Evaluating geomechanical effects related to the production of a Brazilian reservoir
Bibliographic record
Abstract
Abstract This paper presents a coupled finite element approach for modeling geomechanical effects induced by production/injection processes in petroleum reservoirs. The module developed employs coupled- reservoir analysis using CMG IMEX® as the flow simulator and a finite element program in MATLAB® as the stress–strain simulator, in a two-way explicit partial coupling scheme. The flow and mechanical problems are coupled by the change of effective stress due to the change in pore pressure and by varying stress-dependent reservoir properties, such as pore compressibility, absolute permeability, and porosity. The coupling procedure was applied to the Namorado Field (Campos Basin, Brazil) to quantify the impact of the rock deformation on fluid recovery. Based on the cases studied, the coupled analyses predicted higher oil recovery than the conventional reservoir simulations. The results showed that the reservoir deformation can affect its performance and must be taken into account in reservoir-engineering studies depending on production strategy and reservoir stiffness. Besides, the geomechanical calculations were performed only in the coupling timesteps, reducing the computational effort and making this coupling method feasible on a field scale.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".