The value of high-density blended OBN seismic for drilling and reservoir description at the Tangguh gas fields, Eastern Indonesia
Bibliographic record
Abstract
Abstract The Tangguh gas fields in Eastern Indonesia are overlain by a complex overburden, including a thick, heavily faulted, and intensely karstified carbonate interval that tends to scatter and attenuate seismic energy. Development drilling is challenging, with the potential for pack-offs and stuck pipe when drilling into unstable, partially collapsed caves or karstified fault planes while on total losses. Ideally, these karst features are to be avoided when planning and drilling wells, but avoiding them depends on having a well-resolved seismic image. Historical towed-streamer and sparse ocean-bottom cable seismic is low fold and does not give a satisfactory image for well planning. Advances in ocean-bottom node technology, computer processing, and capacity coupled with efficient survey design and blended acquisition utilizing multiple source vessels allowed a step change in data density. This provided a new high-quality seismic image to support future development activities. The advantages of densely sampled, full-azimuth data include rapid delivery of fast-track products (because high-quality images can be constructed with relatively simple processing flows), greatly improved overburden imaging, and a corresponding uplift in deeper imaging leading to enhanced reservoir characterization.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".