3D Post-Stack Acoustic Impedance Inversion Results for Kijing and Malong Fields, South Natuna Sea
Bibliographic record
Abstract
Abstract Post-stack acoustic impedance inversion was performed at Kijing, Malong, and Buntal fields. The results allow detailed reservoir characterization of the fields to be done, and predict the location of porous, reservoir-quality sands, and in many cases, reservoir-quality sands that contain gas. Several aspects of the inversion results prove to be useful that cannot be achieved through the use of seismic analysis alone. The removal of wavelet effects (tuning, extra events due to constructive wavelet sidelobe interference, and dimming due to destructive wavelet sidelobe interference) is one important enhancement. Seeing the impedances as layer properties instead of as interfaces allows information about the sands and shales to be observed. Increase in bandwidth, both on the low end with the introduction of a good low frequency model from the interpolation/extrapolation of well log impedance along interpreted horizons and the use of geologic constraints, and on the high end due to the use of a spectral whitening deconvolution operator and proper inversion parameters produces increased resolution. The inversion results at Kijing, Malong, and Buntal fields may be used to discern details about the reservoir sands, especially in conjunction with the current geologic and geophysical information from conventional seismic data, well logs, analogs, etc. 3D visualization of the reservoir shows the structure of the sands, most of which are channelized.
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 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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".