New Seismic Base Reservoir Characterization (SBRC) Geomechanical Model Based on Mogi Empirical Rock Failure Relationship
Bibliographic record
Abstract
Abstract The objective of this paper is to develop a seismic based reservoir characterization (SBRC) geomechanical model using a non-linear relationship between the maximum and minimum principal stress and taking into account the intermediate principal stress. The proposed geomechanical model is constructed based on Mogi's (1967) empirical rock failure relationship. This allows introducing for the first time in the SBRC the intermediate principal stress. In order to do it, in-situ stress variations as a function of depth are used to match failure stresses for five groups of rock failure experiments that have different values of least principal stress. Next the relationship is integrated with the diffusivity equation for estimating large-scale permeability. German Continental Deep Drilling Project (KTB) data are used to test the validity of the new SBRC model. Two ‘hydraulic fracturing’ depths (9100 and 6360m) in the KTB site are used to demonstrate the methodology. Minimum permeabilities are calculated with the new SBRC to be 2.9×10−20 m2 at 6360 m and 3.1×10−20 m2 at 9100 m. Maximum permeabilities are 2.3×10−16 m2 at 6360 m and 2.9×10−16 m2 at 9100m. Thus the maximum permeability is approximately 4 orders of magnitude larger compared with the minimum permeability (1 m2 = 1.013E15 md). Novelties of the approach can be summarized as follows: (1) the use of Mogi's (1969) empirical rock failure relationship for integrating intermediate principal stress into the SBRC geomechanical model (this cannot be done when using for example the Mohr-Coulomb rock failure criterion), (2) using a nonlinear relationship between the maximum and minimum principal stress and (3) calculating large-scale permeability.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".