Call for Abstracts: Tracks & Topics

We will begin accepting abstracts for DTOG 2025 soon.

We invite authors and individuals who are working on practical, real-world, end-to-end digital solutions that involve machine learning and artificial intelligence applicable to the three tracks below.

An acceptable presentation abstract submission is one that is technically sound, free from personalities and bias (especially of a commercial nature), one in which the author supplies information in a form readily available to the public or adds a new concept or development to existing technical knowledge.

The oil and gas sector is undergoing a transformative digital revolution, integrating advanced technologies to optimize production, reduce costs, and enhance sustainability. This track delves into innovative digital solutions tailored for both onshore and offshore operations, focusing on challenges unique to unconventional reservoirs, complex wells, and emissions monitoring. Discussions will highlight real-time asset monitoring, AI-driven failure predictions, hybrid modeling approaches, and advancements in automation and machine learning. Attendees will gain insights into how computer vision, IoT, and edge computing are reshaping safety, inspection, and operational efficiency. Additionally, this track explores best practices in data governance, cybersecurity, and emerging standards for AI/ML implementation, paving the way for a digitally empowered oil and gas future.

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As the global energy sector shifts toward renewables, the need for digital solutions in wind, solar, hydrogen, and energy storage systems has never been greater. This track addresses the digital transformation challenges faced by renewable energy providers, from optimizing offshore wind farms and batteries to integrating multi-physics systems for holistic efficiency. Participants will explore cutting-edge topics such as AI-driven predictive maintenance, adaptive modeling, and the role of neural networks in analyzing renewable energy data. The track also emphasizes the importance of data governance, compliance with regulatory frameworks, and robust cybersecurity measures. Case studies and entrepreneurial showcases will spotlight groundbreaking projects that demonstrate how digital innovations are driving sustainability and shaping the future of clean energy.

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Breaking into artificial intelligence (AI) through machine learning (ML) or generative AI requires a structured approach that balances foundational knowledge, hands-on experience, and continuous learning. Individuals should start by building a strong mathematical foundation in linear algebra, calculus, probability, and statistics, as these concepts underpin ML algorithms. Programming proficiency, particularly in Python, is crucial.

Teams and organizations aiming to integrate AI should focus on fostering a culture of experimentation and learning. Companies can start small by identifying problems that AI can solve, assembling cross-functional teams with domain expertise, and leveraging pre-trained models or cloud-based AI services before developing custom solutions. Upskilling employees through internal training, workshops, or collaborations with AI research institutions can accelerate adoption. Organizations should also prioritize ethical AI practices, data governance, and model interpretability to ensure responsible implementation.

With sessions on ethical AI, workforce augmentation, and entrepreneurial innovations, this track offers a holistic view of AI's pervasive influence. Whether you're looking to harness AI's potential for your industry or stay ahead in this rapidly evolving landscape, this track is your gateway to understanding and leveraging the power of AI across boundaries.

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