Integration of Formation Pressure Data to Improve Reservoir Characterization and Reservoir Management in PL19-3 Oil Field, Bohai Bay
Bibliographic record
Abstract
Abstract The Peng Lai (PL) 19-3 Oil Field, located in the ConocoPhillips operated Bozhong 11/05 Block in the central southern Bohai Sea, offshore China, is currently the largest offshore oil field in China. The trap is a complex wrench anticline developed along the Tanchen-Lujiang fault system. The main oil accumulation is in the Neogene Lower Minghuazhen and Guantao Formations with a vertical relief from the top reservoir to the deepest oil bearing rock of approximately 500 meters. The PL 19-3 Oil Field, deposited in a fluvial environment, is a complex stacking of unconsolidated sandstone reservoirs, with moderate porosity and permeability and low net gross ratio. The trap has been divided recently into numerous fault blocks which have unique contacts and variable oil properties both vertically and laterally, with oil gravities ranging from 12 to 22 API. This paper reviews the pressure acquisition history and analysis from the 160 well formation test database, which includes both wireline formation test (WFT) and formation test while drilling (FTWD). Formation testing in the Neogene formation of Bohai Bay is challenging since the reservoir is unconsolidated and the oil is heavy. Common problems that affect pressure testing are described, efforts to enhance test efficiency are stated and key learnings and best practices to secure high quality pressure data are summarized. Conventional pressure interpretation to derive fluid gradient and oil water contacts, identify reservoir compartmentalization and flow barrier is challenged due to small density contrast between the heavy oil and water in the field. The excess pressure method, which is attributed to formation water properties and consistent hydrostatic pressure gradient in the field, has been an effective way to analyze pressure data. In this paper, the historical application of excess pressure in the industry is reviewed, examples of the excess pressure interpretation in PL 19-3 Oil Field are given and integrated interpretation practices are emphasized. Pressure data have wide application in the PL 19-3 oil field. This paper summarizes and demonstrates how original excess pressure and dynamic logging while drilling (LWD) pressure data have been used successfully to predict oil water contacts, to analyze fault transmissibility, to monitor water flooding efficiency, to identify fluid properties, to interpret fault cuts in wells, to optimize mud weights while drilling and to mitigate risk and well bore damage during completion operations
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".