Development of a Parallel Simulator Utilizing Multiple Interacting Continua and Embedded Discrete Fracture Models in Fractured Unconventional Reservoirs
Bibliographic record
Abstract
Abstract Unconventional petroleum reservoirs, such as shale gas and tight oil reservoirs, have changed the entire energy equation in the world. An accurate and efficient reservoir simulator is essential for the development and management of these reservoirs and the optimization of their production schedules. However, the gas storage and transport mechanisms in ultra-tight matrix, including gas adsorption/desorption, non-Darcy flow, and surface diffusion, are different from those in conventional petroleum reservoirs. In addition, hydraulic fracturing techniques are often required to achieve their economical production, which leads to existence of complex fracture networks in the unconventional reservoirs. These features of unconventional reservoirs make their accurate numerical simulations a big challenge. In this paper, we develop a simulator for fractured unconventional reservoirs, which takes the specific gas storage and transport mechanisms into consideration, employs a multiple interacting continua (MINC) model to handle well connected natural fractures, utilizes an embedded discrete fracture model to simulate large-scale disconnected hydraulic fractures, and uses a coupled model to efficiently describe multi-scale fractures with irregular geometries. To reduce the computational time, parallel computing techniques are also employed, with which large-scale reservoir simulation cases can be finished in practical time. From the numerical experiments, we can see that reasonable physical phenomena is captured and accurate predictions are performed by this simulator.
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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) | 0.000 | 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 | 0.001 |
| 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".