Surface-Geometry and Trend Modeling for Integration of Stratigraphic Data in Reservoir Models
Bibliographic record
Abstract
Abstract Accurate prediction of reservoir performance depends on an accurate estimate of the subsurface structure, lithofacies, associated petrophysical properties, and fluid distribution. Often the reservoir heterogeneities that are controlled by stratigraphic architecture and sedimentological trends are difficult to predict, particularly at subseismic resolution and far from well control. An ongoing challenge in subsurface modeling has been the utilization of analogs of complex geology along with seismic and sparse well data to predict the natural geologic complexity in models of the subsurface. Time surfaces provide very important constraints on the geometric connectivity and continuity of facies and petrophysical properties in reservoirs. Such time surfaces are a suitable framework for facies and petrophysical properties modeling. Instead of modeling each reservoir later as a whole, the elementary sediment units are more easily modeled separately; the final composite model will show realistic heterogeneity patterns consistent with the underlying physics. This work presents a hybrid deterministic, rule-based, and stochastic technique to generate surface models. These surface models are utilized as a framework to preserve sediment trends and honor analog and well data. Petrophysical properties are modeled for each sediment unit to reproduce trends. Finally, the individual sediment units are assembled into a reservoir model. The surface model is created stochastically with parameterized surface templates. The shape, extent, height, orientation and regularity of the surfaces are controlled by user-specified distributions. The location of each surface in the reservoir is chosen on the basis of previous events. The addition of each surface is based on sedimentological rules. Conditional Gaussian simulation is used to ensure that the surfaces reflect realistic uncertainty through undulations and that well data intersections are honored. The surface model divides the reservoir layer into sediment units. From geology and well data, trends are parameterized with mathematical functions as trend templates. Residuals are characterized after removing the trend. For each sediment unit, a trend and a residual model are generated stochastically. The observed well logs serve as conditioning data to guide the deployment of trends and to condition the generation of residuals. The model of each sediment unit combines its trend plus residual. The final reservoir model is obtained by assembling the separate sediment units.
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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 | 0.001 |
| 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".