Seismic and Well Log Reprocessing, Re-interpretation and Geostatistical Inversion Yields More Detailed View of Yuzhno Khilchuyu Field
Bibliographic record
Abstract
Abstract LUKoil and ConocoPhillips formed the joint venture company NaryanMarNefteGaz (NMNG) to develop jointly owned licenses in the Timan-Pechora Basin. The Yuzhno Khilchuyu license lies in this province and, is expected to be one of the largest and most prolific fields in the region. Development of the Yuzhno Khilchuyu Field requires a huge initial investment in infrastructure, drilling and transportation. Successfully achieving acceptable reserves and production levels from the field will be critical to offset these investments. To meet this challenge a more detailed understanding of the reservoir is needed to optimize well placement. In 2004, a large multi-disciplinary subsurface project team was formed with members from LUKoil, ConocoPhillips and Fugro-Jason to develop updated high-resolution geologic and reservoir simulation models. The seismic and well log data were completely reprocessed, resulting in a significant improvement in the overall data quality. All log, core, and production test data were incorporated into a new, fully integrated, interpretation. A sophisticated Markov Chain Monte Carlo (MCMC) geostatistical inversion methodology was applied, and the resulting high-resolution geologic model yields a dramatic increase in reservoir detail. The new model enabled the team to define the aerial extent of different reservoirs and the distribution of internal barriers. It also provided insight into porosity and permeability distribution within each reservoir, enabling better decisions on the location of production and water injection wells. Development drilling is in progress.
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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 | 0.000 |
| 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 | 0.001 | 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".