Characterization of carbonate microfacies and reservoir pore types based on Formation MicroImager logging: A case study from the Ordovician in the Tahe Oilfield, Tarim Basin, China
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Bibliographic record
Abstract
Improving the characterization of deep carbonate reservoirs requires developing a clear understanding of nature and distribution of their constituent microfacies and associated pore types. These aspects have been little studied in the middle-lower Ordovician of the Tahe Oilfield in the Tarim Basin in large part due to the limited available cores and relatively poor seismic data. Formation MicroImage (FMI) logging provides a bridge to connect the core data and seismic data to facilitate study of the distribution of microfacies and pore types. Based on analysis of the FMI logs from eight wells (each calibrated by comparing with core samples, thin sections, and conventional well logs), five FMI-based microfacies have been established: (1) intershoal sea microfacies (ISMF), (2) low-energy shoal microfacies (LSMF), (3) high-energy shoal microfacies (HSMF), (4) lagoon microfacies (LMF), and (5) tidal flat microfacies (TFMF). Using the FMI-based identification of microfacies, it has been proposed that the Yingshan Formation was deposited in restricted-platform (characterized by LSMF, HSMF, LMF, and TFMF) and open-platform (characterized by ISMF, LSMF, and HSMF) environments. Three pore types, with the potential for reservoir quality porosity, have been identified in FMI logs: pores (laminated and isolated pores), vugs, and fractures (dipping shear and conjugate fractures). Statistical analyses found that, in comparison with other microfacies, HSMF is more favorable for the development of the above three reservoir pore types, vugs being the most abundant. This study shows the effectiveness of FMI images in the identification of carbonate microfacies and reservoir pore types, and in the building of high-resolution 3D geologic models that identify high-quality reservoir zones.
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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 it