Confidential. Not to be copied, distributed, or reproduced without prior approval. © 2017 Baker Hughes, a GE company, LLC - All rights reserved.

September 29, 2017

Industrial Analytics: Finding a Needle in an Ocean of Data

Arun SubramaniyanVP Data Science & Analytics, BHGE Digital

Physical streams create large data streams

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September 29, 2017 3

Industrial data volume, velocity is already high… and will increase

Seismic Data100 GB/survey

Drilling Data0.3 GB/well/day

Wireline Data5 GB/well/day

Pipeline Inspection 1.5 TB / 600 km

Process Data6 GB/plant/day

Ultrasound: Tubes1.2 TB/ 8 hrs

ERP Sys. Predix

• ~10-100x more volume• ~100-1000x more velocity

Industrial data requires a fundamentally different approach

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Converting Data to Actionable insights … Not Easy

Lack of Infrastructure

• Connectivity to remote assets

• Ingestion & management of large volumes of data

• Scaling was costly

Historical Practices

• Fragmented, discrete (and sometimes very good) software solutions.

• Highly localized deployment

• Time consuming & costly to move data around

A very limited amount of data generated is captured.

Leads to data & workflow silos.

Analytical Dearth

• Relied heavily on domain experts & brute force methods.

• Asset & data growth outpaces the growth of domain experts. Aging workforce.

Limited amount of insights.

Only 2-3% of data collected is proactively analyzed in Oil & Gas

BHGE is uniquely positioned to address these challenges

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September 29, 2017 5

Confidential. Not to be copied, distributed, or reproduced without prior approval.

Azure AWS GE Private Cloud

Data Fabric

Rapid Query Engine Analytics Engine

• Ingest

• Federated Access• Intelligent Cache

• Curate • Semantic Model • Deep Search

• Explore: Visualize, Build • Learn: Optimize, Recommend• Refine: Ingest code, productize• Connect: Orchestrate & Run

Full Stream Applications

• Light Weight • Cloud First • Edge Deployable

Our technology is a differentiator

PLM SystemsERP SystemsOT SystemsHistorians3rd Party SWMachines

3rd Party Analytics

A new approach for Analytics

6

Traditional Analytics (BI / Point Solutions) BHGE Analytics

Build

DeployUpdate

Idle

Schema definitions

Manual

Custom

Cumbersome

Large data warehouses

Relational databases

Scale

Deploy & maintain

Distributed queries across data silos

Data Fabric

Schema definitions

Augmented modeling & auto-updating

Automated

Guided

SeamlessElastic, distributed

computing

Distributed queries

Automated schema

Elastic computing

Rapid augmented modeling

Seamless

BHGE Analytics Framework delivers a step change for the industry

7

Finding a Needle in an Ocean

Data Science

Domain Knowledge

Software

“Needle in a haystack” Traditional Solutions

BHGE’s play zone

8

Building Enterprise-Grade Industrial Analytics

Approach

Probabilistic Learning System

Machine Learning

Deep Domain Models

A collection of

templates for broad industry

outcomes

Blueprints

Collection of kernels, technique& models for specific outcomes

Templates

Codifies relation between inputs & outputs

Models

Solution methods

Techniques

Building block for models

Kernels

Digital Twins

9

• A live up-to date digital representation of an asset, system or process• Used to predict performance

Physical Asset Digital Twin

Real TimeOperational Data

Maintenance History

Operational History

Fleet Aggregate Data

FMEA

CAD Model

FEA Model

Control Response

Hybrid Models

Physics Based

Probabilistic

Machine Learning + AI

+

+

.

.

✓Continuously Tuned✓Scalable✓Adaptable

BHGE Enables Analytics @ At All Scales

10

Raw Data Machine Learning / AI Hybrid Analytics

ObservationsBillions - Trillions

Rich DataHundreds - Thousands

Business DecisionsTens - Hundreds

Pro

cess

System of Systemse.g. Oilfield

System of Assets

e.g. WellAsset

e.g. Artificial Lift

Hybrid Approach

Inte

gra

ted

S

olu

tio

ns

Probabilistic Learning System

Machine Learning

Deep Domain Models

BHGE’s Unique Hybrid Approach

11

Traditional Analytics BHGE Analytics

PHYSICS TOOLS

MACHINE LEARNING/AI

DATA

PROBABILISTIC LEARNING

HYBRID MODELING

CONTINUOSLY UPDATE MODELS

DATA

ISOLATED DECISIONSReduce flow

Increase operating temperature

Adjust power use

ISOLATED INSIGHTSProbability of failureProduction forecastImage classification

The Physics Advantage: Sparse Data

12

Requires only 1 observation to predict precisely

𝑣

𝜃

Requires 10,000+ observations to predict approximately

𝑥 = 𝑣 cos 𝜃 𝑡

𝑦 = −1

2𝑔𝑡2 + 𝑣𝑠𝑖𝑛 𝜃 𝑡

Data only approach Hybrid Physics approach

The Hybrid Advantage: Overcoming Sparse & Uncertain Data

13

Input Data

Data Analytics

Pre

dic

ted

Le

ngt

h

Actual Length

Data Analytics Only

Hybrid Model: Data + Probabilistic + Physics Models

Data + Estimation Physics of Failure Model

Pre

dic

ted

Le

ngt

h

Actual Length

• Accurate Prediction• 0 False Negatives

• Poor Prediction• No correlation

14

Building a Nonlinear Probabilistic Model with 1 Observation

𝜂

Stress Stress intensityMetal temperature Damage

Digital Twins…Forecast Events with Accuracy

15

Physics Based Models + Machine Learning = Ability to Predict with High Accuracy

Statistical models Physics-based models Machine learning

Heavy Duty Gas Turbine cracking model

• Sparse events typical in industrial settings

• Gas Turbines don’t crack

everyday

• Poor correlation with statistical models

• No ability to forecast

✓ Physics based models capture variation with better accuracy

✓ Reduces false positives and false negatives & computes uncertainty

• Machine Learning is used to estimate missing data. Blue = Collected data Red / Green = Estimated data.

✓Data + Estimated Data + Physics Model →Prediction

Data is not always complete

Time

Time

Time

Da

ma

ge

Da

ma

ge

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