Seismic reservoir characterization of Utica-Point Pleasant Shale with efforts at quantitative interpretation — A case study: Part 1
Bibliographic record
Abstract
Abstract The Utica Shale is one of the major source rocks in Ohio, and it extends across much of the eastern United States. Its organic richness, high content of calcite, and development of extensive organic porosity make it a perfect unconventional play, and it has gained the attention of the oil and gas industry. The primary target zone in the Utica Play includes the Utica Formation, Point Pleasant Formation, and Trenton Formation intervals. We attempt to identify the sweet spots within the Point Pleasant interval using 3D seismic data, available well data, and other relevant data. This has been done by way of organic richness and brittleness estimation in the rock intervals. The organic richness is determined by weight % of total organic carbon content, which is derived by transforming the inverted density volume. Core-log petrophysical modeling provides the necessary relationship for doing so. The brittleness is derived using rock-physics parameters such as the Young’s modulus and Poisson’s ratio. Deterministic simultaneous inversion along with a neural network approach are followed to compute the rock-physics parameters and density using seismic data. The correlation of sweet spots identified based on the seismic data with the available production data emphasizes the significance of integration of seismic data with all other relevant data.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".