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Record W3110505241 · doi:10.1190/int-2020-0169.1

Stratigraphic variability of Mississippian Meramec chemofacies and petrophysical properties using machine learning and geostatistical modeling, STACK trend, Anadarko Basin, Oklahoma

2021· article· en· W3110505241 on OpenAlexaboutno aff
Lura Hardisty

Bibliographic record

VenueInterpretation · 2021
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsnot available
Fundersnot available
KeywordsGeologyPetrophysicsStructural basinStack (abstract data type)GeostatisticsKrigingMining engineeringGeomorphologyGeotechnical engineeringSpatial variabilityMachine learning

Abstract

fetched live from OpenAlex

Abstract Mississippian Meramec deposits and reservoirs in the Sooner Trend in the Anadarko (Basin) in Canadian and Kingfisher counties (STACK) play of Oklahoma are comprised of silty limestones, calcareous sandstones, argillaceous-calcareous siltstones, argillaceous siltstones, and mudstones. We used core-derived X-ray fluorescence (XRF) data and established environmental proxies to evaluate the occurrence of specific elements (Al, K, Ti, Zr, Sr, Ca, and Si) and to illustrate their stratigraphic variability. For the Mississippian Meramec, six indicator elements or element ratios serve as proxies for clay (Al and K), detrital sediment (Ti and Zr), carbonate deposits (Sr and Ca), calcite cement (Sr/Ca), and quartz (Si/Ti and Si/Al). We used an unsupervised K-means classification to cluster elemental data from which we interpret three chemofacies: (1) calcareous sandstone, (2) argillaceous-calcareous siltstone, and (3) detrital mudstone. We used random forest to relate core-derived chemofacies to well logs and classify chemofacies in noncored wells with an accuracy of up to 83% based on blind tests. We integrated core-derived XRF, conventional well logs, and chemofacies logs to produce a cross-sectional chemofacies model that trends from the northwest to the southeast across the STACK trend. Chemofacies distribution indicates an upward increase of detrital mudstone from parasequences 1 to 3. Parasequence 3 is capped by a maximum flooding surface. From parasequences 4 to 5, an increase in argillaceous-calcareous siltstone and calcareous sandstone reflects the progradational stacking. Porosity is low in calcareous sandstones primarily due to calcite cement. Water saturation is high in argillaceous-calcareous siltstone, moderate in calcareous sandstone, and low in detrital mudstone. Interpreted biogenic quartz from element profiles corresponds to the calcareous sandstone chemofacies, which can be estimated from well logs and mapped. Effective porosity and water saturation models reflect the stratigraphic variability of chemofacies and rock types and can be predicted within the defined chemostratigraphic framework.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.941
Threshold uncertainty score0.458

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.233
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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