Use of resistivity and density borehole image logs to identify and distribute facies in the Pikka unit — A case study from the Nanushuk Formation, North Slope, Alaska
Bibliographic record
Abstract
Abstract Over the past few decades, the North Slope of Alaska has yielded several major hydrocarbon discoveries in the deltaic topsets of the Brookian Nanushuk Formation. Together, the Nanushuk topsets and genetically related foreset and bottomset beds of the Torok Formation comprise part of a giant clinothem system that prograded across the Colville Foreland Basin during the lower Cretaceous (the Aptian through the Cenomanian). The Nanushuk Topset Play contains stratigraphically trapped hydrocarbons within multiple fairways trending roughly north to south along the basin’s extent. Inside the Pikka Unit, the Nanushuk Formation represents a shelf-edge delta and attached shoreface system, defined primarily from sedimentological interpretations of conventional cores integrated with 3D seismic reflection data and well-log motifs. In this study, we build a structural and stratigraphic framework for the Nanushuk reservoir across the Pikka Unit through integrated observations of the borehole image logs, seismic data, and core. In the described workflow, the boundaries delimiting dip zones interpreted in the wells are correlated to higher order stratigraphic cycles within the Nanushuk clinothem zone. These boundaries are observed in seismic profiles and provide the basis of the structural framework. In addition, core facies interpretations are calibrated to CT-scan data to establish each facies character and vertical distribution within the wells. In the absence of core data, core-calibrated borehole images are instrumental in guiding sedimentological and structural interpretations. This extrapolation is dependent on the image log quality and resolution. The calibrated facies interpretations are applied to wells without core data and guided by seismic amplitude interpretation to extrapolate reservoir presence and distribution across the area.
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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".