Sandeep Mukherjee

Petrophysics Advisor Apache

Sandeep Mukherjee is a Petrophysical Advisor at Apache Corporation. He specializes in formation evaluation and integrates multi-scale subsurface observations to develop holistic understanding of reservoir systems. His work focuses on reducing uncertainty and improving confidence in reservoir properties that inform conceptual models of field development.

Sandeep develops physics-informed, domain-aware analytics and machine-learning methods that augment traditional workflows, helping interpreters extract reliable signals from large and complex subsurface datasets. His work emphasizes scalable evaluation frameworks that accelerate interpretation, improve data fidelity, and enable basin-level analog discovery.

He is particularly interested in how knowledge-guided models and retrieval-assisted search can shorten cycle times and make technical workflows more repeatable. He collaborates with multidisciplinary teams to translate geoscience into actionable reservoir intelligence aligned with conceptual models of field development.

Sandeep has an MS in Geology from University of Minnesota.

Main Conference Day 2 - February 25, 2026

11:00 AM Panel Discussion: From Proof of Concept to Scale: Success Factors and Execution Strategies

While the potential of AI is no longer in question, the journey from POC to scalable, enterprise-grade deployment remains complex. A key challenge lies in the disconnect between technology leaders and the operational realities of energy environments, leading to unrealistic expectations around the pace and complexity of scaling AI. In this session we will review the end-to-end lifecycle of AI deployment, from initial PoC, to platform-based implementation within business units and ultimately enterprise-wide integration, shining a spotlight on common pitfalls, success factors and execution strategies.

Examining how data architecture and standardization can help structure both operational and historical data to ensure AI models interact effectively and reliably at scale
Considering the role of human-centered design and user interfaces in driving adoption of AI-powered tools, making complex technologies more intuitive, accessible, and impactful for end users
Real-world use cases that illustrate different implementation strategies by AI type, with a spotlight on how traditional machine learning models and generative AI require distinct approaches to scaling and integration

Check out the incredible speaker line-up to see who will be joining Sandeep.

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