AI-Driven Energy Data Integration and Prospect Evaluation with Sashi Gunturu

No Cost for class, please RSVP, breakfast provided!

High-Level Course Overview

This curriculum is designed to empower energy professionals through the  PIEScale  (Petrabytes Intelligence for Enterprise at Scale) ecosystem. By integrating the OSDU Data Repo, comprehensive energy databases, and real-time OSI PI sensor data, this course facilitates  Accelerated Scientific Discovery  and  Rapid, Low-Cost Improved Modeling . Students will master a verticalized framework for energy data management, transitioning from manual workflows to AI-driven environments where  Agentic AI  assists and accelerates tasks across data ingestion, subsurface interpretation, and predictive modeling.

Learning Objectives

By the end of this course, students will be able to:

Part 1 - Concepts Overview

1. Overview of Scientific Computing Platform in the AWS Cloud - PIEScale

2. Overview of the Agentic Framework with AWS

Part 2 - PIELake Introduction - Scientific Data Foundation for AI 

PIELake Leverages Lakehouse Architecture, MCP and Agentic Framework to provide any scale subsurface repository for small and large E&P with the same framework.

Part 3 - Implementation - Building the Subsurface Repository

3.1 - Data Preparation and Conditioning

Read and Process different Subsurface Datasets 

Geological and formations data, well data, GIS, seismic, completions, production and reservoir monitoring data spread across different data formats - Images, LAS, DLIS, SEGY, Shape file formats.

3.2 - Data Ingestion at Scale

• Ingest Geological data, seismic, geobodies, well and production data.

• Ingest Sensing data

3.3  - Search and Discovery

• AI Assisted search and discovery for surface data

• Contextual visualization of Large Scale data with PIEView

Part 4 - AI Assisted Fast Interpretation of Subsurface Data for Prospecting

Introduction to PIE - AI Assistant for Subsurface Analysis

• Geology assessment

• Geomechanics driven drilling risk assessment

• Flow Estimation

• What-if and Sensitivity Analysis

• Build a suitability model for a prospect

• Summarize prospect suitability by ranking targets on a 1-10 scale

Sashi Gunturu is the founder of Petrabytes and a technology leader in digital energy solutions, with nearly two decades of experience in the upstream oil and gas industry. His expertise includes geomechanics, subsurface data workflows, data modeling, data ingestion, and data integration, with a strong focus on transforming complex oilfield datasets into actionable business and technical insight. At Petrabytes, he has led development of cloud- and edge-enabled analytics platforms that support subsurface modeling, fiber-optic sensing, and integrated visualization for energy operations. He holds M.S. degrees from The University of Tulsa in Petroleum Engineering and Computer Science, and a B.Tech. in Chemical Engineering from Andhra University. Gunturu is a frequent speaker on OSDU, energy data standards, and AI-enabled workflows for the oilfield