Case Study on Using Mundu-Paciran Nannofossil Zones (MPNZ) to Subdivide Mundu and Paciran Sequences in the MDA Field, East Java Basin, Indonesia
Bibliographic record
Abstract
The Husky-CNOOC Madura Limited (HCML) MDA-4 exploration well (2011) in the Madura Strait region targeted Globigerina limestones in the Mundu Sequence (3.8 Ma) and the Paciran Sequence (2.0 Ma). The MDA Field is covered by Merpati 3D Seismic (2005). Seismic features observed from the 3D volume include phase change or polarity reversal at the top of gas filled reservoirs of the MDA structure and DHI flat-spot approximating to the gas-water contact (GWC). The reservoirs are primarily planktonic foraminifera grainstones, packstones and wackestones that have been deposited as pelagic rains and were subsequently redistributed by sea floor bottom currents. Differentiating the Mundu and Paciran Sequences relies heavily on biostratigraphy and chronostratigraphy, as there are no significant lithological features that can be observed between the sequences. This article introduces a method to construct detailed well correlations of the two sequences based on Mundu–Paciran Nannofossil Zones (MPNZ), using high resolution biostratigraphy events. The methodology uses varying nannofossil abundances in the interval NN18 (Late Pliocene) to NN11 (Late Miocene). The best reservoir performance in the study area may occur in the MPNZ-7 and MPNZ-6, which were deposited at the late stage of the depositional cycles.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".