Seismic-based characterization of reservoir heterogeneity within the Meramec interval of the STACK play, Central Oklahoma
Bibliographic record
Abstract
The Meramec interval within the Sooner Trend Anadarko Canadian Kingfisher (“STACK”) play of the Anadarko Basin in central Oklahoma has recently been at the epicenter of increased exploration and production of oil and gas. It has become one of the top target intervals of the “mid-continent” aided by technological advancements in horizontal drilling and completion techniques. The Meramec interval, mainly composed of argillaceous siliciclastic sediments with varying amounts of carbonate cement, exhibits high porosity heterogeneity, which is theorized to be caused by varying amounts of clay and postdepositional calcite cement. Characterization of the porosity heterogeneity in the Meramec interval will improve our understanding of the wide range in Meramec oil and gas production volumes and reduce the risk associated with drilling and completion techniques. We completed an initial interpretation followed by inversion of 3D seismic data in which we generated a detailed characterization of the porosity heterogeneity and overall reservoir quality within the Meramec interval over an area of approximately 150 square kilometers. We then used the 3D seismic volume and available well logs to map the vertical and lateral extents of the Meramec interval and identify possible structural elements that could affect the reservoir quality. A petrophysical analysis of the well logs confirmed porosity heterogeneity and variations in volumetric calculations of clay and carbonate minerals. Finally, we generated a set of porosity volumes using the acoustic impedance from seismic inversion and probabilistic neural network methods. The derived porosity volume helped us identify porous and nonporous intervals within the Meramec throughout the study area. The results improved our understanding of Meramec heterogeneity, further reducing the risk associated with well planning, drilling, and completion.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".