C3 IoT Case Studies Large Oil Producer Leverages Advanced Analytics Platform

	Large Oil Producer Leverages Advanced Analytics Platform C3 IoT

Large Oil Producer Leverages Advanced Analytics Platform

C3 IoT

Technology Category	Analytics & Modeling - Machine Learning Analytics & Modeling - Predictive Analytics Infrastructure as a Service (IaaS)
Applicable Industries	Oil & Gas
Applicable Functions	Process Manufacturing
Use Cases	Predictive Maintenance Root Cause Analysis & Diagnosis
Challenge	Approximately 17,000 wells in the customer's portfolio have beam pump artificial lift technology. While beam pump technology is relatively inexpensive compared to other artificial lift technology, beam pumps fail frequently, at rates ranging from 66% to 95% per year. Unexpected failures result in weeks of lost production, emergency maintenance expenses, and costly equipment replacements. Read More
About Customer	One of the largest oil and gas producers in the U.S. with an upstream portfolio consists of more than 22,000 wells distributed across 10 countries in North America, South America, and the Middle East. Read More
Customer Name	undisclosed Read More
Solution	As part of the C3 IoT analytic software suite, C3 Predictive Maintenance employs machine learning-based algorithms to enhance failure prediction and diagnostic capabilities. The application augments traditional systems by continuously monitoring all instrument signals, tracking complex failure modes, and detecting operating anomalies associated with impending equipment failures for a large range of assets. In this deployment, C3 IoT integrated daily sensor readings from in-field equipment and unstructured data from maintenance work orders. This comprehensive data integration and analysis gives service teams a comprehensive weeks-ahead view of emerging equipment maintenance requirements, with detailed supporting data and diagnostic tools to support maintenance decision making. Hardware Components - Daily sensor Read More Log in to view content
Data Collected	Asset Status Tracking, Fault Detection, Operation Performance, Overall Equipment Effectiveness, Per-Unit Maintenance Costs Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Machine Learning

Analytics & Modeling - Predictive Analytics

Infrastructure as a Service (IaaS)

Applicable Industries

Oil & Gas

Applicable Functions

Process Manufacturing

Use Cases

Predictive Maintenance

Root Cause Analysis & Diagnosis

Challenge

Approximately 17,000 wells in the customer's portfolio have beam pump artificial lift technology. While beam pump technology is relatively inexpensive compared to other artificial lift technology, beam pumps fail frequently, at rates ranging from 66% to 95% per year. Unexpected failures result in weeks of lost production, emergency maintenance expenses, and costly equipment replacements.

About Customer

One of the largest oil and gas producers in the U.S. with an upstream portfolio consists of more than 22,000 wells distributed across 10 countries in North America, South America, and the Middle East.

Customer Name

undisclosed

Solution

As part of the C3 IoT analytic software suite, C3 Predictive Maintenance employs machine learning-based algorithms to enhance failure prediction and diagnostic capabilities. The application augments traditional systems by continuously monitoring all instrument signals, tracking complex failure modes, and detecting operating anomalies associated with impending equipment failures for a large range of assets. In this deployment, C3 IoT integrated daily sensor readings from in-field equipment and unstructured data from maintenance work orders. This comprehensive data integration and analysis gives service teams a comprehensive weeks-ahead view of emerging equipment maintenance requirements, with detailed supporting data and diagnostic tools to support maintenance decision making. Hardware Components - Daily sensor

Data Collected

Asset Status Tracking, Fault Detection, Operation Performance, Overall Equipment Effectiveness, Per-Unit Maintenance Costs

Impact #1	[Efficiency Improvement - Maintenance] Cloud solutions enable prediction of emerging equipment maintenance requirements weeks before equipment failures.
Impact #2	[Efficiency Improvement - Maintenance] Real-time status reports enable maintenance personnel to remotely diagnose the status of a device, often before a failure occurs.

Impact #1

[Efficiency Improvement - Maintenance]
Cloud solutions enable prediction of emerging equipment maintenance requirements weeks before equipment failures.

Impact #2

[Efficiency Improvement - Maintenance]
Real-time status reports enable maintenance personnel to remotely diagnose the status of a device, often before a failure occurs.

Benefit #1	The C3 Predictive Maintenance application accurately predicted 45% of equipment failures that were to occur within 6 months.
Benefit #2	The C3 Predictive Maintenance application analyzed over 1,000 wells.

Benefit #1

The C3 Predictive Maintenance application accurately predicted 45% of equipment failures that were to occur within 6 months.

Benefit #2

The C3 Predictive Maintenance application analyzed over 1,000 wells.

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Overview

Large Oil Producer Leverages Advanced Analytics Platform

Operational Impact

Quantitative Benefit