Geological, Geophysical and Petrophysical Characterisation of Hydrocarbon Reservoirs in South China Sea Deep Water Block 29/26
Bibliographic record
Abstract
Abstract In 2004, Block 29/26 Production Sharing Contract (PSC) was signed between Husky Energy Inc. and China National Offshore Oil Corporation (CNOOC). The block is located about 300 km offshore Hong Kong (Figure 1), on the north east slope of the gas-prone Baiyun (White Cloud) Sub Sag of the Zhu-II sub-basin in the larger Pearl River Mouth Basin (PRMB). The water depth on the block is from 650 m to 1500 m. A total of 27 deep water exploration and appraisal wells were drilled in the period between 2006 and 2011. Three commercial gas fields, Liwan 3-1-1, LH34-2 and LH29-1, were discovered in April 2006, September 2009 and December 2009, respectively. Several sub-economic gas accumulations were also discovered in the same period. Reservoir and hydrocarbon detection and trap delineation, were the key challenges faced by the exploration team. An integrated subsurface approach, including extensive 3D seismic surveying and high quality processing, seismic inversion, accurate structural/stratigraphic mapping, accurate depth conversion and seismic-based geomorphology, played a key role in making these discoveries. At the time of writing this paper, Block 29/26 is the only deep water block in South China Sea (SCS) with commercial discoveries, and boasts the most extensive deep water database in the region, including large amounts of seismic, conventional and side-wall cores, wireline logs, pressure and fluid samples as well as subsequent lab analyses data. Full investigation of the available information can be expected to provide the necessary guidance for future exploration and development activities in this region. There are several types of reservoirs encountered in Block 29/26. Each type of reservoir has distinctively different log characteristics and seismic responses. This paper provides a systematic review on the applicability of AVA technology to the seismic data on the block and focuses on the challenges that need to be addressed in the future.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".