Getting the most from BIG Data Begins Where Centralized Data Management Ends SET Decision Support System PM Succession Planning Tool Maximize Data and R&D ROI

Big data Business model Low Cost of Entry (think Spotify, Adobe) SaaS Distributed Development Costs Distributed Software Licensing Costs Shared Featured Development Everyone Benefits from New Custom Features Single Source of Truth Accessible, Reliable, Repeatable Democratization of Advanced SET

Why should you believe in big data? DIVER Efficiently Implement Scientific Remediation Advancements Substantial Cost Savings – Increase Efficiency – Reduce Tedious Tasks Take Control of Projects – Be Proactive, Not Reactive Rapid, Reliable Response to Crisis Scenarios (Legal, Enforcement) Maximize Value of Costly Data Routinely Gathered Standardize Analysis and Reporting for Even Greater Cost Savings

DIVR™ infrastructure Modularity

DIVR™ infrastructure SECURE CLOUD-BASED DATA MANAGEMENT

Login SECURITY

Landing Page

GIS - Data Queries

Well-Specific Hydrostratigraphs & Diagnostic Gauge Plots

Recovery Trends

Analyte Concentrations

Mann-kendall Trending

Lessons learned Strong client support and sustained rollout/adoption training are critical for success Decentralized data from companies across the board exhibited substantial errors, and the centralized system radically improved data quality The greatest challenge was the collection of reliable historic data – policies and procedures to mitigate that challenge should be adopted Few personnel could reliably create EDD files; electronic field data collection prevents many data errors and “bottle-necking” through limited qualified personnel Clients/consultants were able to respond to crisis scenarios almost instantly due to rapid cloud access to the data/maps/reports/analytics/etc. from any cloud connected device After completion of rollout, ongoing cost reductions were virtually immediate due to automation of many routine or non-routine analytical and reporting functions Project management and reserves estimation were substantially improved with automated predictive analytics identifying when key metrics could be attained (e.g., decline curve analysis, COC decay, plume stability) New technologies could be readily rolled out across the board regardless of consultant sophistication due to electronic data capture and analytics automation

Thank you J. Michael Hawthorne, PG mhawthorne@geiconsultants.com Lisa Reyenga lreyenga@geiconsultants.com