Design optimization of BOP for fatigue and strength in HPHT environment using Isight and FE-Safe

DESIGN OPTIMIZATION OF BLOWOUT
PREVENTER FOR FATIGUE AND
STRENGTH IN HPHT ENVIRONMENT USING
ISIGHT AND FE-SAFE
2016 SIMULIA South Regional User Meeting – Dassault Systèmes
Houston, Texas
October 13, 2016
Presented by VIAS
Arindam Chakraborty, PhD, PE

© 2016 Virtual Integrated Analytics Solutions Inc.
Outline
• Overview of Company
• Strength of Simulation
• BOP Design Optimization using Simulation
• Machine Learning in Design and Simulation
• Summary
2

Overview of Company
Engineering
Consultancy
Training
Hardware
Software
• Cross Industry Experience
• Houston based Entity
• Other US Locations
• Dassault Systèmes SIMULIA Value Added
Reseller – Abaqus, Isight, fe-safe, Tosca
• Provide Virtual Design Experience through
Collaboration and Data Analytics
• Provide 3D printing and AM simulation
services
3

Strength of Simulation Example: BOP Design
BOP Body - Quarter Model
• Mechanical device designed to seal off
wellbore, safely control and monitor oil and
gas well in case of blowout
• High Pressure High Temperature (HPHT)
conditions for Deepwater Wells
• Highest safety and quality standards are
mandatory
• Challenge of optimizing the design – weight
reduction
• Need for efficient simulation process to
reduce design time and cost
6
To seal drill pipe
OD and shear the
main body

BOP Design Optimization
1
5
Response Surface
Approximation
Fatigue and
Strength
3
Capturing
Reality in
Simulation2
Exploring
Design Space
through DOE 4
6
Design Optimization
Product
Requirements
and Problem
Statement
8

Simulation Driven Design Process – Step 1
1
Product
Requirements
and Problem
Statement
10

Problem Statement – Step 1
Initial Design Dimensions
Radius = 2.5 in Cavity Height = 10 in
Cavity Width = 13.75 in
Top Beam = 19 in
Lower Beam = 19 in
Side Wall = 23 in
11
18-3/4 – 20k BOP Design
Product Requirements
• Sufficient design life
under cyclic loading
• Weight reduction

Problem Statement – Step 1
Design VariablesObjective:
Maximize fatigue life
Design Variables (Deterministic):
 Cavity Height(CH): 8" ≤ H ≤ 12"
 Cavity Width (CW): 11" ≤ W ≤ 16.5
 Top Beam(TB): 11.4" ≤ TB ≤ 26.6“
 Lower Beam (LB): 11.4" ≤ LB ≤ 26.6“
 Side Wall (SW): 16.1" ≤ SW ≤ 29.9“
 Radius (R): 2.235" ≤ R ≤ 5.215“
Constraint:
 Maximum Displacement ≤ 0.040 inches
 Mass ≤ 80% of initial mass
 Cavity Height (CH) should be greater than 2 times
the Radius (R)
 Cavity Width (CW) should be greater than 10 inches
plus Radius (R)
R CH
CW
TB
LB
SW
12

2
13
Capturing Reality
in Simulation

Material, Loads, BCs – Step 2
Linear Elastic Material Properties for Steel AISI 4130:
 Young’s Modulus: 29,000 ksi
 Yield Strength: 66.7 ksi
Loads (Cycling from 0 to Max.):
Internal Pressure = 20 ksi
Vertical Load = 150 kips
14
Z-Symmetry
X-Symmetry
Y-Fixed

Fatigue and
Strength 3
16

FEA for Strength – Step 3 (Abaqus)
von Mises Stress due to Internal Pressure von Mises Stress due to Vertical Lift Load
Node 2 for Y-Displacement Constraint
Initial Design
17
Mass
(lb)
Y-Disp
Node 1 (in)
Y-Disp
Node 2 (in)
15497.5 0.0439 0.0333
Output Node 1 for Y-Displacement
Constraint

Fatigue Life – Step 3 (fe-safe)
Initial Design
19
Lowest fatigue cyclesElement/node with lowest life
588.8
Neuber Correction
Internal Pressure = 20 ksi Vertical Load = 150 kips
Pressure Cycles = 100 cycles Vertical Load Cycles = 500 cycles
ONE BLOCK

Exploring
Design Space
through DOE 4
20

Simulation Workflow – Step 4 (Isight)
Initial Design
Design of
Experiments
(DOE)
Response
Surface
Optimized
Design
21
fe-safe
DOE (Optimal Latin Hypercube – 76 Runs)

Design of Experiments – Step 4 (Isight)
Pareto Plot
Radius and Cavity Width are the design parameters that have higher effect on the fatigue life of the BOP
22

5
Response Surface
Approximation
24

Initial Design
Design of
Experiments
(DOE)
Response
Surface
Optimized
Design
25
fe-safe

Approximation – Step 5 (Isight)
Elliptical Basis Function (EBF)
Error Analysis - Fatigue
Responses R-Squared
Mass 0.99789
Y Displacement – Node 1 0.99295
Y Displacement – Node 2 0.99335
Fatigue Cycles 0.94123
26
Error Analysis – Disp N 1

6
Design Optimization
28

Initial Design
Design of
Experiments
(DOE)
Response
Surface
Optimized
Design
29
fe-safe
Sequential Quadratic Programming
– 57 Iterations

Deterministic Optimum Design – Step 6
Case CH
(in)
CW
(in)
R
(in)
LB
(in)
TB
(in)
SW
(in)
Fatigue
Cycles
Mass
(lb)
Y-Disp
Node 1 (in)
Y-Disp
Node 2 (in)
Initial Design
(Mean)
10.0 13.75 3.725 19.0 19.0 23.0 588.8 15497.5 0.0439 0.0333
Optimum
Design
8.0 13.02 3.02 22.05 17.45 16.1 1348.9 12501.8 0.040 0.0287
R CH
CW
TB
LB
SW
Input Output
Initial Design Optimum Design
• Fatigue life is increased 2.3 times from the initial design
• Geometry, weight and displacement constraints are satisfied
31

• Machine learning can help in design simulations by generating
predictive models to estimate output given initial parameters.
• We approximate the output of a simulation using deep network
architectures for regression.
• The idea is to capture compact, high-order
representations in an efficient and iterative
manner.
• Learning takes place by combining non-
linear combinations of inputs on many
layers of abstraction.
• Low levels concepts are the foundation for
high level concepts.
Machine Learning in Design and
Simulation
32

Projection on two variables showing actual data (black) and
approximation using deep learning (red).
Machine Learning to Predict Output of
Simulations
Good Approximation of Predicted Values vs. Actual Values
34

Machine Learning to Minimize the
Number of Simulations
Machine learning can additionally help to minimize the number
of simulations by using a technique known as “active learning”.
In active learning the algorithm point to those instances (parameter
vectors) that are “most informative” to increase the accuracy in the
predictions.
Active
Learning
35

Sample Estimation vs True Performance.
Machine Learning to Minimize The
Number of Simulations
37

Summary
• Optimizing BOP Design using SIMULIA Power of Portfolio
Software Concludes:
• fatigue life is increased by 2.3 times from the initial design;
• weight of the BOP is reduced by 20%;
• maximum allowable displacement of 0.04 inches is satisfied;
• automation of the design and simulation process helps to decrease
the cost and reduce the time;
• Mathematics based as compared to heuristic design approach.
• Using Machine Learning in BOP Design and Simulation
Helps to:
• predict the output of simulations much faster than traditional
FEA type models;
• minimize the number of simulations through active learning.
38

VIAS – Software / Training / Consulting
Certified SIMULIA Support and Training
Email : achakraborty@viascorp.com
Phone : +1 (832) 301-0881
Engineering Consulting
39

Acknowledgements
Noesis Al – Mat Podskarbi, Ricardo Vilalta
SIMULIA Support – Sohini Sarkar, Dave
Naehring, Sreeparna Sengupta
40

Thank you
41

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