A midstream pipeline operator managing 800+ miles of pipeline infrastructure across West Texas. Aging infrastructure, increasing regulatory pressure on pipeline integrity, and a monitoring system that was essentially manual — field technicians driving routes and checking gauges. They had 30 years of operational data sitting in spreadsheets, SCADA historians, and paper records that nobody had ever analyzed systematically.
A pipeline integrity audit flagged several segments as high-risk for corrosion-related failure. The operator's response plan was manual inspection — which would take 18 months and cost $4M. They needed a way to prioritize inspection based on actual risk, not just pipeline age.
They needed data engineering to consolidate 30 years of fragmented data, and ML capability to build a predictive model on top of it. We had both.
Data consolidation pipeline — ingested SCADA historian data, manual inspection records, maintenance logs, soil composition data, cathodic protection readings, and production flow data. Cleaned, normalized, and unified into a single analytical data warehouse. Predictive integrity model using gradient-boosted trees to estimate failure probability per pipeline segment based on 40+ features. Real-time monitoring dashboard overlaying predictive risk scores on a GIS pipeline map. Alert engine for segments approaching risk thresholds.
Identified the 12% of pipeline segments responsible for 80% of integrity risk — enabling targeted inspection that cost $600K instead of $4M. Predictive model accuracy validated at 91% against historical failure data. The operator shifted from reactive maintenance to predictive — catching problems before they became incidents.
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