Numerical investigation of dynamic and static properties of reservoir rocks
Bibliographic record
Abstract
A 3D geomechanical model describes the elastic and mechanical properties of rock as well as underground stresses. The static elastic parameters of rock are required to build a model. However, the elastic properties resulting from wireline logs, dynamic experiments and seismic inversion are dynamic and must be converted. Implementing an accurate conversion is an essential part of any 3D geomechanical model. The static and dynamic moduli can be obtained by numerical and experimental methods. Laboratory experiments are known to provide more realistic outcomes, but this method has its constraints such as availability of samples, time constraints and limitation of experimental resources. Other approaches such as numerical modelling can be used supplementarily to compute the mechanical behaviour and elastic parameters of sandstone. This paper provides a literature review on past numerical modelling efforts to examine dynamic and static parameters of rocks. This is followed by an explanation of grain and core scale model and research methodology. A discrete element model-based numerical simulation is then carried out using Itasca’s particle flow code in 3D. The digital plug scale specimen was calibrated to replicate the experimental findings and was then used to establish a broad sensitivity analysis on the important parameters. The simulation results were in good agreement with experiments on sandstone specimens. The present study forms a foundation for building a more reliable 3D geomechanical model and consequently better field development, reducing risks and lowering costs.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".