GEO4.0 2027 brings together the technologies, methodologies, and expertise transforming geoscience across the energy sector. The symposium will explore advances spanning exploration and production, subsurface modeling, data integration and decision intelligence, emerging computational methods, and reproducible digital workflows.
The technical program also extends to the evolving applications of geoscience across climate and energy solutions, new frontiers and unconventional resources, technology adoption, responsible innovation, and geological asset digitization.
Abstract submissions are invited across the following technical areas, reflecting the technologies, applications, and emerging priorities advancing the future of geoscience.
Physics-based and data-driven methods for seismic processing and inversion, structural mapping, horizon and fault interpretation, prospect generation, portfolio optimization, petrophysical evaluation, log analysis, core data integration, and thin section interpretation. Covers deep learning, machine learning, and automated workflows alongside traditional geophysical and geological evaluation.
Static and dynamic reservoir modeling, integrating physical priors into deep neural networks, history matching, surrogate models, digital twins, multi-scale predictive simulations, and seamless seismic-to-well integration workflows. Encompasses reservoir physics, geostatistical modeling, and next-generation data-driven simulation tools.
Comprehensive geodata governance, automated QA/QC, scalable subsurface data architecture, metadata optimization, and AI data readiness. Integrates multi-physics and multi-scale data fusion, uncertainty quantification and propagation, risk mitigation frameworks, and automated decision-support systems for exploration and development.
Frontier developments in subsurface computing, including autonomous AI agents for geoscience workflows, quantum computing applications for complex geophysical problems, high-performance cloud architectures, and novel computational algorithms for energy applications.
Benchmarking, open-source subsurface software tools, open datasets, standardized performance metrics, code optimization, workflow reproducibility, and trustworthy, explainable computational models in subsurface engineering.
Subsurface characterization, spatial analytics, spatial data processing, and digital twins applied to low-carbon energy. Key domains include predictive subsurface modeling and geomechanics for CCUS, geophysical characterization for geothermal systems, subsurface hydrogen storage and monitoring, geomechanical integrity, environmental surveillance, and geospatial/satellite analytics for critical mineral mapping.
Risk reduction in under-explored basins, complex structural regimes, deepwater targets, and unconventional resource plays using physics-informed neural networks, advanced seismic interpretation, and cloud data integration. Emphasizes predictive reservoir analytics, real-time drilling data integration, AI-assisted geomechanical modeling, digital rock physics, fracture prediction and characterization, and unconventional reservoir dynamics.
Operationalizing digital geoscience workflows, transitioning from computational pilots to full-scale enterprise deployment, algorithm scaling, IT/OT integration, and measuring digital ROI in subsurface asset development.
Evolving workforce competencies, upskilling geoscientists, domain-specific data literacy, ethical technology deployment, regulatory compliance, IP protection, and data governance policies across the energy sector.
Digitization, indexing, and standardization of legacy geological data and physical assets including core photos, thin sections, mud logs, paper well records, and petrophysical archives. Focuses on transforming raw physical and analogue geological records into AI-ready digital repositories to expedite geological studies and operational decision-making.
