A practical petrophysical model for a source rock play: The Mancos Shale
Bibliographic record
Abstract
Recently, the focus in source rock exploration has moved from gas-rich to liquid-rich plays and warrants revisiting “bypassed” hydrocarbon charged source rocks, which were deemed uneconomic when first drilled. In North America’s oil fields, there are thousands of wells with different vintages of nuclear and electrical logs, yet these wells generally lack any advanced logs beyond the traditional triple combo. We have developed a workflow that uses a considerable amount of laboratory measurements made on crushed rock to upscale a petrophysical model based on a triple combo logging suite only. The model divides the field (laterally) in oil window and gas window fairways and (vertically) in petrophysical units. The remaining hydrocarbon generation potential is based on geochemical measurements, such as thermal maturity and total organic carbon content (TOC), from core and cuttings in the area. The petrophysical units reflect major geologic intervals with similar porosity and clay content. The workflow was sequentially built by correlating logs with core measurements, using TOC and maturity for organic matter, X-ray diffraction for mineralogy and grain density, porosity, and water saturation from fluids extraction, for volumetrics. The model is applied to the Mancos Shale (New Mexico, USA), a Cretaceous-age source rock, which includes the Niobrara Formation. The Mancos Shale has been penetrated in various fields while developing conventional sandstone reservoirs. The model is validated with measurements on a core recently acquired in the anticipated high-hydrocarbon-yield window. Petrophysical properties predicted from logs agree well with core measurements in blind tests, demonstrating the robustness of the model despite being based on a basic suite of logs and a simple deterministic approach. This model is now routinely used by the asset team as an automated workflow to generate fairway maps, locate sweet spots, and for landing lateral wells.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".