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Record W2342990926 · doi:10.2118/180369-ms

Detecting Opal-CT Formation Resulting From Thermal Recovery Methods in Diatomites

2016· article· en· W2342990926 on OpenAlexaff
Cynthia M. Ross, Bolivia Vega, Jing Peng, M. Ikeda, John Reuben Lagasca, G.-Q. Tang, Anthony R. Kovscek

Bibliographic record

VenueSPE Western Regional Meeting · 2016
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsConocoPhillips (Canada)
Fundersnot available
KeywordsSorptionPorosityPetrophysicsMineralogyDissolutionMaterials scienceBiogenic silicaAmorphous solidCrystalliteNanometreGeologyAnalytical Chemistry (journal)Chemical engineeringAdsorptionChemistryComposite materialCrystallographyDiatomChromatography

Abstract

fetched live from OpenAlex

Abstract Evaluation of the effects of thermal recovery methods upon diatomaceous reservoirs with their inherent high porosity and low permeability is problematic in that diatoms, a main component of their namesake rock, are composed of amorphous, hydrous biogenic silica (Opal-A) and can alter when heated. The opal-A to opal-CT transformation, is readily apparent using imaging methods, X-ray diffraction (XRD), and petrophysical measurements when the rock has been fully converted. In laboratory experiments with partial transformation, these changes, if any, are subtle and easily missed due to the minute amount of alteration products and the substantial amount of natural variability within the rocks. For example, XRD measurements may show an increase of 1 wt % in opal-CT after an experiment. It is not apparent whether additional opal-CT either formed as a result of the experiment or is a relative enrichment caused by the dissolution of more susceptible minerals such as opal-A and pyrite. A new method based on nitrogen sorption was developed to detect silica-phase alteration in diatomaceous samples. We observed that nanometer-scale pore-size distributions as measured via nitrogen sorption and processed using the classic BJH method differ for opal-A and opal-CT reservoir samples. Opal-A samples have less nanometer-scale pore volume (~0.1 cc/g), smaller nanoscale pore sizes (~3.8 nm), and distinct pore-size distributions compared to samples containing opal-CT (e.g., 0.3 cc/g and 6.6 nm). This method detects subtle amounts of opal-CT in that samples containing only 3 wt % (XRD) exhibit a distinct opal-CT peak at 7.8 nm in one example. These nanometer-scale pore-size changes occur whether micrometer-scale pores either increase in size (dissolution) or decrease in size (alteration). This method was applied to reservoir and quarry diatomites before and after laboratory experiments conducted at ambient to 230 °C temperatures, pH values of 6 to 10, durations of 10 hours to two years, different fluids, various pressures, and a gamut of flow conditions including spontaneous imbibition, forced imbibition, and static. Supporting data such as water chemistry and XRD data were also measured. Comparison of before and after BJH pore-size distributions reveals a reduction in peak size when dissolution occurs and a shift to larger nanometer-scale pore sizes when alteration (converting to opal-CT) occurs. Many samples exhibit both characteristics. The inlet side of the cores exhibit more dissolution and alteration than the outlet side of the same core. Other factors could also contribute to these changes in the nanometer-scale pore structure such as fines mobilization and compaction.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.425
Threshold uncertainty score0.550

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.028
GPT teacher head0.279
Teacher spread0.251 · 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 designBench or experimental
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

Citations4
Published2016
Admission routes1
Has abstractyes

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