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Copyright 2018 Baker Hughes, a GE company, LLC (“BHGE”). All rights reserved. The information contained in this document is company confidential and proprietary property of BHGE and its affiliates. It is to be used only for the benefit of BHGE and may not be distributed, transmitted, reproduced, altered, or used for any purpose without the express written consent of BHGE.
ESP shutdown prediction using hybrid support vector machine and improved parallel particle swarm optimizationMoradeyo Adesanwo, Oladele Bello, Lajith Murali, Ajish Kb, William Milne, BHGE
EuALF 2018 European Artificial Lift Forum13-14 June 2018, AECC, Aberdeen, UK
June 27, 2018
Copyright 2018 Baker Hughes, a GE company, LLC (“BHGE”). All rights reserved.
Outline
June 27, 2018 2
• Introduction and current challenges
• Current challenges in why predict ESP shutdown?
• Objectives
• Methodology
• Field examples - results and discussion
• Conclusions
Copyright 2018 Baker Hughes, a GE company, LLC (“BHGE”). All rights reserved.
Background and motivation for ESP-based well operations management
June 27, 2018 3
• Big data technologies are now being used to address many industrial problems & challenges related to high volume, high velocity, high variety
• Digitization using advanced sensors in intelligent oilfield solutions has resulted in the generation of huge amount of data that can be used to enhance the performance, operational health surveillance & management of ESP
• Need to use powerful analytics to estimate inexpensively and accurately equipment performance degradation, equipment failures, or detrimental interactions among subsystems
• Enhanced monitorability to reduce downtime and improve hydrocarbon recovery
Facility Performance Operational
Casual Data
- Reliability Data- Equipment Design Data - Well Design Data - Reservoir Data - PVY Data
- Cross Flow - Water Cut - Gas Cut- Prod. Data - Gas Coning - Water BT Time - Dynamic Reservoir Properties
Casual Data Casual Data - Motor Data- Pump Data - Controller Data- Set-Points - Maintenance Data- Equipment Condition
Benefits
Advanced Analytics
Intelligent Insights
Higher Production Rate
Lower Prod. Loss
Explicit View of Performance Alignment with Production KPI
Improved ProductionCasual analysis to identify process bottlenecksControl variability in the process
Value
Heterogeneity of data from dispatch control systemsWell management systems and SCADA
Variety Velocity Volume
Historical data amounting to several terabytes for analysis
Heterogeneity of data from dispatch control systemsWell management systems and SCADA
Copyright 2018 Baker Hughes, a GE company, LLC (“BHGE”). All rights reserved.
Current challenges in smart ESP-based well operations management
June 27, 2018 4
• Overwhelming downhole & surface sensor data has become too large and complex to be effectively processed by traditional approaches
• Acquiring, cleansing, processing & analyzing data has to be done in shorter time frames
• Downhole big data analytics requires a range of technologies (big data platforms – Spark; data warehouses and databases; data ingestion, pipeline, integration; virtualization and containerization of workloads; servers, storage and networking infrastructure; analytics model tuning, visualization and reporting applications;
• Current upscaling of online ESP surveillance practices, are largely determined by the size of the training data and the limited computational infrastructure for storing, managing, analyzing and visualizing big datasets
Kafka Cluster
Spark Streaming
Spark Engine
Cassandra
ESP Real-Time Analytics
ESP Offline/ Batch
Analytics
Dis
trib
ute
d D
ata
Acq
uis
itio
n C
lust
er
(co
nn
ect
ing
to
De
vice
s)
ESP Data Source
ESP Data Management
ESP Data & Analytics Service
Cassandra
Copyright 2018 Baker Hughes, a GE company, LLC (“BHGE”). All rights reserved.
Objectives
June 27, 2018 5
• Develop a general purpose system architecture for predicting ESP shut-down focusing on low-cost cloud –based computing platform
• Explore the feasibility of using real-time data collected by downhole sensors as the only source of information about the normal and abnormal operating conditions of a ESP system
• Investigate the potential of using data-driven artificial intelligence and statistical techniques for efficiently extracting and capturing information about the expected patterns of the signals coming from the ESP sensors, controller data, power data, surface data during the normal operating conditions of an ESP (normal operating patterns)
Copyright 2018 Baker Hughes, a GE company, LLC (“BHGE”). All rights reserved.
Methodology: proposed solution framework
June 27, 2018 6
ESP Operation Expertise
Pattern detection, alarm,
model and Prescriptive
recommendation
Data Access
• Right data is transmitted in the right time - reliably
• Data is available to people, tools, model calibrator where it can be use• No guess work on data -
Fuzzy logic based analysis for finding whether data is increasing, decreasing or constant
• Easy to recognize ESPs with issues – exception based surveillance (Exceptions are cross verified with the Prescriptive rules)
• Once the analyzed data and rules/pattern matches, alarms are triggered based on the prescription
• Subject Matter Expert experienced on operating ESPs in different application
• Support model institutional learning and continuous model calibration
Copyright 2018 Baker Hughes, a GE company, LLC (“BHGE”). All rights reserved.
Methodology: proposed deployment service platform
June 27, 2018 7
Copyright 2018 Baker Hughes, a GE company, LLC (“BHGE”). All rights reserved.
Methodology: proposed system architecture
June 27, 2018 8
CSV-Time Series
Shutdown Prediction Engine
(Rule based)&
(ML Apache Spark)
Real Time Data
History Data
DTS Datamining
DSS Datamining
DNP3
SCPI
Demand Trac
<PRODML/>1.2, 1.3, 2.0(DAS)
RTAWS Engine
Training Data from Gateway
Gateway UI
History Data
Configuration parameters
Visual alerts
Cluster
Real Time Data
Data Acquisition Data Streaming (RT/HA) and Storage Data Computation Data Visualization
Copyright 2018 Baker Hughes, a GE company, LLC (“BHGE”). All rights reserved.
Methodology: proposed spark computational cluster architecture
June 27, 2018 9
Spark Driver
Cassandra NameNode
Spark Worker
Cassandra DataNode
Spark Worker
Cassandra DataNode
Spark Worker
Cassandra DataNode
Master VM
Slave VM-6Slave VM-1 Slave VM-2
Copyright 2018 Baker Hughes, a GE company, LLC (“BHGE”). All rights reserved.
Methodology: ESP shutdown prediction model
June 27, 2018 10
Identify Problem Domain
ESP Data Selection ESP Investigate
Data Set
Normal ESP Conditions
ESP Shutdown Conditions
Classification
Clustering Algorithm
Low
Pattern Recognition Algorithm for Anomaly
Detection
Medium High
ESP Shutdown Prediction
Copyright 2018 Baker Hughes, a GE company, LLC (“BHGE”). All rights reserved.
Applications – case studies
June 27, 2018 11
Dataset for the case study
Normal Conditions
Testing set
Normal Conditions
Shutdown Conditions
02000 4000 6000 8000
Training set
Shutdown Conditions
0 100 200 400 600300 500
testing
training
data set
0 2000 4000 6000 8000 10000 12000
Distribution of dataset
TOTAL NUMBER OF WELLS ~350
Total number of shutdown 3,984
Maximum shutdowns per month 597
Minimum shutdowns per month 111
Average shutdowns per month 332
Total SCADA shutdowns 1,751
Total input power quality 1,516
Total operations/downhole 635
Total external signal shutdown 46
Total internal drive component 36
Copyright 2018 Baker Hughes, a GE company, LLC (“BHGE”). All rights reserved.
Important operating parameters that may be Inferred include ...
• Seal section oil expansion
• Driveline torque
• Thrust bearing loading
• Motor terminal voltage
• Motor terminal current
• Motor load factor
• Pump operating range
• Stator temperature
• Seal oil temperature
Important operating parameters that is typically monitored include ...
• Frequency
• Pump intake pressure and temperature
• Pump discharge pressure and temperature
• Internal motor operating temperature
• Flow rate
• Surface current and voltage
• Voltage imbalance
• Tubing pressure and casing pressure
• Current leakage
• Unit vibration
June 27, 2018 12
Applications – case studies
Copyright 2018 Baker Hughes, a GE company, LLC (“BHGE”). All rights reserved.
Applications – case studies
June 27, 2018 13
0
20
40
60
80
100
120
140
160
Total
Trip (Non-ESP trip) Underload Low Intake Pressure
High Motor Temp. High Intake Temp. High Discharge Pressure
Motor Stall Overload High Downstream Pressure
Troubleshooting Leak Low Speed Trip
Over Current
OPERATIONAL/DOWNHOLE TOTAL
Trip (non-ESP trip) 151
Underload 148
Low intake pressure 136
High motor temp. 57
High intake temp. 41
High discharge pressure 38
Motor stall 33
Overload 12
High downstream pressure 6
Troubleshooting 5
Leak 1
Low speed trip 6
Over current 1
Copyright 2018 Baker Hughes, a GE company, LLC (“BHGE”). All rights reserved.
Applications – case studies
June 27, 2018 14
44.0%
38.1%
15.9%
0.9%1.2%
Shutdown Category Breakdown
SCADA Shutdown
Input Power Quality
Operations/DownHole Issues
Internal Drive Component Issues
External Signal Intervention
197
5190 110 130 110
242 223
150
207
107134
4116
332 320343
6842 47
21
101 118
6746 41 54 53
114
26 37 47 3073 64 50
3 2 2 0 3 2 5 8 1 7 0 33 1 4 2 7 1 6 3 7 6 2 40
100
200
300
400
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Monthly Num. of Shutdowns per Category
SCADA Shutdown Input Power Quality Operations/DownHole Issues Internal Drive Component Issues External Signal Intervention
Copyright 2018 Baker Hughes, a GE company, LLC (“BHGE”). All rights reserved.
Applications – case studies
June 27, 2018 15
0
5
10
15
20
25
30
35
40
45
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Monthly number of operations/downhole issues per category
Trip (Non-ESP trip) Underload Low Intake Pressure High Motor Temp. High Intake Temp. High Discharge Pressure Motor Stall
Copyright 2018 Baker Hughes, a GE company, LLC (“BHGE”). All rights reserved.
Applications – case studies
June 27, 2018 16
151
148
136
57
41
38
3312 65161
Trip (Non-ESP trip)23.78%
Underload23.31%
Low Intake Pressure21.42%
High Motor Temp.8.98%
High Intake Temp.6.46%
High Discharge Pressure5.98%
Motor Stall5.20%
Overload1.89%
High Downstream Pressure0.94%
Troubleshooting0.79% Leak
0.16% Low Speed Trip0.94%
Over Current0.16%
Operations/downhole issues breakdown
Trip (Non-ESP trip)
Underload
Low Intake Pressure
High Motor Temp.
High Intake Temp.
High Discharge Pressure
Motor Stall
Overload
High Downstream Pressure
Troubleshooting
Leak
Low Speed Trip
Over Current
Copyright 2018 Baker Hughes, a GE company, LLC (“BHGE”). All rights reserved.
Conclusions
June 27, 2018 17
• An automated detection and prediction of shutdown has been designed and implemented for real-time Electrical Submersible Pumps (ESP) performance monitoring and diagnostics
• The main novelties are that the proposed approach extracts features from operating conditions based on clustering analysis and predict via data classification
• Test, verify and demonstrate the effectiveness and efficiency of the novel methodology for ESP shutdown prediction on a number of real-life case studies from the field
• Robust performance is guaranteed and versatile to various ESP applications – onshore, offshore, SAGD, etc.
• Future directions – more sophisticated anomaly detection algorithms for online self-learning on cloud-based distributed clusters