Reservoir rock typing assessment in a coal-tight sand based heterogeneous geological formation through advanced AI methods.

Geoscientists now identify coal layers using conventional well logs. Coal layer identification is the main technical difficulty in coalbed methane exploration and development. This research uses advanced quantile-quantile plot, self-organizing maps (SOM), k-means clustering, t-distributed stochastic neighbor embedding (t-SNE) and qualitative log curve assessment through three wells (X4, X5, X6) in complex geological formation to distinguish coal from tight sand and shale. Also, we identify the reservoir rock typing (RRT), gas-bearing and non-gas bearing potential zones. Results showed gamma-ray and resistivity logs are not reliable tools for coal identification. Further, coal layers highlighted high acoustic (AC) and neutron porosity (CNL), low density (DEN), low photoelectric, and low porosity values as compared to tight sand and shale. While, tight sand highlighted 5-10% porosity values. The SOM and clustering assessment provided the evidence of good-quality RRT for tight sand facies, whereas other clusters related to shale and coal showed poor-quality RRT. A t-SNE algorithm accurately distinguished coal and was used to make CNL and DEN plot that showed the presence of low-rank bituminous coal rank in study area. The presented strategy through conventional logs shall provide help to comprehend coal-tight sand lithofacies units for future mining.

Using multiple normalization procedures and contamination indices in the assessment of trace metal(loid) contents of the surface sediments of Lake Xingyun, southwestern China.

Trace metal(loid) (TM) contamination, especially of aquatic ecosystems, is a global ongoing environmental problem. Fully and accurately determining their anthropogenic sources is a key requirement for formulating remediation and management strategies. Herein, we developed a multiple normalization procedure, combined with principal component analysis (PCA) to assess the influence of data-treatment and environmental factors on the traceability of TMs in surface sediments of Lake Xingyun, China. Multiple contamination indices, i.e., Enrichment factor (EF), Pollution Load Index (PLI), Pollution Contribution Rate (PCR) and Exceeded multiple discharge standard limits (BSTEL) suggest that contamination is dominated by Pb with the average EF exceed 3, especially within the estuary aeras with the PCR >40 %. The analysis demonstrates that the mathematical normalization of data, which adjusts it for various geochemical influences, has a significant effect on analysis outputs and interpretation. Routine (Log) and extreme (outlier removing) transformations may mask and skew important information contained within the original (raw) data, which create biased or meaningless principal components. Granulometric and geochemical normalization procedures can obviously identify the influence of grain size and other environmental impact on TM contents in principal components, but incorrectly explains the potential sources and contamination on different sites. Reducing the influence of organic matter by normalization allowed the mineralogy, bio-degradation, salinity, and anthropogenic sources associated with local sewage and anthropogenic smelting to be identified and interpreted more clearly. Moreover, the co-occurrence network analysis also confirms that the influence of grain size, salinity, and organic matter content are the primary factors controlling the spatial variability in the type and concentrations of TMs.

Contamination and eco-risk assessment of toxic trace elements in lakebed surface sediments of Lake Yangzong, southwestern China.

Toxic trace elements represent an ongoing environmental problem in aquatic ecosystems. However, a lack of quantitative analysis and accurate evaluation has led to unguided control and water management strategies. Lake Yangzong is the main freshwater resource for nearly one million people in Yunnan Province in southwestern China. It has been heavily contaminated in recent years by significant anthropogenic activities including an industrial phosphor-gypsum spill, sewage effluent, and chemical remediation processes. Herein, we combine eco-environmental indices with multiple statistical analyses to determine the ecological risk and degree of contamination of 11 toxic trace elements in the upper sediments of the lakebed. Local geochemical background concentrations were determined using robust regression models developed from sediment core data. Pollution indices (EF/PLI) indicate that severe As contamination was centralized in the southwestern part of the lake. Other toxic trace elements (e.g., Cd, Cu, Pb) are slightly to moderately enriched, and progressively decrease from the northwestern to the southeastern areas of the lake. A more accurate and sensitive index (PCR) was proposed herein, suggesting that contamination was dominated by As and Pb in different lake sections. The northern section of the lake and the southwestern bay exhibited higher contaminant levels than other regions of the lake. Bio-toxic indices (ERF/PERI) indicate that As and Cd pose a high ecological risk, whereas Cu and Pb pose a low risk to biota. Statistical analyses (PCA/PMF) demonstrate that metal contaminants originated from three types of anthropogenic sources: the smelting of metal ores, the leakage of tailings effluent, and coal consumption.

The Bioaccumulation and Health Risk Assessment of Metals among Two Most Consumed Species of Angling Fish (Cyprinus carpio and Pseudohemiculter dispar) in Liuzhou (China): Winter Should Be Treated as a Suitable Season for Fish Angling.

Wild fish caught by anglers were validated to be commonly polluted by metals, but their contamination status could be varied with changing seasons. To determine the seasonal variation in metal pollution and health risks in these fish, this study took Liuzhou City as an example to investigate the concentrations of eight metals in two dominant angling fishes (Cyprinus carpio and Pseudohemiculter dispar) collected, respectively, in winter and summer. The obtained results suggested the mean concentrations of metals in fish are overall lower in winter. Only Cr, Zn, and Cd in some fish were beyond the thresholds in summer. The significant correlations between fish length and weight and most metals suggested the biological dilution effect could exert its influence in winter. The similar distribution of metals in winter suggested that metal bioaccumulation should be manipulated by living habitats, while the inconsistent distribution of metals in summer may be related to the variation in feeding behavior. The metal pollution index (Pi) values were all below 0.2 in winter, which suggested no metal contamination in fish, but most fish were found to be mostly contaminated by Cr and Cd in summer, which was confirmed by their Pi > 0.2. The fish could be consumed freely in winter due to the total target hazard quotient (TTHQ) below 1, while the consumption of fish was not entirely safe in summer, particularly for children, due to TTHQ values that were generally beyond 1. Given the higher weekly recommended consumption of fish in winter, winter should be treated as a suitable season for fish angling.

Distribution and health-ecological risk assessment of heavy metals: an endemic disease case study in southwestern China.

This research focuses on the health risks caused by heavy metal (HM) environmental pollution. Soil, water, corn, rice, and patients' hair samples from Daping Village, Yunnan Province, China, were analyzed for seven selected HMs. Geoaccumulation index (Igeo), pollution indexes (PI), and the Nemerow integrated pollution index (PN) were used to evaluate pollution levels. We employed principal component analysis (PCA), correlation analysis (CA), and spatial distribution to identify the source and distribution characteristics of HMs in soil. Health risks of HMs and exposure pathways were accessed by calculating the hazard quotient (HQ) and hazard index (HI). The Igeo, PI, and PN results show that cadmium (Cd) and arsenic (As) pollution is severe in soil, while other pollution is relatively little. PCA, CA, and spatial distribution show that HMs may be derived from black shale weathering and enrichment. Residents' drinking water is relatively safe. Arsenic is the element most threatening to local residents (HI = 3.8). Soil (HI = 3.55) ingestion and plant (HI = 1.67) ingestion are the primary exposure pathways to HMs. This unusual disease may be caused by children's relatively low immunity and long-term exposure to As. We must enhance the protection of children and encourage avoiding soil contact as much as possible. Our results highlight the importance of investigating HM pollution from geological sources and blocking potential exposure pathways.