NOVOTEL LONDON WEST • LONDON, UNITED KINGDOM • 2-4 APRIL 2019

DIOT: An Intelligent Predictive Maintenance

Strategy for Subsea Structure

Subrata Bhowmik

McDermott International

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Background

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• Low oil price prediction ($55-$75)

• No increase in production rate (North Sea)

• Old and inefficient maintenance approach increase OPEX

• Operators need significant OPEX reduction

Possible Solution

• Inspection less often AND at fewer location ANDrapidly AND remotely AND improve Reliability

• Use modern technology IOT, Cloud Computing and Artificial Intelligence

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Sensor Data for Structural Health

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Which is better for structural health assessment?

Visual Inspection

Sensor Data

Inspection

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Digital Intelligent Operational Twin (DIOT)

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Digital Intelligent Operational Twin (DIOT)

Low Cost IoT Sensors

Low Cost Cloud Storage

Machine Learning Algorithm

Data Driven Model for Subsea Structure

Predictive Maintenance

Real-Time Data Monitoring

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Overview of Maintenance Strategy

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Corrective or Maintenance

• Unscheduled interventions

• High downtime & cost

Planned or Preventive Maintenance

• Scheduled inspection

• Not preferable for subsea components

Predictive maintenance

• Indicate the possible Fault happening before it actual happen

• Reduction in unplanned downtime

• Reduction in inspection and maintenance time

• Reduction in OPEX

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Digital Intelligent Operational Twin (DIOT) Architecture

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Long-Short Term Memory (LSTM) Algorithm

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Machine Learning Algorithm forTime Series Forecasting/ Prediction

Special kind of Recurrent Neural Network (RNN)

Save the current output and feedback to the input and predict the future output.

Capability to memorize the inputs due to its internal memory.

Capability to memorize information for long periods of time.

Long-Short Term Memory (LSTM) Algorithm

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Digital Subsea Field

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Subsea Jumper

Subsea Jumper

Subsea Structure monitoring is required for

Xmas-Tree

Subsea Jumper

Pipeline Span

Subsea Wellhead

PLEM

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Case Study: Subsea Jumper

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Sensors installed

Strain Gauge

Accelerometer

Pressure gauge

ADCPStrain gauge AccelerometerData

Logger

Cloud

Digital Model

Data Logger

Wireless data transmission Data are stored in Cloud(AWS)

Data Analytics on Cloud

Structural Assessment from Data Driven model

Predictive maintenance strategy

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DIOT Modelling Stages

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StressEstimation

Machine LearningModel (Operational

Twin of Subsea Jumper)

Stage I: Data Preparation

Stage II: ML Model Building

Stage III: Stress Estimation from ML Model

StressRanges

Remaining Fatigue Life Calculation

Predictive Maintenance

Strategy

Stage IV: Remaining Fatigue Life Estimation

Stage V: Predictive Maintenance Strategy

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Results

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Time(s)

0 50 100 150 200 250 300 350 400 450 -0.1

5

-0

.10

-

0.0

5

0

0.0

5

0

.10

0.1

5

Acc

ele

rati

on

(m

/s2

)

PredictedMeasured

Comparison between measured and predicted acceleration data

Comparison between FEM calculated and predicted remain fatigue life

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Summary

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Reduction in ROV inspection frequency

Reduction in inspection time due to predictive maintenance schedule

Elimination of operational inefficiencies with cutting edge technologies

Iimprovement of useful remaining fatigue life using actual loading condition

Reduction in OPEX (Expected 30-50%)

Challenges

Sensor data transmission reliability in deepwater

Sensor data measurement rate

Model accuracy

Battery reliability

Majority of operators are still reluctant to AI

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McDermott Digital Twin Journey

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