Carbonate shoal microfacies characterization using the interdependent of depositional systems and diagenesis of Kais Formation in the Matoa Field, Salawati Basin
Bibliographic record
Abstract
Matoa Field in the Salawati Basin, Papua, Indonesia was discovered in 1991. Oil production commenced in 1993, yielding a cumulative production of more than 21 MMBO by 2015. In the latest POD that was approved in 1999 and was valid until 2019, it was recommended to drill six additional wells within three integrated structures. By 2018, a static model was built, incorporating present-day island analogues and the latest interpretation from reprocessed 3D seismic data, paleontological data, and petrographic analyses. The latest static model renews the understanding on reservoir characteristics (geometry and microfacies) for the purpose of further field development. This paper discusses field analogues in correlation with an interplay of carbonate depositional environment with diagenetic evolution in the Matoa Field. Three candidates for possible present-day analogues of Matoa Field were screened i.e., Java Sea’s Thousand Islands, Kapoposang Islands of Spermonde Archipelago, and the Great Barrier Reef of Australia. The objective was to improve understanding of the conceptual geological model in the regional sense. Based on palaeogeomorphology (morpho-arrangements, shelf geometry and sedimentary architecture), the present-day Central Reef Zone of Wet Tropic at the Great Barrier Reef is fit to be utilized as an analogue to the continental, Miocene Salawati carbonate complex. Well-tied 3D seismic data, from which several multiple attributes were analysed, have been reprocessed twice. Attributes such as RMS Amplitude, realized RMS Amplitude, Sweetness and Relative Acoustic Impedance were analysed and extracted to understand the diagenetic features of Matoa Carbonate reservoir. The result shows that RMS Amplitude, in combination with petrographic – paleontological data, exhibits the most likely attribute to characterize sedimentology and top diagenetic picks of the carbonates. It is concluded from four reservoir zones in Matoa’s static model, that present-day analogues and distinct interplay among lithology, depositional and diagenetic systems are key aspects to revise the development strategies. The most significant interdependent factors can be quantitatively measured and modelled, such as the dual porosity system which was synthesized by Phi separate vug from the RFN Lucia porosity model.
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 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 | 0.000 |
| 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) | 0.000 | 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".