DAY 01: Digital Oilfield Fundamentals

Architecture, Components, and Data Infrastructure for Modern Upstream Operations

Module 1.1: The Digital Oilfield Journey

Evolution from manual readings to SCADA to AI-driven operations

Key milestones: SCADA systems (1980s), real-time centers (2000s), cloud adoption (2010s), AI/ML era (2020s)

Industry drivers: cost pressure, talent shortage, safety, ESG commitments

Module 1.2: Digital Oilfield Architecture

Five architecture layers: Field Devices, Control Systems, SCADA/DCS, Operations, Enterprise

Data infrastructure: Historians (OSIsoft PI, Honeywell PHD, AspenTech), data lakes, edge computing

Integration patterns: OPC-UA, REST APIs, Kafka, MQTT

Module 1.3: Data Management in Digital Oilfield

Data quality dimensions: accuracy, completeness, timeliness, consistency

Real-time data challenges: high-frequency data, compression, gaps and outliers

IT/OT convergence, security, and access control

Demo: Digital Oilfield Architecture Visualization - Real-time data flow from field to enterprise

Exercise: Data Architecture Assessment - Review architecture, identify bottlenecks, propose improvements

Case Study: Shell's Digital Transformation - IAM program, real-time centers, 5-10% production uplift

DAY 02: Real-Time Monitoring & IoT

SCADA Systems, Sensor Technologies, and Edge Computing for Upstream Operations

Module 2.1: Modern SCADA for Upstream

SCADA components: RTUs, PLCs, communication networks, master station, HMI

Modern trends: cloud-based architectures, mobile access, remote operations

Cybersecurity: IEC 62443, NIST frameworks

Module 2.2: Sensor Technologies & IIoT

Measurement technologies: pressure, temperature, flow, multiphase meters

Wireless sensors: WirelessHART, ISA100.11a, LoRaWAN

Advanced sensing: fiber optics (DTS, DAS), smart completions, condition monitoring

Module 2.3: Edge Computing for Upstream

Edge vs. fog vs. cloud processing strategies

Edge platforms: AWS IoT Greengrass, Azure IoT Edge, AVEVA Edge

Use cases: real-time protection, local ML inference, data filtering, offline operation

Containerization: Docker, Kubernetes for IoT

Demo: SCADA Simulator - Real-time data acquisition, alarm management, remote control

Exercise: Monitoring System Design - Design solution for remote well pad including sensors, communication, edge processing

Case Study: Equinor's Johan Sverdrup - 30,000+ sensors, integrated operations, 70% reduction in offshore personnel

DAY 03: AI/ML for Digital Operations

Machine Learning Applications for Production Optimization and Predictive Maintenance

Module 3.1: AI/ML Overview for Operations

Supervised/unsupervised learning, time-series forecasting (LSTM, Prophet)

Feature engineering from SCADA and historian data

Model validation: train/test split, cross-validation, backtesting

Module 3.2: Production Optimization with AI

Virtual Flow Metering, well test optimization, choke optimization

Gas lift and ESP optimization using ML

Model architectures: XGBoost, neural networks, hybrid physics-ML

Human-in-the-loop vs. closed-loop control

Module 3.3: Predictive Maintenance

Maturity levels: reactive, preventive, condition-based, predictive, prescriptive

ML techniques: Remaining Useful Life estimation, anomaly detection (Isolation Forest, Autoencoders)

Failure classification and root cause analysis with explainable AI (SHAP)

Demo: AI Production Optimization Dashboard - Virtual flow meter, production forecast, optimization recommendations

Exercise: Building Anomaly Detection Model - Load data, preprocess, train Isolation Forest, evaluate performance

Case Study: BP's APEX Platform - Predictive maintenance across 20,000+ equipment, $100M+ annual value

DAY 04: Generative AI & Agentic AI Applications

Large Language Models, RAG Systems, and Autonomous Agents for Digital Oilfield

Module 4.1: LLMs in the Digital Oilfield

LLM capabilities: natural language interfaces, automated reporting, troubleshooting, code generation

Foundation models: GPT-4, Claude, Gemini, Llama, Mistral

API integration for enterprise systems

Module 4.2: Prompt Engineering & RAG

Prompt techniques: zero-shot, few-shot, chain-of-thought reasoning

RAG architecture: document ingestion, chunking, embedding models, vector databases

Semantic search and response synthesis

Module 4.3: Agentic AI for Digital Oilfield

Agent architecture: perception, reasoning, action, memory

Tool use: database queries, API calls, calculations

Applications: production surveillance, maintenance planning, regulatory compliance, operations assistance

Safety: human-in-the-loop, guardrails, audit trails

Demo: Operations Chatbot with RAG - Query manuals, analyze alarms, generate reports

Demo: Multi-Agent Production Monitoring - Surveillance agent, diagnostic agent, recommendation agent with human approval

Exercise: Designing Operations Agent - Define purpose, tools, interaction flow, safety constraints

Case Study: SLB's Lumi AI Platform - Domain-specific assistant, Delfi integration, RAG over knowledge base

DAY 05: Integration & Business Value

Digital Transformation Strategy, ROI Quantification, and Implementation Roadmap

Module 5.1: Quantifying Digital Value

Value drivers: production uplift, cost reduction, safety, environmental, efficiency

ROI framework: baseline, KPIs, benefit categories, cost elements

Payback period, NPV analysis, risk-adjusted returns

Module 5.2: Implementation Strategy

Four-phase approach: Foundation (data infrastructure), Optimization (analytics), Automation (AI/ML), Autonomy (Agentic AI)

Success factors: executive sponsorship, cross-functional teams, agile delivery, scalable architecture

Change management and workforce development

Demo: Digital Value Dashboard - Real-time value tracking, KPI performance, ROI projections

Exercise: Digital Transformation Roadmap - Assess maturity, identify opportunities, design plan, estimate costs/benefits

Case Study: Aramco's 4IRC - AI/ML across 500+ assets, digital twin for Ghawar, $1B+ annual value

Case Study: Chevron's Digital Journey - Azure platform, AI Center of Excellence, predictive maintenance

Course Wrap-up - Key takeaways, resources, certification process