Integrated study of Vatyogan oil field, West-Siberian region of Russia
Bibliographic record
Abstract
Abstract The western Siberian basin contains some of the largest oil fields in Russia and the former Soviet Union. In early 2000, LUKOil, in cooperation with PetroAlliance, completed the largest integrated reservoir study ever undertaken in the region. The project spanning nearly four years culminated in an enhanced 3D geological and reservoir models of the giant Vatyogan oil field, which holds over 1 billion tons of calculated oil reserves. The major technical objective was to utilize state-of-the-art technology to create a new 3D geological model of Vatyogan’s six major producing zones, which range in age from Upper Jurassic to Upper Cretaceous and cover a wide range of depositional environments. The new geologic model would form the basis for reservoir simulation models which would guide further operational and development activities in the field. Specific tasks to be accomplished with the new model include: In undeveloped areas: Estimate reserves and prepare a drilling programIn developed areas: Identify areas of by-passed oil and develop an optimum infill drilling and enhanced recovery program to improve production. A multi-disciplinary team of 25 specialists was involved in various stages of the project. The study began with the creation of a digital database of 3,800 wells from paper records. Newly acquired data such as: 2D and 3D seismic, log data, and high precision gyroscopic surveys were incorporated into the revised models. This paper describes the processes employed in the project and highlights some of the stumbling blocks. Details are provided describing data management, geological modeling, upscaling and history matching of thousand of wells. Conclusions illustrate how data from the specialized logs and high precision gyroscopic surveys resolved numerous questions and contradictions in the existing data, providing improved reservoir structure analysis and more accurate fluid-contact depths--data which significantly impacted both the models and the simulation forecasts.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".