Geomechanical Technology for Seal Integrity Analysis: The Three-Step Approach
Bibliographic record
Abstract
Abstract A clear understanding of the subsurface pore pressure and the presence of geological seals are significant considerations when designing safe wells and optimal production strategies for oil and gas resources. This paper highlights geomechanical techniques and tools which have been developed to assure injection operations for large scale EOR projects in South-East Asia. A key concern is if depletion or injection will lead to reservoir or seal failure by tensile fracturing or shear faulting. If so, will these fractures and faults "grow" upwards and provide a pathway for fluids to escape to the seafloor? First, a 1D-model is made of the total stresses and pore fluid pressures and how these change as a function of depth and depletion/injection. This empirical approach helps to highlight the zones of relatively low minimum effective stress where there is greater risk of rock deformation which will impact operations. Where more detail and refinement is required in terms of the identified risks, we analytically describe the reservoir and overburden deformation with the theory of poro-elasticity, Mohr-Coulomb-type shear faulting, and tensile fracturing. Analytical geomechanics aims at a mechanistic understanding using simplified geology and simplified pore pressure patterns, but with realistic mechanical rock properties. The third step delivers greater detail of the technical complexity using a 3D finite-element simulator. These numerical techniques can simulate the effects of complex structural geology or intra-reservoir compartmentalization, inhomogeneous depletion, and spatial variation in rock mechanical properties. It makes sense to start simple and gradually bring in more complexity whilst adjusting geomechanics support accordingly. We demonstrate the above three-step approach with examples from projects in the South China Sea, offshore Malaysia, which illustrate the multi-disciplinary aspect of geomechanics and its impact on safety, efficiency (costs), and the technical reputation of our company and industry.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".