EOR Methods to Enhance Gas Oil Gravity Drainage:Modeling Aspects
Bibliographic record
Abstract
Abstract Conventional displacement methods such as water flooding do not work effectively in densely fractured reservoirs: due to the high fracture permeability it is not possible to establish significant pressure differentials across oil bearing matrix blocks to drive oil from matrix rock towards producers. In such reservoirs one has to rely on natural mechanisms like capillary imbibition or gravity to recover oil from the matrix reservoir rock. In Middle-East fractured carbonates, the matrix rock is commonly oil-wet or mixed wet and only gravity drainage remains a feasible process. However, permeabilities are usually low, <10 mDarcy, resulting in low gravity drainage production rates with high remaining oil saturation and/or capillary holdup. EOR techniques such as steam injection and miscible gas injection have the potential to improve GOGD rates and recoveries: –In shallow fractured reservoirs it is possible to inject steam in the fracture system. Steam will condense as long as it contacts cooler matrix rock, resulting in a steam front that develops in a stable way through the fracture system. Heating of the matrix will result in oil expansion, reduction of viscosity, gas drive and stripping effects.–In deeper reservoirs GOGD under miscible conditions becomes an option. Injection gas that is miscible with the oil will result in swelling and viscosity reduction, both increasing oil mobility and therefore improving the GOGD rates. Miscibility further adds the advantages of single-phase flow at high effective relative permeability and reduced interfacial tension, thereby reducing re-imbibition effects and increasing ultimate recovery from inhomogeneous reservoirs. To assess the benefit of these EOR methods, simulation techniques should be capable of modelling the impact of these processes on GOGD. We present in this paper a general dual permeability method that can handle GOGD as well as different, mutually interacting, processes expected to occur when EOR techniques are applied to fractured reservoirs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".