Improved modeling of channel prediction based on gray relational analysis and a support vector machine: a case study on the X pilot area in the Daqing oilfield in China
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Considering the complex reservoir conditions and rapid changes in lithological facies, it is difficult to predict the channel distributions in the Heidimiao oil layer in the X pilot area of the Daqing oilfield. To address this problem, a model for fluvial reservoir prediction under complex geological conditions is established by combining gray relational analysis (GRA) and a support vector machine (SVM). Attribute selection is firstly processed based on 2D forward modeling. A predictive model of the main channel combining GRA and SVM methods is then built using the selected attributes as inputs. The predictive pay thickness is our proposed model is well validated with the realistic pay thickness data interpreted from 18 wells, and all the relative errors are within 10%. Channel predictions from our proposed models also confirmed the accuracy based on historical oil production.
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.
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 | 0.001 | 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) | 0.000 | 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 it