HIGH-RESOLUTION MAPPING OF ORGANIC CARBON DISTRIBUTION IN THE LOWER HURON MEMBER OF THE OHIO SHALE, EASTERN OHIO, USA
Bibliographic record
Abstract
The Devonian shales of the Appalachian Basin have been studied extensively for hydrocarbon resource potential; however, the increasing demand for carbon capture, utilization, and storage calls for reassessment of the properties of Devonian shale formations at a finer scale. The goal of this project is to estimate and map total organic carbon (TOC) distribution in the lower Huron Member of the Ohio Shale at a high geographic and stratigraphic resolution using geophysical logs. This project uses the high-resolution stratigraphic framework for the lower Huron Member developed by the Ohio Department of Natural Resources, Division of Geological Survey, which revealed changes in basin morphology throughout deposition of eight Milankovitch-scale transgressive-regressive cycles. For this study, TOC content was estimated and mapped across each depositional cycle to determine if changes in basin morphology impact the distribution of high-TOC zones. Previously published TOC data from cores that penetrate the Devonian shale interval were compiled and used to develop an equation to estimate TOC using gamma-ray and bulk density logs. A multivariate regression analysis was performed in Microsoft Excel to develop the equation, and GeoGraphix software was used for geophysical log analysis and TOC estimation. Maps showing the average TOC content across eastern Ohio for each of the eight lower Huron cycles were generated. In each cycle, TOC weight percent is highest in the west and decreases to the east as the units thicken into the basin center. The upper four cycles have overall lower average TOC estimates, which also corresponds with a change in basin strike. Localized TOC highs and lows (bullseyes) seem to cluster around potential bathymetric highs and fault systems.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".