Research on Reservoir Prediction With High Precision Based on the Control of Logging and Seism
Bibliographic record
Abstract
With the continuous development of detailed reservoir description technology, the research of multiple disciplines reservoir based on the geological cognition has gradually carried out in Daqing oilfields. In this paper, the research mainly studied Putaohua oil layer of Xingshi district, and the study about the method of improving the reservoir prediction accuracy was carried out after finishing the basic data, geological statistics formation, fine seismic horizon interpretation and fault interpretation which based on the combination of logging and seism. Optimize the properties for predicting the structure through seismic attribute and post-stack density pseudo wave impedance inversion to realize quantitative characterization of sandstone and mudstone threshold value by seismic attributes, and coincidence rate showed that, the prediction accuracy of interwell channel sand body reached 85%, and results of block application showed that narrow channel sand had better continuity after the combination of logging and seism, boundary and swing position of river changed large, and interwell connectivity is more accurate, which provides a more reliable basis for the further development of the residual oil in channel sand bodies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".