CASE STUDY | Global Oil & Gas Company

Enterprise AI to Improve Gas
Turbine Compressor Reliability

A major Asian oil & gas company wanted to improve theIn 6 weeks, the Baker Hughes and C3 AI (BHC3) team ingested

efficiency of its upstream operations by increasing the reliability

over one billion rows of data from 4 disparate data sources,of the Gas Turbine Compressors (GTC) used across its fields.

re-factored the existing anomaly detection model, embedded it

GTCs are equipped with hundreds of sensors that constantly
monitor the state of this critical equipment using predefined
thresholds to maintain production. However, the system

into a scalable deployment framework, and integrated the oil &gas company’s proprietary Failure Mode Library to provide
prescriptive insights in the application.

monitoring the GTCs generated thousands of alerts every month

The configured BHC3 Reliability application accurately detects

for each compressor unit and could not prioritize alerts or provide

over 88% of anomalous events, while reducing the number ofprescriptive insights. As a result, engineers manually sifted throughgenerated alarms by 99%. Furthermore, the BHC3 teamalarms to identify anomalies, perform failure mode analyses, anddemonstrated how additional rotating equipment can berecalibrate thresholds. This time-intensive process required a highincorporated in a matter of days, and how the application canlevel of expertise and knowledge of compressor systems.

be scaled across the company’s operations in weeks.

To address these challenges and increase the reliability of its

With BHC3 Reliability, the oil & gas company can achieve overcompressors, the oil & gas company developed a prototype$40M in annual savings from increased efficiency, increased

machine learning model to detect anomalous equipment

production, and reduced maintenance costs, and the ability tobehaviors with greater accuracy. The company then selected

deploy a cohesive predictive maintenance strategy across its

Baker Hughes and C3 AI® to deploy the model at scale using

fleet of compressors.

the BHC3

TMReliability application.

Project Objectives
Create a unified, federated data model integrating disparate

data sources (e.g., aset hierarchy, telemetry, anomaly events,and Failure Mode Library)

Deliver a workflow-enabled application that uses machinelearning to detect anomalies in GTCs and provides
prescriptive insights to prevent unplanned outages

Reduce the number of false alarms created by the existing
rules-based system

Results After Six Weeks

99%

$40M

88%

4.7%

Reduction in false alarms

Compressor failuresaccurately predicted using
6 months of data

Estimated annual savings

Reduction in Non-Productive
Time

Reduce Non-Productive Time caused by unplanned downtime© 2021 C3.ai | All Rights Reserved | 21_0713

Challenges

About the Oil & Gas Company

High-power GTCs are capital-intensive, critical assets for oil & gas upstream operations.

• $50B+ in 2019 annual revenue

GTCs are used to compress gas for injection in order to increase production or to drive gas
from the fields to gas processing facilities, LNG facilities, and local markets. With several
compression and expansion stages and many operating parameters (e.g., temperature,pressure, flow, rotational frequency, vibration) that need to be controlled simultaneously,
GTCs are amongst the most complex equipment across onshore and offshore fields.
The oil & gas company utilized a conventional rules-based application to monitor a fleetof 60 GTCs of different types, each equipped with more than 150 sensors. Due to the highsensitivity of the rules-based alerting system, more than 1,000 alerts were triggered for eachcompressor unit every month, most of which were false alarms. Engineers manually sifted
through alerts to identify critical anomalies and perform failure mode analyses, a timeconsuming process that requires high level of expertise and knowledge of sensor systems.
Furthermore, operators and engineers found it challenging to maintain the current
monitoring software. Nearly 100,000 individual thresholds form the rule base for the fleet.
As assets are maintained and parts replaced, production set points adjusted, and sensors
calibrated, the task of keeping these individual thresholds up to date became an
additional time-consuming task.
While operators and asset engineers leveraged their field experience to investigate andresolve the failures and alarms, several challenges remained. Operators:• Lacked the tools to accurately detect anomalous behavior of GTCs and prevent• 40,000+ employees• Fully integrated oil & gas company
operating in 20+ countries globally

Project Highlights• Six weeks from data availability toproduction-ready application• One billion+ rows of data ingested
from four disparate data sources
across four+ years of operational data
• Created a unified and scalable object
model to represent asset hierarchy,
telemetry data, anomaly events, andintegrated a proprietary Failure
Mode Library• Re-factored the company’s existing

unplanned compressor shutdowns

anomaly detection model onto the• Analyzed over 1,000 alarms per month from each compressor unit, most of which weresuccessful scaling to a second

false alarms

compressor• Manually inspected and identified potential failure modes in the case of an anomaly, aprocess that required a high level of expertise to interpret raw sensor signals andmaintenance and inspection logs

BHC3 AI Suite and demonstrated• 150+ sensor signals used as machinelearning features per asset• 10+ machine learning model• Manually updated the rule-based alerting system with changing operating set points,

configurations trained and tested perpart replacements, and instrumentation, requiring high effort and timecompressor

To address these challenges, the company developed a prototype machine learning model,and engaged Baker Hughes and C3 AI to help build a production-ready application that• Six BHC3 Reliability application userinterfaces configured

could scale across all of its GTCs.

Approach
Over 6 weeks, a team of Baker Hughes and C3 AI experts collaborated with data scientists and subject matter experts from the oil & gas
company to re-factor the prototype machine learning model onto the BHC3 AI Suite, and extend the model into a production-readyapplication that could detect compressor anomaly events, provide operators with early warning signals, and incorporate failure mode
insights to reduce unplanned downtime.
The BHC3 team began by ingesting over 1 billion rows of data from four disparate source systems to create a unified, federated data
image. This analytics-ready data image included more than 4 years of telemetry data, anomaly events, an asset hierarchy
representation, and the company’s proprietary Failure Mode Library.
© 2021 C3.ai | All Rights Reserved | 21_0713

The existing anomaly detection model was refactored to run at scale on the BHC3 AI Suite and using a dataset with labelled anomaly events
created by experts from company, the BHC3 team configured asset-optimal alerting thresholds to improve the quality of alerts. A proprietary
Failure Mode Library was integrated to generate prescriptive insights and enable faster alarm resolution, with specific focus on an extensible
implementation of the Failure Mode Library that allows for continuous improvement and regular updates. The team demonstrated thescalability of the new application by onboarding a second GTC in only two days. This separately trained anomaly detection model was fed
by its own data pipelines using a BHC3 asset deployment framework and a newly developed workflow for structured asset onboarding.
Lastly, to provide an easily customizable UI framework, the BHC3 Reliability application was configured to visualize the AI-based alerts
and prescriptive insights generated by the models. User-friendly dashboards provide a managerial view of high-level KPIs, top AI-driven
alerts, and overall asset health of the compressor systems and subsystems. Users can filter alerts and are provided with the most likely
Failure Modes for each anomaly.

Benefits
With the BHC3 Reliability application, the oil & gas

By using the BHC3 Reliability application, the operators

company can:will be able to:• Reduce Non-Productive Time by 4.7%, equivalent to at least• Identify high-risk compressor units by accurately detecting over

$40M in annual savings when scaled across upstream

88% of all GTC anomaly events and proactively dispatching

operations

resources to address impending outages• Reduce maintenance costs by enabling predictive, versus• Increase alert quality, with a 99% reduction in false alarms

reactive, maintenance• Extend compressor asset lifetime due to reduced operating
hours in anomalous state• Reduce high-risk emergency repairs and improve safety• Resolve issues faster by leveraging precise Failure Modeinsights that inform the AI alerts• Onboard new assets into the application in a matter of days
• View top-line operating KPIs such as overall system health andall relevant data through a single application

Solution Architecture

Operation Systems
Asset Hierarchy

BHC3 Reliability

Telemetry
Failure Mode LibraryAnomaly Labels© 2021 C3.ai | All Rights Reserved | 21_0713

Enterprise AI for Oil & GasThe BHC3 AI Suite provides the necessary and comprehensive services to build enterprise-scale AI applications up to 25x faster than alternative
approaches. The BHC3 AI Suite integrates all relevant data sources to rapidly generate predictive insights across the oil and gas value chain. When
deployed at enterprise-scale, BHC3 applications can deliver up to $100 million and more in annual economic value to oil and gas organizations.

Upstream

Drilling Risk& Productivity

Midstream

Well Integrity

WellPlacement

Production
Optimization

Asset Integrity

Downstream

Pipeline Reliability

Production
ReliabilityHydrocarbon
Loss

LNG Operations

Refining Reliability

Production Schedule
Optimization

Sustainability

Refining Process
Optimization

SupplyNetwork Risk

Demand Forecasting

Inventory
Optimization

BHC3 AI Suite

Exploration

Development

Production

Transportation

Refining

RetailBHC3 provides wide-ranging Enterprise AI applications for oil and gas companies, including optimizing artificial lift systems operations, selection ofdrilling targets, and production operations. These pre-configured applications provide insights to automate the well lifecycle design process, allow
real-time monitoring of process reliability, and lower costs of maintenance interventions.

BHC3 ProcessOptimization

BHC3
Reliability
Increase operations uptime by anticipating
equipment risks using a system of systems
approach. Unsupervised deep-learning algorithms
leverage the unified data to identify anomalies andrecommend prescriptive actions. Actionableinsights increase production, reduce unplanned
downtime, and improve safety in operations.

Improve throughput and product quality byapplying advanced machine learning to complex
discrete, batch, or process manufacturing datain order to pinpoint process opportunities
to identify defects early and improve
production yield.

BHC3 Inventory
OptimiztionMinimize industrial parts and equipment inventory
levels, free up working capital and reducestock-out risks. BHC3 Inventory Optimization
real-time visibility across all inventory anddynamically optimizes reorder parameters tominimize holding and shipping costs.

BHC3 Production
Optimization

BHC3 Production
Schedule Optimization
Optimize planning and scheduling of
midstream, and downstream operations using
advanced AI and machine learning. BHC3Production Schedule Optimization generates
dynamic manufacturing and distribution plansand optimal industrial schedules using aholistic view of customer demand, supply
chain, manufacturing, and distribution.

Initial Results in 8-12 Weeks

Optimize upstream oil & gas production, reduce
operational expenses, and improve operationalvisibility. BHC3 Production Optimization unifies
all available data and uses AI to generate
well-based virtual metering network, provide
surface network visualizations, and optimize
injection wells to optimize upstream production.

BHC3 Asset
Integrity
Prevent downtime, loss of containment, orsafety hazards by applying advanced machinelearning and computer vision to identify andaddress corrosion risks. Integrate
physics-based models with machine learningalgorithms and computer vision to predict
corrosion risks and optimize intervention.

Visit BakerHughesC3.ai/get-started© 2021 C3.ai | All Rights Reserved | 21_0713