Integrated Reservoir Characterization of Complex Globigerina Limestone Reservoir in Madura Strait: Case Study of MAC Field
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".