An ensemble-based strategy for robust predictive volcanic rock typing efficiency on a global-scale: A novel workflow driven by big data analytics.

Laboratory measurements, paleontological data, and well-logs are often used to conduct mineralogical and chemical analyses to classify rock samples. Employing digital intelligence techniques may enhance the accuracy of classification predictions while simultaneously speeding up the whole classification process. We aim to develop a comprehensive approach for categorizing igneous rock types based on their global geochemical characteristics.

5G-enabled, battery-less smart skins for self-monitoring megastructures and digital twin applications.

With the current development of the 5G infrastructure, there presents a unique opportunity for the deployment of battery-less mmWave reflect-array-based sensors. These fully-passive devices benefit from having a larger detectability than alternative battery-less solutions to create self-monitoring megastructures. The presented 'smart' skin sensor uses a Van-Atta array design enabling ubiquitous local strain monitoring for the structural health monitoring of composite materials featuring wide interrogation angles.

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.

Early-Middle Holocene high lake levels of Rinqen Shubtso on the southern Tibetan Plateau and the formation mechanisms.

The lake-level highstands on the southern Tibetan Plateau (TP) during the Early-Middle Holocene have traditionally been attributed to increased monsoonal precipitation. However, there has been limited discussion and evaluation regarding how the elevated shoreline indicates the formation of mega-paleolakes and the effects of glacial meltwater on rising lake levels. In this study, we conducted an investigation into the well-preserved paleoshorelines of Rinqen Shubtso, a closed-basin lake system located on the southern TP.

Improving predictions of shale wettability using advanced machine learning techniques and nature-inspired methods: Implications for carbon capture utilization and storage.

The utilization of carbon capture utilization and storage (CCUS) in unconventional formations is a promising way for improving hydrocarbon production and combating climate change. Shale wettability plays a crucial factor for successful CCUS projects. In this study, multiple machine learning (ML) techniques, including multilayer perceptron (MLP) and radial basis function neural networks (RBFNN), were used to evaluate shale wettability based on five key features, including formation pressure, temperature, salinity, total organic carbon (TOC), and theta zero.

Hydrocarbon Potential Assessment of Carbonate-Bearing Sediments in a Meyal Oil Field, Pakistan: Insights from Logging Data Using Machine Learning and Quanti Elan Modeling.

The Meyal oil field (MOF) is among the most important contributors to Pakistan's oil and gas industry. Northern Pakistan's Potwar Basin is located in the foreland and thrust bands of the Himalayan mountains. The current research aims to delineate the hydrocarbon potential, reservoir zone evaluation, and lithofacies identification through the utilization of seven conventional well logs (M-01, M-08, M-10, M-12, M-13P, and M-17).

An Emerged Challenge of Air Pollution and Ever-Increasing Particulate Matter in Pakistan; A Critical Review.

This study, for the first time, aims to evaluate the situation of air quality in Pakistan critically; through a detailed assessment of sources, policies, and key challenges to identify the plausible way forward. Air pollution and particulate matter have merged as a global challenge in recent years because of its growing health and socio-economic risks. The intensity and impacts of these risks have become more pronounced, especially in developing countries like Pakistan that lack adequate warning, protection, and management systems.