A Compositional Model for Gas Injection IOR/EOR in Tight Oil Reservoirs Under Coupled Nanopore Confinement and Geomechanics Effects
Bibliographic record
Abstract
Abstract Gas injection has become the top choice for IOR/EOR pilots in tight oil reservoirs because of its high injectivity. The effects of nanoconfinement and geomechanics are generally considered as non-negligible, but its coupled effects and resulting flow and displacement are still not well understood for gas injection. We hence present a general compositional model and simulator to investigate the complicated multiphase and multicomponent behaviors during gas injection in tight oil reservoirs. This compositional model is able to account for vital physics in unconventional reservoirs, including nanopore confinement, molecular diffusion, rock-compaction, and non-Darcy flow. The MINC method is implemented to handle fractured media. The nanopore confinement effect is modeled by including capillarity in VLE calculations. The rock compaction effect is represented by solving the mean stress from a governing geomechanical equation which is fully coupled with the mass balance equations to ensure the numerical stability as well as a physically correct solution. The equations are discretized with integral finite difference method and then solved numerically by Newton's method. The simulator is validated against a commercial compositional software (CMG-GEM) before it is applied to simulate gas injection. Huff-n-puff with dry gas in Eagle Ford is investigated. The simulation result shows that if the reservoir pressure is much higher than the bubble point pressure, the nanopore confinement effect will have a minimal impact on the recovery factor (RF) for both the depletion and the first few cycles of gas huff-n-puff. Geomechanics is found to be an influencing factor on RF but not always in a detrimental way, as enhanced rock compaction drive could offset the reduction of permeability in certain scenarios. Gas huff-n-puff would improve the RF of each component compared with the depletion. The heavy component would first have a higher recovery than the light component at the first few cycles of huff-n-puff, but its RF will be outpaced by the light component when the gas saturation in the matrix surpasses the critical gas saturation. Lastly, considering the nanopore confinement effects would slightly reduce the RF of the light component but increase the RF of the heavy component after huff-n-puff when combined with the critical gas saturation effect in the matrix. This study presents a 3D multiphase, multicomponent simulator which is a practical tool for accurately modeling of primary depletion as well as gas injection IOR/EOR processes in unconventional oil reservoirs. This simulator is not only of great importance for assisting researchers to understand complex multiphase and multicomponent behaviors in tight oil production but also of great use for engineers to optimize gas injection parameters in field applications.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".