Classification and Evaluation of Microscopic Pore Structure in Carbonate Rocks by Integrating MICP-Based Dynamic Information
Bibliographic record
Abstract
Abstract In carbonate reservoirs, the establishment of a coherent correlation between petrophysical static rock type (PSRT) and petrophysical dynamic rock type (PDRT) schemes poses a formidable challenge due to its petrophysical complexity. Additionally, the interpretation of the dynamic properties of microscopic pore structure (MPS) based on mercury injection capillary pressure (MICP) data has been an issue. The objective of this study is to alleviate the divergence in flow properties in MPS classification and evaluate the oil recovery potential of different MPS quantitatively based on MICP. A total of 76 core plugs without fractures were studied from the Middle East region. The data set available included helium porosity, gas permeability and high-pressure mercury injection. MPSs were qualitatively classified according to the morphological characteristics of the MICP data correlated oil recovery potential. Unsteady-state oil-water relative permeability tests were subsequently conducted to ensure the effectiveness of the classification. Sensitivity parameters were correlated with the efficiency of mercury withdrawal and condensed with the factor analysis (FA) method. After dimensionality reduction, interpretable general factors were obtained to quantitatively characterize the oil recovery potential of MPS and to establish a core quality evaluation model from a dynamic view. Results showed that the proposed classification can maintain the consistency of dynamic attributes in five qualitative categories and significant differences were observed among the different MPSs. A total of five sensitivity parameters were screened to quantitatively characterize the oil recovery potential of MPS. Moreover, FA defines three aspects that affect the ability to oil recovery: sweep, displacement, and storage. The relative relationship between the MPS and oil recovery potential predicted by the evaluation model and the laboratory-measured oil recovery are in general agreement, and this relative relationship can evaluate the oil recovery potential based on the MPS without the laboratory-measured oil recovery. This work presents a qualitative classification method for reducing the discrepancy between PSRT and PDRT. The proposed quantitative evaluation model provides new insights into the effects of MPS on fluid flow. Both of them can improve the screening of representative samples for special core analysis and accurate numerical simulation of carbonate 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".