Comprehensive Evaluation Method of the Tight Oil Reservoir Quality in the Ordos Basin
Bibliographic record
Abstract
The Wuqi area of the Ordos Basin boasts significant resource potential in the Chang 8 member of the Yanchang Formation. However, under the dual control of lithology and physical properties, reservoirs are generally dense and heterogeneous, and the quality of the oil layer changes rapidly, which brings difficulties to the optimization of favorable areas. To evaluate the reservoir quality more accurately, based on core observations, and logging and dynamic data analysis, combined with casting thin sections, scanning electron microscopy, high‐pressure mercury injection, nuclear magnetic resonance, and other related experiments, different reservoir types and characteristics were analyzed, and a comprehensive method for evaluating the reservoir quality was established. There are three types of sand body structures in the shallow‐water delta of Chang 8 in the study area, including the continuous superposition type, interval superposition type, and lateral single‐layer type. They mainly experienced diagenesis, such as compaction, cementation, and dissolution. Among these, porosity loss in the Chang 8 reservoir due to compaction and cementation reached 79.3%, consistent with trends observed in other continental tight oil plays such as the Songliao and Junggar Basins, while the improvement in physical properties due to dissolution was minimal. The main parameters influencing different reservoir types are optimized, and the comprehensive classification with the multivariate coefficient is constructed after providing coefficients with different weights. Four reservoir types are quantitatively delineated, among which the physical properties of Type I reservoirs are the best and the physical properties of Type IV reservoirs are the worst. Combined with the difference in the characteristics of sensitive logging curve responses, four logging parameters, density, neutron, resistivity, and acoustic time difference, are optimized, and different reservoir types are quantitatively identified by Fisher discriminant analysis. Comprehensively considering the change in the vertical sand body structure and reservoir type, the three key parameters of interlayer density, interlayer frequency, and reservoir thickness are selected, and the comprehensive evaluation index N of reservoir quality is innovatively constructed. The proposed evaluation index effectively decouples lithological and petrophysical variations, refining reservoir quality assessments for enhanced exploration and production strategies. The greater the N value is, the better the quality of the oil layer. The smaller the N value is, the thinner the oil layer, the more developed the interlayer, and the worse the oil layer quality. The N index exhibits a strong correlation with production characteristics, indicating that the method has effectively evaluated reservoir quality and provided a theoretical basis for targeting favorable areas.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".