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Carbon Capture and Low-Carbon Energy System: Development, Modeling and AI Empowerment

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "B3: Carbon Emission and Utilization".

Deadline for manuscript submissions: 20 December 2026 | Viewed by 279

Editors


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Guest Editor
Institute of Ocean Engineering, Northeastern University, Qinhuangdao 066004, China
Interests: CCUS; deep-sea resource development; low-carbon energy system modeling; multi-field coupling
Special Issues, Collections and Topics in MDPI journals
Institute of Ocean Engineering, Northeastern University, Qinhuangdao 066004, China
Interests: CO2 geological storage and utilization
School of Petroleum Engineering, Yangtze University, Wuhan 430100, China
Interests: CCUS-EOR modeling and optimization; AI for CCUS; machine learning for energy system optimization

Special Issue Information

Dear Colleagues,

Lately, carbon capture technology and low-carbon energy systems have witnessed rapid development, under the backdrop of the global carbon neutrality strategy and the green energy transition, becoming the core direction of the energy green transformation. Efficient adsorption, absorption, and catalysis in carbon capture processes. Multi field coupling, flow processes, and physicochemical changes during carbon sequestration. The construction and optimization of low-carbon and sustainable energy systems. The research and development of these core technologies, numerical modeling of mechanisms, and comprehensive system optimization can effectively improve the operational efficiency of carbon capture, utilization, and storage processes, significantly reducing system construction and operation costs. Furthermore, we will continue to promote the industrialization and large-scale application of various low-carbon clean energy sources, comprehensively assist the traditional energy industry in green and low-carbon transformation and high-quality upgrading, and promote sustainable development. In addition, the deep integration of machine learning and artificial intelligence technology continuously overcomes challenges such as complex working condition simulation, process control, and parameter optimization, fully empowering carbon capture and sustainable energy systems to improve quality and efficiency.

This Special Issue mainly focuses on the cutting-edge achievements and latest research in the fields of technology development, numerical modeling, and artificial intelligence empowerment of carbon capture and low-carbon energy systems. Topics of interest for publication include, but are not limited to:

  • Carbon capture technology;
  • Carbon storage technology;
  • Renewable energy;
  • Energy system construction and optimization;
  • Sustainable Transformation;
  • Zero emissions;
  • Energy-related climate mitigation strategies.

Dr. Xinyuan Gao
Dr. Yiqi Zhang
Dr. Bin Shen
Guest Editors

Manuscript Submission Information

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Keywords

  • carbon capture technology
  • carbon storage technology
  • renewable energy
  • energy system construction and optimization
  • sustainable transformation
  • zero emissions
  • energy-related climate mitigation strategies

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Published Papers (1 paper)

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Research

17 pages, 4211 KB  
Article
A Traceable Event-Window Evidence-Fusion Workflow for Screening Injection–Production Responses in Mature Waterflood Reservoirs
by Jian Li, Peng Cao, Yunfeng Xu, Congying Qiao, Yuhui Zhou, Yuanhao Zheng, Hongtu Qian and Qiaoyu Ge
Energies 2026, 19(14), 3424; https://doi.org/10.3390/en19143424 - 21 Jul 2026
Viewed by 36
Abstract
Mature waterflood reservoirs contain long injection–production histories, yet routine surveillance remains challenged by delayed producer responses and interference from multiple injectors. Existing diagnostic plots, CRM-type analyses, and data-driven models are useful, but they often require preselected patterns, aggregate long-period behavior, or provide limited [...] Read more.
Mature waterflood reservoirs contain long injection–production histories, yet routine surveillance remains challenged by delayed producer responses and interference from multiple injectors. Existing diagnostic plots, CRM-type analyses, and data-driven models are useful, but they often require preselected patterns, aggregate long-period behavior, or provide limited event-level traceability. This study presents an event-window evidence-fusion workflow (EWEF) for screening candidate injection–production responses without treating the screened objects as causal proof. EWEF uses material injection-rate perturbations as observational triggers and constructs auditable event-window evidence for candidate injector–producer objects. The workflow includes dynamic-record checking, injection-event extraction, candidate-pair construction, pre/post response-window quantification, lag scanning of oil-rate and water-cut responses, static-prior scoring from geometry and transmissibility proxies, relative multi-injector attribution, and rule-model disagreement routing. A transparent rule layer assigns engineering review classes, and a random-forest model trained on rule-derived weak labels serves only as a consistency-review gate. The workflow was applied to three anonymized field waterflood samples comprising 54 dynamic wells, 153 injection perturbation events, 297 candidate injector–producer evidence objects, and 30 final review-list objects. In the weak-label consistency model, lagged oil-rate correlation, water-cut shift, oil-rate shift, static prior, and water-cut lag correlation were recurrent influential input variables. In one representative event, the six-month post-event window showed that oil rate increased by 5.49 m3/d, whereas water cut decreased by 0.52 percentage points relative to the pre-event baseline. Lag scanning yielded modest correlations, with maximum absolute values of approximately 0.34; these correlations are treated as descriptive screening evidence rather than as statistically confirmed connectivity indicators. EWEF therefore provides a traceable workflow for screening and prioritizing field-review candidates. It does not establish causal interwell connectivity. Confirmation requires independent tracer, pressure, intervention, logging, or simulation evidence, and field-specific calibration remains necessary. Full article
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