SparkCognition Case Studies Cognitive Analytics for Oil and Gas

	Cognitive Analytics for Oil and Gas SparkCognition

Cognitive Analytics for Oil and Gas

SparkCognition

Technology Category	Analytics & Modeling - Big Data Analytics Analytics & Modeling - Machine Learning Analytics & Modeling - Predictive Analytics
Applicable Industries	Oil & Gas
Applicable Functions	Maintenance
Use Cases	Predictive Maintenance
Challenge	Oil and gas companies are having problems learning from the data to understand the different operational states and failure modes of assets, and uses this learning to provide adequate warning before failures occur so operators can plan for corrective actions thus optimizing their Operations and Maintenance budgets. Read More
About Customer	An large oil and gas company Read More
Customer Name	Undisclosed Read More
Solution	SparkPredict has been deployed on Upstream assets such as Drillstrings and Electrical Submersible Pumps as well as Downstream assets such as pumps in refineries. For assets with no labeled failures, SparkPredict analyzes events and identifies anomalies (unknown operating states, failure conditions, etc.) automatically. SparkPredict uses the identified anomalies to recognize patterns of deviation and raise alarms if significant deviation from normal is observed. SparkPredict leverages cutting edge, cognitive, machine learning techniques to additionally predict asset failures. The cognitive, or reasoning based, nature of our algorithms mean SparkPredict can be deployed to any asset in any location and the insights will adapt to the unique characteristics of that particular asset. In addition, SparkPredict integrates with already installed Asset Monitoring systems or works with data historians, like OSI PI, to leverage pre-existing and/or live streaming data for improved failure predictions. Read More Log in to view content
Data Collected	Asset Performance, Asset Status Tracking, Device Diagnostic Status, Fault Detection, Operation Performance Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Big Data Analytics

Analytics & Modeling - Machine Learning

Analytics & Modeling - Predictive Analytics

Applicable Industries

Oil & Gas

Applicable Functions

Maintenance

Use Cases

Predictive Maintenance

Challenge

Oil and gas companies are having problems learning from the data to understand the different operational states and failure modes of assets, and uses this learning to provide adequate warning before failures occur so operators can plan for corrective actions thus optimizing their Operations and Maintenance budgets.

About Customer

An large oil and gas company

Customer Name

Undisclosed

Solution

SparkPredict has been deployed on Upstream assets such as Drillstrings and Electrical Submersible Pumps as well as Downstream assets such as pumps in refineries. For assets with no labeled failures, SparkPredict analyzes events and identifies anomalies (unknown operating states, failure conditions, etc.) automatically. SparkPredict uses the identified anomalies to recognize patterns of deviation and raise alarms if significant deviation from normal is observed. SparkPredict leverages cutting edge, cognitive, machine learning techniques to additionally predict asset failures. The cognitive, or reasoning based, nature of our algorithms mean SparkPredict can be deployed to any asset in any location and the insights will adapt to the unique characteristics of that particular asset. In addition, SparkPredict integrates with already installed Asset Monitoring systems or works with data historians, like OSI PI, to leverage pre-existing and/or live streaming data for improved failure predictions.

Data Collected

Asset Performance, Asset Status Tracking, Device Diagnostic Status, Fault Detection, Operation Performance

Impact #1	[Process Optimization - Remote Diagnostics] SparkPredict detects both Stuck Pipe and Wash Out conditions with high accuracy.
Impact #2	[Efficiency Improvement - Operation] SparkPredict provides an in-context advisory system technicians can use to quickly find documents and other digital resources to address issues, automatically provide meaningful remediation steps, and seamlessly communicate and share data with team member
Impact #3	[Efficiency Improvement - Maintenance] For Electrical Submersible Pumps (ESPs), SparkPredict predicts failures days in advance.

Impact #1

[Process Optimization - Remote Diagnostics]
SparkPredict detects both Stuck Pipe and Wash Out conditions with high accuracy.

Impact #2

[Efficiency Improvement - Operation]
SparkPredict provides an in-context advisory system technicians can use to quickly find documents and other digital resources to address issues, automatically provide meaningful remediation steps, and seamlessly communicate and share data with team member

Impact #3

[Efficiency Improvement - Maintenance]
For Electrical Submersible Pumps (ESPs), SparkPredict predicts failures days in advance.

Benefit #1	The average lead times for detection of these failures is: Stuck Pipe: 1.2 hours, Wash Out: 2.3 hours.
Benefit #2	SparkCognition targeted two failure modes, accounting for 85% of all ESP failures, and were able to provide the following median forewarning: Electrical Short: 5.5 days, Mechanical Breakdown: 6 days.

Benefit #1

The average lead times for detection of these failures is: Stuck Pipe: 1.2 hours, Wash Out: 2.3 hours.

Benefit #2

SparkCognition targeted two failure modes, accounting for 85% of all ESP failures, and were able to provide the following median forewarning: Electrical Short: 5.5 days, Mechanical Breakdown: 6 days.

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Overview

Cognitive Analytics for Oil and Gas

Operational Impact

Quantitative Benefit