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Record W4408136589 · doi:10.3997/2214-4609.202477186

Integrated Reservoir Characterization of Complex Globigerina Limestone Reservoir in Madura Strait: Case Study of MAC Field

2024· article· en· W4408136589 on OpenAlexaff
Vian Indrasatwika, Asep Handaya Saputra

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geophysical Studies
Canadian institutionsHusky Energy (Canada)
Fundersnot available
KeywordsGeologyField (mathematics)Reservoir modelingGeotechnical engineeringMathematics

Abstract

fetched live from OpenAlex

Summary This study examined a complex Globigerina Limestone reservoir in the MAC field, Madura Strait, Indonesia. The goal was to obtain an understanding of the reservoir characterization using the combination of seismic inversion, facies association, and rock type identification. Petrographic analysis plays important role in the lithofacies and rock type identification. Five rock types were identified, showing different relationship of porosity and permeability, and associated with certain pore type and size. Moreover, facies development generally controlled by paleo-depositional structure. The sediments were winnowed by the currents and deposited in a clinoform pattern. This structure has four recognized facies associations: restricted shelf, upper-middle foreslope, and lower foreslope. Seismic inversion was used to predict porosity distribution across the reservoir, aiding in reservoir characterization. Gas presence gives additional effect to the seismic response. Based on AI, gas reservoir is well separated from the non-gas reservoir. Secondary porosity commonly appears, showing that diagenetic processes significantly altered the original pore system, but depositional environment also influenced the type of secondary porosity formed. Reservoir model shows a good relationship between rock type and well’s productivity, associated with high porosity and permeability. The outcome of this study can be used as the intake for further development plan.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.109
Threshold uncertainty score0.999

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)0.0020.000

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 head0.048
GPT teacher head0.266
Teacher spread0.218 · 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.

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
Published2024
Admission routes1
Has abstractyes

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