putting analytics to work for you - bhge | fullstream oil & gas s3 putting... · 2018-06-20 ·...

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

September 18-20, 2017

Arun SubramaniyanVP Data Science and Analytics, BHGE Digital

Data and Models

Putting analyticsto work for you:

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

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

September 29, 2017 2


Industrial data requires a fundamentally different approach

Drilling data0.3 GB / well / day

Wireline data5 GB / well / day

Seismic data500+ GB / survey

Ultrasound: tubes1.2 TB / 8 hrs

Pipeline inspection

1.5 TB / 600 km

Process data6 GB / plant / day

ERP Sys. Predix™

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


A new approach for analytics


BHGE Analytics Framework delivers a step change for the industry

Build

DeployUpdate

Idle

Schema definitions

Manual

Custom

Cumbersome

Large data warehouses

Relational databases

Scale

Deploy and maintain

Traditional analytics (BI / Point Solutions) BHGE analytics

Distributed queries

Automated schema

Elastic computing

Rapid augmented modeling

Automated

Guided

Seamlessr

r

r

r

Distributed queries across data silos

Data fabric

Schema definitions

Augmented modeling and auto-updating

Elastic, distributed computing


Finding a needle in an ocean



Data science

Domain knowledge

Software

“Needle in a haystack” Traditional solutions

BHGE’s play zone


Building enterprise-grade industrial analytics


Hybrid approach

Probabilistic learning system

Machine learning

Deep domain models

Building block for models

Kernels

Solution methods

Techniques

Codifies relation between inputs and outputs

Models

Collection of kernels, techniques, and models for specific outcomes

TemplatesA collection of templates for broad industry outcomes

Blueprints


.

.

Digital Twins


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

• Used to predict performance

Physical asset Digital TwinReal-time

operational data

Maintenance history

Operational history

Fleet aggregate data

FMEA

CAD model

FEA model

Control response

Physics based

Probabilistic

Machine learning+ AI

Hybrid models

• Continuously tuned• Scalable• Adaptable


Our technology is a differentiator



PLM SystemsERP SystemsOT SystemsHistorians3rd Party SWMachinesAzure AWS GE's private Cloud

Fullstream Applications

• Light weight • Cloud first • Edge deployable

• Explore: visualize, build• Learn: optimize, recommend• Refine: ingest code, productize• Connect: orchestrate and run

3rd Party AnalyticsRapid Query Engine• Federated access• Intelligent cache

Analytics Engine

Data Fabric• Ingest • Curate • Semantic model

™


BHGE’s unique hybrid approach


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


Industrial outcomes require massive scale – on demand


Python

Excel

Hysys

Fortran

Python

Excel

Hysys

Fortran

Python

.NET

HYSYS

Fortran/C/C++

Operational data

Maintenance data

Failure & Test reports

Analytic Type

ESP Python

AGRU HYSYS

Compressor Fortran

Optimizer Java

Pipe model C++

Fortran HYSYS Python

CURATED CATALOG

ANALYTICS SILOS

Robust optimization

ORCHESTRATED MODEL & OPTIMIZER

DATA SILOS

Millions of runs/day

CONNECT

REFINE

LEARN

Au

tom

ate

d I

ng

est

ion

Silos of data/analytics + Federation + Scale + Constant Learning = Need for Cloud

Python


Machine learning for insight coverage


Insight coverage significantly increases with machine learning

Increased insight coverage for rapid model development and deployment

Example:Descriptive modelsData sources Raw data Statistical models Machine learning / AI

1.5 TB / 600 km300M scans per inspection x

2,730 sensors per ring x2 bytes per reading

Inspections DataCalipers

Magnetics

Ultrasonics

250-500 MB0

10

20

30

40

50

60

70

80

90

0 100 200 300 400

Axial length, mm

Dep

th, %

WT

Operating pressure

42 bar

Operating pressure x SF

58 bar

Operating pressure x SF

58bar

Operating pressure

42 bar

<100 MB

Years of domain expertise to build models

~1 TB10x faster model

development timeand

100x increase in data coverage

=1,000x increase

in insight coverage

3 month total to build models

Weld Angle Detection

ROI Scoring

Statistical Models

Model Development Speed →

Hig

he

r D

ata

Co

vera

ge →

Machine Learning based Model

Insight Coverage


Machine learning for a non BHGE asset


Machine learning is essential to build models for non BHGE assets

Model LEARNS high dimensional relationships purely from data

• Build ML models for fast accurate prediction

• Accurate data-driven models built from observed data

• Trends extracted not evident from raw data

Interaction plot of raw data – no obvious correlations

Sensitivity data withmachine learning

✓ Asset agnostic model development and deployment

Digital Twin Framework

™




Physics-based models + machine learning = ability to predict with high accuracy

Heavy Duty gas turbine cracking model

Statistical models

• Sparse events typical in industrial settings

• Gas turbines don’t crack everyday

• Poor correlation with statistical models

• No ability to forecast

• Machine Learning is used to estimate missing data

• Blue = Collected data. Red / Green = Estimated data. Poor correlation with statistical models

✓ Data + estimated data + physical model = prediction

Time

Machine learningData is not always complete

Time

Da

ma

ge

Physics-based models

✓ Physics-based models capture variation with better accuracy

✓ Reduces false positives and false negatives and computes uncertainty

Da

ma

ge

Time


System of Systems Digital Twin


Cracklength Previous S1 Coefficients Tcd Duration Tfire

Cracking ModelPhysics Based

Statistical Model

Beta Compressor Eta Compressor

Failure Mode 3

Creep ModelMachine Learning Based

Beta Eta

Failure Mode 1 Failure Mode 2

Crack Length Next Critical Crack Length

System Failure

Digital Twins Forecast Component & System Level Risk


Digital Twin of a well


Fluid Model – Computes Density, Surface Tension, Viscosity, etc.

Casing (Pipe) ModelIPR Model Pump Model Tubing Model Pressure Gradient

@ Surface

Flow Rate, Q

Head

Efficiency

Horse Power

Best Efficiency Point


Current operating

point

Sub-optimal points that consume same power



Manually increasing power without optimizing other parameters could result in lower production

Using Digital Twin to find bestoperating point

167 extra barrels / day by changingrpm and wellhead pressure

Genetic algorithm-based global optimizer

• Explores the entire solution space

• Provides a family of optimal solutions for various scenarios

1

1

Total Liquid Flow (bb / day)


The 100x Advantage



On-Prem Single Well Optimizer

2 runs, 1 well

64GB RAM, 12 Cores3 min

IntelliStreamTM cloud optimizer

3 runs, 25 wells

16GB RAM, 4 Cores, 4 Nodes1 min

Core engines developed by BHGE delivers a 100x speed up


Cloud based solution – is a necessity



• Data aggregation storage and curation

• Analytics aggregation and orchestration

• Elastically scale compute and services

• Distributed and parallel computing

• Large scale simulation and modeling

• Continuously update digital twins

• Upgradability and Maintainability

• Learning and knowledge capture across systems


We invent smarter ways to bring energy to the world.



putting analytics to work for you - bhge | fullstream oil & gas s3 putting... · 2018-06-20 ·...

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