Feasibility Study of WAG Injection in Naturally Fractured Reservoirs
Bibliographic record
Abstract
Abstract The fundamental aspects of Water Alternating Gas (WAG) injection are still not well understood. There are a few applications in fractured media and these show potential1. This study looks at the sensitivity of production to reservoir and fluid properties on a pattern scale using tools derived from experimental design. Also a look has been taken into the drive mechanisms in the fractured media that play an important role during WAG injection. Earlier studies claimed that WAG injection in fractured reservoirs is not the best improved oil recovery (IOR) method2. However when the conditions are fully understood and the injection is modeled correctly, it is optimal in some fractured reservoirs. The proper modeling of fractured reservoirs remains difficult. The standard dual porosity formulation in commercial simulators is based on a continuous matrix grid overlaid by a continuous fracture grid. The transfer of fluid from matrix to fracture is described by a transfer function. The heart of this transfer function is the shape factor, sigma. As can be seen in a fine-scale grid, the shape factor is different for different reservoir properties and injection types. The upscaling of this sigma from the fine-scale grid to the pattern scale model is important for correct modeling. This study performs several fine scale single porosity simulations to properly upscale the flow model to a dual system. The process of determining sensitivities in an organized manner on properly upscaled models will be shown here with a limited number of parameters. Fractured reservoirs can be divided into different categories3. Based on a fine grid model of one type of fractured media, a dual porosity model with varying parameters is set up. This yields recovery outputs across a range of reservoir properties that are represented by two dimensionless numbers. These numbers represent the capillary over viscous and gravity over viscous forces. By looking into the mechanisms that are behind the recovery in different balances of forces, an insight into when and why WAG is working is given.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".