Partial Least Squares Regression of Oil Sands Processing Variables within Discrete Event Simulation Digital Twin

Tools, Technologies and Frameworks for Digital Twins in the Oil and Gas Industry: An In-Depth Analysis

The digital twin (DT), which involves creating a virtual replica of a physical asset or system, has emerged as a transformative set of tools across various industries. In the oil and gas (O&G) industry, the development of DTs represents a significant evolution in how companies manage complex operations, enhance safety, and optimize decision-making processes. Despite these significant advancements, the underlying tools, technologies, and frameworks for developing DTs in O&G applications remain non-standardized and unfamiliar to many O&G practitioners, highlighting the need for a systematic literature review (SLR) on the topic. Thus, this paper offers an SLR of the existing literature on DT development for O&G from 2018 onwards, utilizing Scopus and Web of Science Core Collection. We provide a comprehensive overview of this field, demonstrate how it is evolving, and highlight standard practices and research opportunities in the area. We perform broad classifications of the 98 studies, categorizing the DTs by their development methodologies, implementation objectives, data acquisition, asset digital development, data integration and preprocessing, data analysis and modeling, evaluation and validation, and deployment tools. We also include a bibliometric analysis of the selected papers, highlighting trends and key contributors. Given the increasing number of new DT developments in O&G and the many new technologies available, we hope to provide guidance on the topic and promote knowledge production and growth concerning the development of DTs for O&G.

Recycled Excavation Soils as Sustainable Supplementary Cementitious Materials: Kaolinite Content and Performance Implications

In response to the environmental implications of the massive quantities of excavation soil generated by global urbanization and infrastructure development, recent research efforts have explored the repurposing of calcined excavation soils as sustainable supplementary cementitious materials (SCMs). As it is still at an early stage, current research lacks systematic analysis across diverse soil deposits regarding their reactivity and mechanical properties within cementitious binders, despite recognized geographical variability in kaolinite content. Through comprehensive experimentation with soils sourced from four major southern Chinese cities, this study presents a pioneering assessment of the compressive strength, pozzolanic reactivity (X-ray diffraction, Fourier-transform infrared spectroscopy, solid-state nuclear magnetic resonance), and microstructural development (mercury intrusion porosimetry, scanning electron microscopy) of mortars modified by various calcined excavation soils (up to 28 days curing). The experimental data suggest that soils with a kaolinite content above 53.39% produce mortars of equal or superior quality to plain cement mixes, primarily due to their refined pore structures, microstructural densification, and enhanced hydration reactions. The findings highlight kaolinite-specifically, aluminum content-as the principal indicator of excavation soil viability for SCM application, suggesting a promising avenue for sustainable construction practices.

Segmentation, Detection, and Tracking of Stem Cell Image by Digital Twins and Lightweight Deep Learning

The current work aims to strengthen the research of segmentation, detection, and tracking methods of stem cell image in the fields of regenerative medicine and tissue damage restoration. Firstly, based on the relevant theories of stem cell image segmentation, digital twins (DTs), and lightweight deep learning, a new phase contrast microscope is introduced through the research of optical microscope. Secondly, the results of DTs method and phase contrast imaging principle are compared in stem cell image segmentation and detection. Finally, a lightweight deep learning model is introduced in the segmentation and tracking of stem cell image to observe the gray value and mean value before and after stem cell image movement and stem cell division. The results show that phase contrast microscope can increase the phase contrast and amplitude difference of stem cell image and solve the problem of stem cell image segmentation to a certain extent. The detection results of DTs method are compared with phase contrast imaging principle. It indicates that not only can DTs method make the image contour more accurate and clearer, but also its accuracy, recall, and F1 score are 0.038, 0.024, and 0.043 higher than those of the phase contrast imaging method. The lightweight deep learning model is applied to the segmentation and tracking of stem cell image. It is found that the gray value and mean value of stem cell image before and after movement and stem cell division do not change significantly. Hence, the application of DTs and lightweight deep learning methods in the segmentation, detection, and tracking of stem cell image has great reference significance for the development of biology and medicine.

Discrete Event Simulation for Machine-Learning Enabled Mine Production Control with Application to Gold Processing

Interdisciplinary barriers separating data scientists and geometallurgists have complicated systematic attempts to incorporate machine-learning into mine production management; however, experiences in excavating a vein-hosted gold deposit within the Alhué region of Chile have led to methodological advances, which is the subject of the current paper. These deposits are subject to several challenges, from increasing orebody complexity and decreasing gold grades to the significant geological uncertainty that is intrinsic to these systems. These challenges then translate to mineral processing, which is already dealing with increased environmental and technological constraints. Geological uncertainty causes stockout risks that can be mitigated by the approach that is developed within this paper, which features alternate operational modes and related control strategies. A digital twin framework based on discrete event simulation (DES) and a customized machine-learning (ML) model is proposed to incorporate geological variation into decision-making processes, including the setting of trigger point that induces mode changes. Sample calculations that were based on a simulated processing plant that was subject to mineralogical feed changes demonstrated that the framework is a valuable tool to evaluate and mitigate the potential risks to gold mineral processing performance.

Introduction to the Special Issue “Advances in Computational Intelligence Applications in the Mining Industry”

This is an exciting time for the mining industry, as it is on the cusp of a change in efficiency as it gets better at leveraging data [...]

Digital twin-driven smart supply chain

Today’s supply chain is becoming complex and fragile. Hence, supply chain managers need to create and unlock the value of the smart supply chain. A smart supply chain requires connectivity, visibility, and agility, and it needs be integrated and intelligent. The digital twin (DT) concept satisfies these requirements. Therefore, we propose creating a DT-driven supply chain (DTSC) as an innovative and integrated solution for the smart supply chain. We provide background information to explain the DT concept and to demonstrate the method for building a DTSC by using the DT concept. We discuss three research opportunities in building a DTSC, including supply chain modeling, real-time supply chain optimization, and data usage in supply chain collaboration. Finally, we highlight a motivating case from JD.COM, China’s largest retailer by revenue, in applying the DTSC platform to address supply chain network reconfiguration challenges during the COVID-19 pandemic.