Pore Structure and Fractal Characteristics of Mixed Siliciclastic-Carbonate Rocks from the Yingxi Area, Southwest Qaidam Basin, China
Bibliographic record
Abstract
Summary Evaluating reservoir properties at the pore scale is vital to better estimate hydrocarbon reserves and plan field development. The lacustrine mixed siliciclastic-carbonate deposits of the Upper Paleogene Xiaganchaigou Formation in the west Yingxiongling area form one of the most important hydrocarbon reservoirs in the southwestern Qaidam Basin (China). In this study, we analyzed well samples with X-ray diffraction (XRD), nuclear magnetic resonance (NMR), and mercury injection capillary pressure (MICP) data in integration with scanning electron microscopy (SEM) images to decipher the mineral composition and pore structure characteristics of the Xiaganchaigou Formation. We also calculate the fractal dimensions using MICP, NMR T2 spectrum, and SEM images based on fractal theory models. The results indicate that the mixed siliciclastic-carbonate samples of the upper section of the Xiaganchaigou Formation are mainly formed by dolomite and clay minerals with low siliceous and calcite content. Porosity is relatively low (2.01−9.83%) and positively correlated with dolomite content, thus indicating that the dolomite intercrystalline pores formed by infiltration and reflux dolomitization control the reservoir characteristics. The size of dolomite intercrystalline pores varies between several and hundreds of nanometers. The porosity has a poor correlation with permeability, which indicates that the pores are mostly primary, which lack the transformation of late dissolution. Three types of mixed siliciclastic-carbonate reservoirs are identified according to pore size distribution (<20 nm, 20−300 nm and multiple distribution), calculated using the NMR T2 spectrum. Fractal curves calculated by combining the MICP and NMR data are characterized by multisegments. The number of segments depends on the degree of heterogeneity of pore structure: two segment for high heterogeneity and three segment for low heterogeneity, also indicating a multifractal feature in mixed rock reservoirs. There is a negative correlation trend between porosity and fractal dimensions, and larger pores often have larger fractal dimensions. These results show that MICP-based fractal values are higher than those of NMR-based, which result from unconnected pores that the MICP is unable to reach. Fractal dimensions obtained from SEM have a small and narrow distribution range and are negatively correlated with the number of pores with smaller sizes. In essence, this study shows that the fractal dimension can be a concise index to evaluate the heterogeneity of lacustrine mixed siliciclastic-carbonate reservoirs, which can serve as an important reference for hydrocarbon development plans.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".