SAP Innovation Awards 2018 Entry Pitch Deck

Varun Garg- Marathon Oil

Operating at the Speed of Business: Machine Learning on SAP HANA

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Marathon Oil

“Adopting the right technology for solving a

problem is crucial. Machine learning is the right

technology for predicting these situations.”

- Varun Garg, Development Manager

The Marathon Oil team partnered with SAP to deliver

machine learning applications native on the SAP

HANA platform. The solution is designed to replace

rule based alerts in favor of machine learning models

that identify patterns that lead to downtime, ultimately

improving oil production per well and lower operating

costs.

Operating at the Speed of Business: ML on HANA

SAP HANA Platform- Text Analytics, Predictive Analytics Library,

Automated Predictive Libraries

SAP Predictive Analytics Factory

OSIsoft IoT Integrator

Faster identification of

production issues

Less downtime and more oil

production

Varun Garg, Development Manager

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Participant Bio

Marathon Oil

Operating at the Speed of Business: Machine Learning on SAP HANA

Varun Garg- Development Manager, Machine Learning Project Manager

Marathon Oil partnered with the SAP Data Science team to deliver machine

learning

Marathon Oil is an independent E&P company focused on lower cost, higher margin

U.S. resource plays that are oil-rich. Marathon’s strategy is focused on

strengthening the balance sheet, relentless focus on costs, simplifying and

concentrating the portfolio, and profitable growth within cash flows.

www.marathonoil.com

Oil & Gas

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Solution Overview

Business Challenge:

• Many conditions cause oil well production loss and downtime; these conditions should be identified, predicted, and prevented

• Paraffin build-up is an example of a condition that causes production loss. Paraffin can plug flowlines, gas lift valves, and the wellbore

Early detection can preempt/reduce downtime.

• Due to the dynamic nature of the problem, current rule-based alerts are limited in their effectiveness

Solution:

• Marathon Oil extracted 90 attributes of data from the HANA Platform to train machine learning algorithms in SAP Predictive Analytics

• The data driven model improves over time

• Flags “paraffin build-up” for a human to review

• Human confirms or rejects decisions of the model and enables creation of curated data, which leads to continuous improvements

of model accuracy

• Model lifecycle management is automated

Benefits:

• Increase output by reducing downtime and potentially identify and rectify situations from happening sooner.

• The approach is fit to enhance the other 25 rules identified and substitute rule based alerts with adaptive models based on

machine learning

• Additional use cases will be developed with machine learning to improve production:

• Well loading

• Auto choke plugging

• Gas lift valve plugging

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Solution Details

Key technology components:

• SAP HANA Platform

• OSIsoft PI

• IoT Integrator by OSIsoft

• SAP Plant Maintenance

*Data and HANA engines uses in PoC

*Data flow

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Benefits

Business value:

Social value:

Human empowerment:

▪ Faster identification of production issues

▪ Less downtime and more oil production

▪ Continuous improvement of machine learning algorithms

▪ Production engineers now have reliable data to identify production issues

▪ All issues can be analyzed and detected with machine learning, including previously overlooked issues that were considered low value

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Architecture

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Illustration

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Illustration

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Quote

“Adopting the right technology for solving a problem is crucial. Machine learning is

the right technology for predicting these situations.”

- Varun Garg, Development Manager

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Deployment details

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Additional information