Mississippian Meramec lithologies and petrophysical property variability, stack trend, Anadarko Basin, Oklahoma
Bibliographic record
Abstract
Mississippian Meramec reservoirs of the Sooner Trend in the Anadarko (Basin) in Canadian and Kingfisher Counties (STACK) play are comprised of silty limestones, calcareous siltstones, argillaceous calcareous siltstones, argillaceous siltstones, and mudstones. We found that core-defined reservoir lithologies are related to petrophysics-based rock types derived from porosity-permeability relationships using a flow-zone indicator approach. We classified lithologies and rock types in noncored wells using an artificial neural network (ANN) with overall accuracies of 93% and 70%, respectively. We observed that mudstone-rich rock type 1 exhibits high clay and relatively low calcite, whereas calcareous-rich rock type 3 has high calcite and low clay content with rock type 2 falling in between as a balance between rock types 1 and 3. Results of the ANN were applied to a suite of well logs in noncored wells in which we generated lithology and rock-type logs for the Meramec. We identified that the Meramec consists of seven stratigraphic units characterized as strike-elongate, shoaling-upward parasequences; each parasequence is capped by a marine-flooding surface. The lower three parasequences (lower Meramec) form a retrogradational parasequence set that back steps to the northwest and is capped by a maximum flooding surface. The upper Meramec is characterized by parasequences that form an aggradational to progradational stacking pattern followed again by a retrogradational trend. We predict that the parasequence stacking, associated lithology distribution, and diagenetic cements appear to control the spatial distribution of petrophysical properties (porosity, permeability, and water saturation), pore volume, and hydrocarbon pore volume (HCPV). Calcareous-rich lithologies exhibit lower porosity, permeability, HCPV, and higher water saturation. We deduced that argillaceous-rich lithologies that occur near the maximum flooding surface are the most favorable reservoir intervals because they exhibit relatively higher porosity, permeability, HCPV, and lower water saturation. Productivity could not be directly correlated to rock types as operational and completion factors as well as overpressure and oil phase play important roles on production.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".