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Record W3133704455 · doi:10.1190/int-2020-0167.1

Mechanical stratigraphy of Mississippian strata using machine learning and seismic-based reservoir characterization and modeling, Anadarko Basin, Oklahoma

2021· article· en· W3133704455 on OpenAlexaboutno aff
Javier Tellez, Matthew J. Pranter, Carl Sondergeld, Chandra Rai, Jing Fu, Heyleem Han, Son Dang, Chris T. McLain

Bibliographic record

VenueInterpretation · 2021
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsnot available
Fundersnot available
KeywordsGeologyStratigraphySedimentary rockPetrologyDrillingStructural basinBrittlenessPaleontologyTectonics

Abstract

fetched live from OpenAlex

The Sooner Trend in the Anadarko (Basin) in Canadian and Kingfisher counties play primarily produces oil and gas from Mississippian strata. The interval consists of interbedded argillaceous mudstones and calcareous siltstones. Such a contrast in rock composition is linked directly to the mechanical stratigraphy of the strata. Brittle (calcareous siltstones) and ductile beds (argillaceous mudstones) are related to the sequence-stratigraphic framework at different scales. We have used seismic and well-log data to estimate and map the geomechanical properties’ distribution and interpret the mechanical stratigraphy of rocks within the Mississippian strata. First, we defined the parasequences that form the main reservoir zones of the Meramecian-Mississippian strata. Once we established the stratigraphic framework, we estimated and compared rock brittleness index (BI) using two independent laboratory-based measurements from the core. The first method, the mineralogical-derived BI, uses mineralogical composition inverted from Fourier-transform infrared spectroscopy analyses, whereas the second method, the mechanical-derived BI, involves measurements of compressional and shear velocities from core plugs. We use the data from core-plug velocity measurements along with well logs and an artificial neural network approach to establish relationships among the geomechanical properties, well logs, and acoustic impedance values. We then applied these relationships to generate 3D geomechanical models constrained to seismic volumes. The resulting grid distributions illustrate the stratigraphic variability of the properties at the parasequence scale. Overall, brittle strata decrease in thickness and abundance basinward as the frequency of interbedded brittle and ductile zones increases and gradually transitions into thin calcite-cemented siltstones and clay-rich mudstones. Analysis of the production performance of selected horizontal wells drilled within the Mississippian strata indicates that the proportion of brittle and ductile rocks along the well path drilled and the drilled area vertical stacking pattern play a significant role in hydrocarbon production for these Mississippian units.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.425
Threshold uncertainty score0.540

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.243
Teacher spread0.228 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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