CUSTOMER STORY | OIL AND GAS
GX Labs: €1.3M of extra revenue and €19.6M of repair costs avoided each year
In the oil sector, pump failures in remote locations caused well shutdowns lasting up to 6 months. TIMi built predictive models on 3 years of data from 5,000 wells and 500 sensors.
Additional revenue per year
€1.3M
Repair costs avoided per year
€19.6M
Wells covered, with 500 sensors
5,000
OVERVIEW
The challenge.
Predict breakdowns to prevent accidents and optimize operations.
Shutdowns of up to 6 months
Pump failures caused well shutdowns lasting up to 6 months.
Remote locations
The wells are in remote locations, where each intervention is costly.
SOLUTION
What was done.
A suite of predictive models built on 3 years of sensor data.
Data
3 years of data from 5,000 wells and 500 sensors: 50 GB of raw data and 4,000 derived variables.
Predictive models
The models enable early failure detection and real-time analysis of weak signals.
Actionable insights
Maintenance teams get clear insights they can act on.
RESULTS
What changed.
Less downtime, and a direct financial effect.
Downtime
Drastically reduced, which improves both productivity and safety.
Revenue
€1.3M of additional revenue per year.
Repair costs
€19.6M of avoided repair costs per year.
USE CASES BEHIND THIS STORY
Read the method.
Predictive Maintenance
Anticipate equipment failures from the data you already collect, with the reasons.
Equipment Failure Prediction
Estimate the probability that an equipment fails at a given horizon, and why.
Sensor Data Modeling at Scale
Model thousands of sensor variables together on a standard server.
SEE FOR YOURSELF
Test it on your own data.
Run TIMi on your own data with a free trial, or talk to us about your case.
