-alternating-CO2 cycles are experimentally achieved for the aforementioned two well configurations, respectively. The excellent agreement between the measured and simulated cumulative oil production indicates that the displacement mechanisms governed water-alternating-CO2 processes have been numerically simulated and matched. The WAG ratios of 0.75 and 1.00 are found to be the optimum values for Scenarios #1 and #2, respectively.
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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 | 0.001 | 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 | 0.001 |
| 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 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".