Coupled Reservoir Flow Simulation: Fault Leakage Analysis
Bibliographic record
Abstract
Abstract Petroleum project costs may increase tremendously if wells need to be shut in or abandoned. A scenario where this possibility exists occurs in a reservoir hanging a major fault that connects the petroleum field to the seafloor. During water injection operations the fluid pressure within the fault may increase to a value that leads to fault slip and hydrocarbon flow through to the seabed. The hydrocarbon leakage can result in severe environmental damage and must be avoided. So, a proper simulation model that enables the evaluation of the maximum injection pressure allowed to impede oil leakage during water injection projects is a necessity. The focus of this work is an offshore unconsolidated sandstone reservoir field located in Campos Basin, Brazil. This field has used waste water for injection since the beginning of production, and there are strong uncertainties about the limits of pressure injection and reservoir fault properties that are related to the project development. To achieve the objective of examine uncertainties of flow through a major fault in offshore oil reservoir undergoing waterflooding; a methodology is developed that enables the incorporation of key mechanisms (e.g. effective stress law) and parameters (e.g. volumetric strain) to solve a complex numerical field reservoir simulation problem that includes geomechanical process. The proposed methodology uses an external-iterative-coupled reservoir-geomechanical modeling approach to capture the link between fluid flow and in situ stress. As a conclusion this study will reveal the effects of dynamic changes of permeability and porosity on reactivation of faults in a real offshore reservoir (Field "A"), and set up a minimum safe water pressure injection level for the field. The methodology developed in this study is valuable for assessing other oil exploitation project scenarios where limited information and production uncertainties are present. This work is important for reservoir characterization, enhanced oil recovery and production applications such as oil rate leakage through a fault.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".