A Method of Quantitative Evaluation of Diagenetic Reservoir Facies of Tight Gas Reservoirs With Logging Multi-Parameters: A Case Study in Sulige Area, Northern Ordos Basin, China
Bibliographic record
Abstract
Reservoir and flow characteristics of low, ultra-low permeability tight sandstone reservoir were largely controlled by diagenesis in reservoir assessment. In previous studies, diagenesis were researched only by using core analysis data, and it was difficult that diagenetic reservoir facies of the interval and the well without core analysis data were evaluated. Therefore, it was easy and quick that diagenetic reservoir facies were characterized with logging response characteristics which were extracted effectively. Taking tight gas reservoirs for example in Sulige area, northern Ordos Basin, China, logging response characteristics of different classification were analyzed by multiple samples with core analysis data, and the quantitative evaluation index of diagenetic reservoir facies based on logging multi-parameter was set up. A method of quantitative evaluation of diagenetic reservoir facies of tight gas reservoirs with logging multi-parameters was formed in the method of integration of analysis technology of Grey theory, and the accuracy and availability of the method were evaluated. The results shown that non-digitalized problems of diagenetic reservoir facies evaluation was solved by the digitalization method of logging multi-parameters, and the rate of accuracy, of returned classification using methods of mutual test, reached to 91.2%. The results provided a new and effective evaluation approach of low, ultra-low permeability tight sandstone reservoir.
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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 | 0.002 | 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 | 0.000 | 0.001 |
| 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 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".