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Petrophysical Evaluation of Lithology And Mineral Distribution with an Emphasis on Feldspars and Clays, Middle and Upper Williams Fork Formation, Grand Valley Field, Piceance Basin, Colorado

2014· article· en· W7623679 on OpenAlexaboutno aff
Jeremy Daniel Ring

Bibliographic record

VenueMedical education · 2014
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsnot available
Fundersnot available
KeywordsGeologyLithologyPetrophysicsCanyonFork (system call)Mineral resource classificationStructural basinArchaeologyGeochemistryGeomorphologyGeographyGeotechnical engineering

Abstract

fetched live from OpenAlex

Petrophysical evaluation of lithology and mineral distribution with an emphasis on feldspars and clays, middle and upper Williams Fork Formations, Piceance Basin, Colorado. Understanding accessory mineralogy occurrence and distribution is critical to evaluating the reservoir quality and economic success of tight–gas reservoirs, since the occurrence of iron–rich chlorites can decrease resistivity measurements and the occurrence of potassium feldspar increases gamma–ray measurements, resulting in inaccurate water saturation and net–to–gross calculations, respectively. This study was undertaken to understand the occurrence and distribution of chlorite and potassium feldspar in the middle and upper Williams Fork Formations of the Piceance Basin at Grand Valley Field. Eight lithofacies are identified in core based on grain–size, internal geometry, and sedimentary structures. Four architectural elements (channel fill, crevasse splay, floodplain, and coal) were determined from lithofacies relationships, and then associated with well–log responses. Logs and models were used to determine the occurrence and distribution of lithology, architectural elements, chlorite and potassium feldspar, as well as the relationships between minerals and lithology and architectural elements. Net–to–gross ratios vary stratigraphically, from 8% to 88%, with a higher average in the middle Williams Fork Formation (58.3%) than in the upper Williams Fork Formation (48.5%). Volumetric proportions vary stratigraphically for both channel fills (18– 75%) and crevasse splays (1–7%). The average volume percent of chlorite and potassium feldspars are both <1%, with P 50 values of 1.3% and 7%, respectively. Chlorite is pervasive at the base of the middle Williams Fork Formation: almost 90% of the sandstones in sand–rich intervals contain chlorite. The distribution of chlorite did not vary between reservoir architectural elements, with 70% of both crevasse splays and channel fills containing chlorite. The results of this study show that, for the middle and upper Williams Fork Formations at Grand Valley Field, 1) there are eight lithofacies and four architectural–element types identified from core; 2) the occurrence and distribution of accessory minerals (<10%) of chlorite and potassium feldspar can be accurately estimated from limited core and well–log data; 3) chlorite occurrence does not vary significantly between reservoir architectural elements; 4) the abundance of chlorite near completion intervals and the occurrence of potassium feldspar in calculated mudstone lithologies indicate a need to re–evaluate the utilization of saturation models and lithology calculations in reservoir–quality evaluations.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.368
Threshold uncertainty score0.732

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.254
Teacher spread0.244 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2014
Admission routes1
Has abstractyes

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