Streamline-based Reservoir Geomechanics Coupling Strategies for Full Field Simulations
Bibliographic record
Abstract
Summary Geomechanics is a recent added physics into reservoir simulators that considers the interaction between reservoir fluid and rock, which is quite important in accurate recovery prediction of stress-sensitive reservoirs. However, inclusion of geomechanics into fluid flow simulation workflows has been computationally crucial and known as a bottleneck in both fully coupled and sequentially coupled scenarios. Therefore conventionally the geomechanical simulation of reservoirs is either neglected or is investigated only in the vicinity of injection/production well that is subjected to more pressure changes. To tackle this problem, and integrate geomechanics in field scale, streamline-based class of hydromechanical couplings were used. This paper presents and implements different coupled sequentially implicit and semifully implicit geomechanics-streamlines simulation techniques for simulation of large reservoirs with elastic geomechanical constitutive equations. The main idea behind inclusion of geomechanics in streamline simulation lies in the streamline time-stepping, which is different from conventional flow simulation time-steps: convective time steps, user induced time steps, and saturation forward sub-interval time steps. On the other hand, since porosity, and permeability change dynamically due to geomechanical rock-fluid interactions, the pressure field needs to be updated by selection of proper time steps. This work also provides a basis for selection of the coupling strategy to have a numerically and physically stable coupling strategy for both of compressible and incompressible scenarios. The techniques were tested on a highly heterogeneous two-dimensional plain-stress model, and a three-dimensional full-field case. Sensitivities on the number of grid-cells were performed and provided a good foundation to assess the power of each single coupling technique for each particular application and reservoir type. The finding of this paper can be used for optimization techniques where inclusion of geomechanics is essential as well.
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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) | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".