Reconstructing a Three-Dimensional Geological Model from Two-Dimensional Depositional Sections in a Tide-Dominated Estuarine Reservoir: A Case Study of Oil Sands Reservoir in Mackay River, Canada
Bibliographic record
Abstract
A tide-dominated estuarine reservoir is an important oil reservoir. However, due to the force of bidirectional water flow, its internal structure is complex, and the heterogeneity is serious. Accurately establishing the tide-dominated estuarine reservoir model is a great challenge. This paper takes the Mackay River oil sands reservoir in Canada as the research object to establish the elaborate geological model of a tide-dominated estuarine reservoir. Through the meticulous depiction of core data, 14 kinds of lithofacies and nine kinds of architectural elements are identified, and the lithological and electrical response in sedimentary architectural elements is established. On this basis, the plane and vertical distribution of architectural elements, as well as the spatial superimposition patterns, are depicted and characterized through well seismic combination and plane and section interaction, and the representative plane and section architecture maps are obtained as 2D training images (TIs) for multi-point statistical modeling. The 2D TI is scanned by 2D data template to obtain the multi-point statistical probability of the 2D spatial architectural pattern. Then, the 2D multi-point probability is fused to generate three-dimensional (3D) multi-point statistical probability by the probabilistic fusion. Finally, Monte Carlo sampling is used to predict the spatial distribution of architectures, and an elaborate geological model of a tide-dominated estuarine reservoir is established. Compared with the traditional sequential indication modeling method, the point-to-point error of the model section based on the 2D section reconstruction method is only 25.92%, while the sequential indication modeling method is as high as 58.52%. Even far from the TI, the point-to-point error of the 2D section model is 33.13%. From the cross-validation, the average error of the 2D section is 11%, while the sequential indicator modeling error is 23.1%, which indicates that the accuracy of 2D reconstruction of the estuarine reservoir model is high, and this method is suitable for the establishment of the tide-dominated estuarine reservoir model.
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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 | 0.001 |
| 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) | 0.003 | 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".