Machine Learning in Oil and Gas

Dave Lafferty

Introductions

Analytics Framework

What Is Machine Learning

Branch of artificial intelligence

Learns from training examples to predict future events

Mimics human reasoning

Best used for when there is a complex relationship between variables

Methods include Neural networks

Multilayer perceptron (MLP)

Radial basis functions

Support vector machines

Naïve Bayes

k-nearest neighbors

Geospatial predictive modeling

Why Machine Learning for Oil and Gas?

Ability to Scale

Ability to See More Complex Relationships

Ability to Automatically Continuously Refine Models

Yielding

Increased Production/Productivity

Reduced Operating Costs

Enhanced Safety

Barriers To Machine Learning

Machine learning can significantly increase production,

reduce costs and improve safety

However

A whole new approach needs to be used that goes

around the cost and complexity of conventional systems

to over come key barriers to adoption

Barrier 1: Getting Data Into Shape

Sensor Networks

Historians

Operator Logs

Control SystemEvents

Encryption

Data Ingestion

Data Cleansing

Normalization

Contextualization

Aggregation

Segmentation

Anonymization

Data Lake

Barrier 2: Moving Beyond Simple Analytics

Descriptive – What is happening now based on incoming

data. To mine the analytics, you typically use a real-time

dashboard and/or email reports.

Diagnostic – A look at past performance to determine what

happened and why. The result of the analysis is often an

analytic dashboard.

Predictive – An analysis of likely scenarios of what might

happen. The deliverables are usually a predictive forecast.

Prescriptive – This type of analysis reveals what actions

should be taken. This is the most valuable kind of analysis

and usually results in rules and recommendations for next

steps.

Descriptive Analytics Issues

Tells you what has happen – not

what will happen

Difficult to scale – people

interpret the information

“What” leads to “Why”

Difficult to pick the right KPIs

Tend to be static reports

Diagnostic Analytics Issues

Tends to be very laborious

Limited amount of “actionable”

insight generated

Provides good insight to a very

specific part of a problem

Cannot handle complex problems –

best for simple cause and effect

Barrier 3: Providing An End to End Solution

Sensing

Communicating

Big Data

Analytics

Visualization

Value

Barrier 4: Lack Of Domain Knowledge

Results must be put into actionable form

90ft case

Results must be merged back into 1st principals

Data driven models must meet the physical world

Barrier 5: Overcoming Machine Learning Issues

Requires clean data – data prep maybe

90% of the effort

Models require continuous refinements

Cloud based analytics better suited for

high latency applications (predictive

maintenance)

May require large amounts of data

streamed to the cloud

Barrier 6: Articulating Business Value

Think about business problems – not as a data science

problem

Present as verticals – not generic solutions

Production optimization

Corrosion

Chemical management

Equipment maintenance

Equipment health

Completions optimization

New Developments

New Business Models

“Behind the Firewall”Conventional Licensing

Infrastructure As a ServiceIaaS

Software As a ServiceSaaS

Platform As a ServicePaaS

Data As a ServiceDaaS

Product As a Service“Power By The Hour”

Owner/Operator

Risk

SharedRisk

SupplierRisk

New Developments

Edge Computing Edge Sensors – storage, computing and

communications

“Fog” computing

Low Power Wide Area (LPWA) Public Networks More like cellular service

Ubiquitous coverage

Shared infrastructure -> lower costs, faster time to market

Watch RPMA. LoRa and Sigfox

Open Automation Promoted by ExxonMobil

Make systems look like more aircraft systems than DCS

Smart RTU

Control

Loop

Data

Optimization

Model

Fog Computing

One or more collaborative end-user clients to carry out a

substantial amount of storage, communication, control,

configuration, measurement and management directly in the

field

Conclusions

Consider Two Things During The Conference

1) How Can I Use Machine Learning To Bring Value To My

Organization?

2) How Can Overcome The Barriers To Adoption?