Towards an integrated restoration/forward geomechanical modelling workflow for basin evolution prediction
Bibliographic record
Abstract
Many sedimentary basins host important reserves of exploitable energy resources. Understanding of the present-day state of stresses, porosity, overpressure and geometric configuration is essential in order to minimize production costs and enhance safety in operations. The data that can be measured from the field is, however, limited and at a non-optimal resolution. Structural restoration (inverse modelling of past deformation) is often used to validate structural interpretations from seismic data. In addition, it provides the undeformed state of the basin, which is a pre-requisite to understanding fluid migration or to perform forward simulations. Here, we present a workflow that integrates geomechanical-based structural restoration and forward geomechanical modelling in a finite element framework. The geometry and the boundary kinematics derived from restoration are used to automatically create a forward geomechanical model. Iterative correction may then be performed by either modifying the assumptions of the restoration or modifying the restoration-derived boundary conditions in the forward model. The methodology is applied to two problems; firstly, a sand-box scale benchmark model consisting of sand sediments sliding on silicon leading to the formation of a graben structure; secondly, a field-scale thrust-related anticline from Niger Delta. Two strategies to provide further constraint on fault development in the restoration-derived forward simulation are also presented. It is shown that the workflow reproduces the first order structural features observed in the target geometry. Furthermore, it is demonstrated that the iterative approach provides improved understanding of the evolution and additional information of current-day stress and material state for the Niger Delta Case.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".