Advanced Meandering Fluvial Reservoir Characterisation for Static Model Improvement
Bibliographic record
Abstract
Abstract Modelling of meandering fluvial reservoirs with point bars and crevasse splays is very challenging. The conventional modelling approaches, especially for meandering fluvial reservoirs, are mainly controlled by wells, which have contributed to uncertainties in lateral variations between and away from well control. Integration of the improved sedimentology, geophysics and 3D reservoir geomodelling techniques of fluvial reservoir system are proposed in the study. In stratigraphic and structural framework building, the improved methodologies included 3D seismic geobody extraction, stratal slicing and high order architectural elements interpretation. 3D geobody extraction and stratal slicing technique enhanced interpreter ability to visualize fluvial features at specific time-equivalent stratigraphic surface. In lithofacies modelling, more refined high-order architectural elements were modelled using methodologies included 3D facies seismic probability, local varying azimuth and dip angle to capture lateral accretion of point bars inside the channels for better facies distributions following point bar architectures. In property modelling, porosity was populated using Gaussian Random Function Simulation constraint to lithofacies trend to control the distribution of porosity away from wells. This methodology resulted in the porosity distributions being well controlled following the lithofacies trend. The proposed workflows and methodologies enable geomodeller to produce a more geological realistic meandering fluvial reservoir model with internal lithofacies and property distribution honouring well data and input distribution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".