A new rock physics model for fractured oil shale reservoirs in Mahu oil field
Bibliographic record
Abstract
Abstract The exploration of unconventional reservoirs such as oil shale has become a focus of research in the oil/gas industry, but due to the diversity of lithology and structural complexity of shale reservoirs, the study of their rock physics laws is a challenge. By analyzing the physical, lithologic, and fluid characteristics of oil shale reservoirs in the Mahu area of Xinjiang, China, we adopted a variety of effective-medium theories to carry out rock physics modeling. We analyzed the differences in the calculation results of various theoretical models, and finally constructed a set of rock physics modeling processes suitable for oil shale reservoirs. The analysis shows that the calculation results of Voigt-Reuss-Hill (VRH) and Hashin-Shtrikman (HS) average for mixed mineral matrix are very similar, but there are certain differences between the upper and lower bounds calculated by them. The upper and lower bounds of HS average are closer than VRH average. We used the Kuster-Toksoz effective-medium, differential effective-medium (DEM), and self-consistent approximation models to construct the rock skeleton and analyzed the differences of the models. The results of the three models are almost identical, but their assumptions and limitations differ. We used DEM in the final shale model, considering the large porosity and the order of inclusion filling. According to the fracture distribution characteristics of oil shale reservoirs, we used an inclined fracture model to describe the fractures with different structural characteristics. Finally, the established shale rock physics model was used to calculate the actual logging data, and the results of elastic parameters are consistent with the measured data. Through the inversion of the fracture parameters, the inversion results are consistent with the measured results as a whole, indicating that the model has certain applicability to the simulation of oil shale reservoirs.
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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 | 0.000 | 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 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".