Study on the Couple of 3D Geological Model and Reservoir Numerical Simulation Results
Bibliographic record
Abstract
Taking Dongying Formation, Palaeogene, B Oilfield in Bohai Bay Basin as an example, this paper discusses research on coupling 3D geological model and reservoir numerical simulation results during oilfield development. 3D geological modeling technology and reservoir numerical simulation plays important roles in oilfield development nowadays. 3D geological modeling integrates the static information and data from cores, logs and seismic to approach the reality of reservoir as much as possible. Numerical simulation based on geological models, provides a way to use dynamic data by history matching production. Thus, static data from the subsurface reservoir and dynamic data from production are synthesized with the combination of 3D geological modeling and numerical simulation. At present, except upscaling, which connected these two steps, modeling and simulation are usually discussed and operated separately. This paper tried to find an approach to realize the couple of 3D geological modeling and reservoir numerical simulation, which admits the uncertainty of the geological model and emphases the use of simulation result to adjust geological model. 3D geological modeling provides reservoir numerical simulation with initial reservoir static parameter. With the initial geological knowledge, history matching is conducted to quantitatively describe the flowing rule of oil-water. During the process of matching production history, the changes of reservoir parameters may put insight on corresponding geological knowledge. Based on these updated geological knowledge, these possible changes are coupled to the new geological model. 3D geological model of B oilfield was studied as an example in this paper，how to sufficiently integrate numerical simulation results was researched to improve geological knowledge on the connectivity relationship between well groups, then the 3D geological model was updated.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".