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Record W4383340875 · doi:10.1306/10242220140

Fluid evolution in deeply buried and karstified carbonate reservoirs of the central Tarim Basin, northwestern China

2023· article· en· W4383340875 on OpenAlexaff
Jiaqing Liu, Zhong Li, Malcolm W. Wallace, Ashleigh v.S. Hood, Yang Liu, Chaojin Lu, Sam J. Purkis, Peter K. Swart

Bibliographic record

VenueAAPG Bulletin · 2023
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsPetro-Canada
Fundersnot available
KeywordsTarim basinGeologyCarbonateStructural basinChinaCarbonate rockGeochemistryGeomorphologyPaleontologySedimentary rockArchaeology

Abstract

fetched live from OpenAlex

Abstract The Middle–Lower Ordovician Yingshan Formation is an important reservoir unit in the Tazhong oil field (Tarim Basin, northwestern China). This oil field is deeply buried (>5500 m [>18,045 ft]) and has endured a complex diagenetic history. To understand the mechanisms of reservoir formation, we conduct a broad portfolio of geochemical analyses on the calcite cements that fill the pores and fractures in samples retrieved from wells penetrating the Yingshan Formation. Our results identify six diagenetic episodes, each associated with the emplacement of different cements, varying from marine conditions (C1), near-surface to shallow burial (C2), intermediate-to-deep burial (C3 and C4), and infiltration of the formation with mixing of the underlying basinal brines with meteoric waters and hydrothermal fluids (C5 and C6) along northeast-trending strike-slip faults. The productive wells display evidence of a strong burial diagenetic overprint linked to exotic fluids (C5 and C6) along fractures. We conclude that eogenetic meteoric waters were vital in producing early diagenetic porosity within the Tazhong reservoir, which was subsequently refined by late burial (hypogenic) diagenetic processes. We anticipate our findings hold lessons for the evolution of other geodynamically active cratonic basins.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.605
Threshold uncertainty score0.436

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.007
GPT teacher head0.189
Teacher spread0.182 · 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

Citations7
Published2023
Admission routes1
Has abstractyes

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