machine learning in practical terms
TRANSCRIPT
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MACHINE LEARNING In Practical Terms
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Mayadah Alhashem – LRRSD, NRMD
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RELATED EXPERIENCE
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IPTC 2020: Machine Learning Model for Multiphase Flow Regimes
ADIPEC 2019: Supervised Machine Learning In Predicting Flow Regimes
Chemical Engineering (UC Santa Barbara, California)MS- Mechanical Engineering (KAUST, Thuwal – Saudi Arabia)
Programming & Machine Learning(EdX, Coursera, Udacity) S
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ntsInkless Printer (Undergraduate, 2014)
Removable Trap Stations For Hydrocarbon Flowlines (Aramco, 2020)
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MACHINE LEARNING
“Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed”
Samuel, Arthur (1959)
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HUMAN BRAINLearns from Experience
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MACHINE LEARNINGLearns from Experience
TRADITIONAL PROGRAMFollows
Instructions
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AGENDA1. Motivation2. Introduction to ML3. Benefits for US!4. Challenges5. Conclusion
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MotivationWhy do we need an upgrade? 1
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“We will leverage the close proximity of energy sources and our distinctive logistical offer to stimulate
a new phase of industrialization”
Motivation
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THE OIL BUSINESS
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Oil Trap
ExplorationProduction
Upstream
Midstream & Downstream
Oil
Gas
$$$ ?
Motivation
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THE INFORMATION BUSINESS
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Upstream – Big Data System
Downstream –Outcomes
Data Sources
Data AnalyticsData Acquisition
Visualization
Models
Machine Learning
Data Mining
Artificial Intelligence
Motivation
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Introduction to Machine Learning
What is ML anyway? 210
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COMPUTER
A COMPARISON: TRADITIONAL Modeling
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DATA
Program/Model Expected
Output
PredictedOutput
Motivation Introduction to ML
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COMPUTER
A COMPARISON: MACHINE LEARNING
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COMPUTER
DATA
OPTIMIZATION
PredictedOutput
Program/Model Expected
Output
Motivation Introduction to ML
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THE MACHINE LEARNING PROCESS
Motivation Introduction to ML14
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ARE YOU SMARTER THAN SAHER?
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INPUTOUTPUT
A B
2 1 2
1 2 0.5
35 5 7
18 6 ?
2202 2 ?
84 168 ?
Units: km Units: hour
3
1101
0.5
Motivation Introduction to ML
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ARE YOU SMARTER THAN SAHER?
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Uses sensors to measure time taken to
pass a fixed distance
Logic (Program) is given:
Speed =∆𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒
∆𝑇𝑖𝑚𝑒
Motivation Introduction to ML
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Training Set ~(70%)
Test Set ~(30%)
MACHINE LEARNING TERMS
Important Terms
FeaturesData Set
Shape
Size
Number of bites
Color
Motivation Introduction to ML
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MAIN PROBLEM TYPES TACKLED BY ML
Problem Types
Predict a numeric quantity instead of a class.
Numeric Prediction
Learn relationships between attributes.
Association
Put instances into predefined classes.
Classification
Discover classes of instances that belong together.
Clustering
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Supervised Learning“Known Answer”
Unsupervised Learning“Unknown Answer”
Motivation Introduction to ML
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Case Study:PREDICTING MULTIPHASE FLOW REGIMES
18Motivation Introduction to ML
Problem
Pipeline
Annular Flow with DropsAnnular FlowDispersed BubbleSlug Flow
?Churn Flow
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Case Study:PREDICTING MULTIPHASE FLOW REGIMES
(Experimental Data)
435 examples
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70% Training10% Cross Validation20% Testing
Motivation Introduction to ML
INPUT OUTPUT
Flow Regime (Known)%
Liquid velocity
Gas velocity
Water cut
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0.0
02
0.0
16
0.1
42
0.2
80
0.2
71
86
.20
% 90
.80
%
77
%
85
.10
%
82
.80
%
70%
75%
80%
85%
90%
95%
0.000
0.050
0.100
0.150
0.200
0.250
0.300
D E C I S I O N T R E E
R A N D O M F O R E S T
L O G I S T I C R E G R E S S I O N
S U P P O R T V E C T O R
M A C H I N E
N E U R A L N E T W O R K
( M L P )
F1-A
CC
UR
AC
Y SC
OR
E
TRA
ININ
G T
IME
(SEC
)
ML ALGORITHMS TRAIN TIME COMPARISON
Case Study:PREDICTING MULTIPHASE FLOW REGIMES
20Motivation Introduction to ML
Random Forest = 0.0003 sDecision Tree = 0.0015 s
86
.2% 90
.8%
77
.0%
85
.1%
82
.8%
70%
75%
80%
85%
90%
95%
F1 S
CO
RE
ML ALGORITHMS F1-ACCURACY-SCORE COMPARISON
Flow Control Slug Mitigation Corrosion Prevention
5 Models
Random Forest = 91%Decision Tree = 86%
𝐹1 =2 × 𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 × 𝑟𝑒𝑐𝑎𝑙𝑙
𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 + 𝑟𝑒𝑐𝑎𝑙𝑙
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Long Term BenefitsWhy should Aramco invest in ML? 3
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ML BENEFITS FOR ARAMCO
More Accurate ModelsMake better predictions in less time.
Cost Reduction◉ Operation/Execution
Savings◉ Planning Savings
Improved SafetyPredicted hazards and accurate results to ensure safe implementation.
22Motivation Introduction to ML Benefits for Aramco
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ChallengesFor using ML in Aramco and in general 4
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ML IMPLEMENTATION CHALLENGES
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OPPORTUNITIES
Acquiring A LOT of Data
Digitizing Maps & Data
Organizing Data
Determining Important Features
“Results” Data for
Supervised Learning
Testing Different
Algorithms
Motivation Introduction to ML Benefits for Aramco Implementation Challenges
Data Quality
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ML IMPLEMENTATION CHALLENGES
25Motivation Introduction to ML Benefits for Aramco Implementation Challenges
Fear of the Unknown
Actually needs YOUR help!
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ConclusionKey takeaways 5
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MACHINE LEARNING OPPORTUNITIES
27Motivation Introduction to ML Benefits for Aramco Implementation Challenges Conclusion
ML TOOLSBUSINESS FUNCTION
Reduced Time
Improved Accuracy
Cost Optimization
Predictions (e.g. Forecasts, Equipment Life, etc)
Improved Safety
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CHANGE can start NOW
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It starts with the individual
Initiatives
Education
Projects
Application
Motivation Introduction to ML Benefits for Aramco Implementation Challenges Conclusion
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ADDITIONAL RESOURCES
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Course Name Link Duration Purpose PracticalityIntroduction to Python:
Absolute Beginner(Microsoft, EdX)
https://www.edx.org/course/introduction-to-python-absolute-beginner-2
5 Weeks3–4 hrs / week
Get introduced to Python Programming
Medium
Introduction to Python: Fundamentals
(Microsoft, EdX)
https://www.edx.org/course/introduction-to-python-fundamentals-2
5 Weeks3–4 hrs / week
Get introduced to Python Programming
Medium
30 Days of Code (Hackerrank)
https://www.hackerrank.com/domains/tutorials/30-days-of-code
1 Month10 min–1 hr/ day
Improve coding skills by coding challenges for 30 days in a row
High
Machine Learning(Stanford, Coursera)
https://www.coursera.org/learn/machine-learning
11 Weeks5 hrs / week
Get introduced to Machine Learning
Low
Machine Learning Engineer Nanodegree
(Udacity)
https://www.udacity.com/course/machine-learning-engineer-nanodegree--nd009t
3 Months12-15 hrs / week
Learn how to apply machine learning on
real projectsHigh
Code Free Data Sciencehttps://www.coursera.org/learn/code-free-
data-science/3 Weeks
5 hrs / weekLearn how to use KNIME Analytics
TBD
@alhashmm / [email protected]
30 Sep 2020
Pipeline Corrosion Behavior & Performance
machine Learning (mL) Engineering Management[1,2]
Lessons Learned
RAFA G. Mora
NACE Dhahran Saudi Arabia Section
[1] Mora, R.G., Hopkins, P., Cote, E.I., Shie, T., 2016, Pipeline Integrity Management Systems :
A Practical Approach, ASME Press, LCCN 2016012084 | ISBN 9780791861110
[2] The views, judgments, opinions and recommendations expressed in
this webinar do not necessarily reflect those of the Saudi Aramco nor
is it obligated to adopt any of them
Presentation: 20 min. Q&A: 10 min.
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Saudi Aramco: Public
Pipelines & Digital: Business Needs &Opportunities
AGEING PIPELINES
MORE NEEDS & CHALLENGES
CORROSION
Scrapable & Unscrapable Transmission Pipelines
mL: machine Learning D-Twin: Digital Twin
Large Data, Complex Variables changing over time
Dynamic
CORROSION
BEHAVIOR &
PERFORMANCE
Challenge
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Saudi Aramco: Public
Outline
Business Needs & Opportunities
Traditional Software & machine Learning (mL)
Corrosion mL “Big Picture” Model
Integrating CRISP-DM + Corrosion Approach
Engineering Management (EM) Lessons Learned
Q&A
mL: Machine Learning
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Saudi Aramco: Public
Traditional
Software Fixed Formulation
How Software is different from mL? How to Connect both?
Model
Formulation Results
Fixed
Answers at the time
“Regular Planning”
mLearning
Dynamic Model
Reality Variability
Analysis
Predict Behavior
Behavior, Newer Rules, Ongoing Learning
Dynamic Answers
& Results
“Ever Green Planning”
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Saudi Aramco: Public
Corrosion mL Big Picture: Behavior, Performance & Prediction
CORROSION
Machine
Learning (Dynamic)
Dynamic
Behavior &
Performance
Dynamic
Prediction
Digital Twin
Scrapable & Unscrapable Transmission Pipelines
Fixed Formulation: Updated Results
mL: machine Learning D-Twin: Digital Twin
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Saudi Aramco: Public
Integrating CRISP-DM & Pipeline Corrosion
2. Knowledge
Synergy
1. Phenomena
3. Talent
Growth
4. Business
Sustainability
5. Adapt &
Scale
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Saudi Aramco: Public mL: machine Learning D-Twin: Digital Twin
Corrosion Dynamics Phenomena
Dynamic Behavior
Hydrocarbon quality -> corrosivity
Multiple Causal Factors: from
hazards to threats
Initiation & Propagation
Time-dependent
Pipeline Location-dependent
External & Internal Corrosion in Transmission Pipelines
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Saudi Aramco: Public mL: machine Learning D-Twin: Digital Twin
Knowledge + Synergy
Corrosion Degradation
O&M effects and measures
R&D ongoing learning
mL modeling types & focuses
D-Twin multi-levels
Multi-disciplinary Synergy
Corrosion, O&M, R&D, mL, D-Twin
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Saudi Aramco: Public mL: machine Learning D-Twin: Digital Twin
Talent Growth
Early Engagement
Selection of mL eLearning courses
Training Workshops, OJT & SME-led
Accelerate In-house growth
Accountability & Operational
Model
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Saudi Aramco: Public mL: machine Learning D-Twin: Digital Twin
Business Sustainability
PL corrosion + mL Charter
Well-defined Governance
Open-Source mL Programming
(no Black box software)
Digital Road Map
Oversight and Guidance
Use Management of Change
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Saudi Aramco: Public mL: machine Learning D-Twin: Digital Twin
5. Adapt & Scale
Progressive development from pilot onwards
Platform-ready for Improvements
Scaling enabled for other pipeline services
Aiming towards Harvesting
Continue Improving…
Thank YOU
Q&A