Smart Emissions Monitoring

Design sprint to create an intelligent monitoring solution that empowered field teams with actionable insights for compliance and operational decision-making.

Project Overview

A major US oil and gas company faced mounting regulatory pressure and operational inefficiencies in emissions monitoring across their onshore wells. I led a rapid 4-week design sprint to create an intelligent monitoring solution that integrated multiple data sources, reduced false positives, and empowered field teams with actionable insights for compliance and operational decision-making.

Timeline: 4 weeks | Role: Design Lead

Key Responsibilities

Executive Facilitation: Led stakeholder workshop to define scope and align on critical functionality
User Research: Conducted comprehensive interviews with field engineers, production managers, and quality teams
Solution Architecture: Designed system concept integrating sensor data, operational context, and environmental factors
Technical Strategy: Collaborated on service architecture and phased implementation planning
Concept Development: Created detailed solution vision addressing regulatory compliance and operational efficiency

Problem

US onshore operators struggled with increasing emissions regulations while managing complex, siloed operations. Critical challenges included:

Data Reliability: False positives from sensors eroded trust and compromised decision-making
Manual Inefficiencies: Disparate datasets and manual calculations created operational bottlenecks
Prioritization Difficulties: Competing priorities and inefficient tools hindered timely field responses
Regulatory Pressure: Need for improved detection accuracy and faster response times

Field teams highlighted specific pain points: difficulty getting precise emissions data quickly, complex calculations requiring multiple variables, and the need to cross-reference operational and environmental data for accurate baselines.

Impact and Solution

Solution: Designed an intelligent emissions monitoring platform that combines continuous sensor data with contextual intelligence to deliver actionable insights for field operations.

Core Features

Smart Sensor Placement: Guidance system for optimized emissions detection coverage
Unified Dashboard: Real-time sensor data with robust filtering, search, and timestamp tracking
Contextual Intelligence: Integration of operational data, weather patterns, and aerial imagery
Anomaly Detection: AI engine for classifying emission events and automatic calculations
Field-Optimized Alerts: Lightweight, SMS-style notifications designed for low-connectivity environments
Cross-Referenced Analysis: System correlating multiple data sources for accurate event validation

Projected Impact

50% Improvement: Target for correct identification of emission alerts
Faster Response: Significant reduction in time between alert detection and field action
Regulatory Compliance: Enhanced tracking and reporting capabilities for changing regulations
Operational Efficiency: Reduced manual work and improved prioritization of field activities

Key Learnings

Connectivity Constraints Shape Design: Designing for field operations with unreliable connectivity required rethinking information density and alert prioritization to ensure critical data could be accessed when needed.

Domain Expertise Acceleration: Deep stakeholder interviews revealed the complexity of emissions calculations and the importance of environmental context, leading to more sophisticated solution requirements than initially scoped.

Trust Through Transparency: Previous system failures due to false positives emphasized the need for explainable AI and clear data provenance to rebuild user confidence in automated detection.

Rapid Stakeholder Alignment: The compressed timeline required efficient workshop facilitation to balance ambitious vision with actionable short-term deliverables, resulting in a phased approach that satisfied both strategic and immediate needs.