Abqaiq Intelligent Energy KPI System Success Story

Transcription

Abqaiq Intelligent Energy KPI System
Success Story
Empowered by Integration Objects
© Copyright 2015, Saudi Aramco. All rights reserved.
Saudi Aramco: Company General Use
Outline
•About Abqaiq Plants
•Business Case
•The Solution
•Keys to Success
•The Results
2/16/2015
Empowered by
www.integrationobjects.com
2
About Saudi Aramco Abqaiq Plants
• Biggest oil processing and crude stabilization facility.
• Main oil processing center for Arabian Extra Light (AXL)
and Arabian Light (AL) crude oils, with a capacity of
more than 7 million bpd.
• 3 main processing operations: oil, NGL and utilities.
• Average energy consumption: ≈ 30 KBTU/Boe.
Boe : Barrel oil equivalent
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Business Case
Challenges
•
•
Energy cost was too high.
The Kingdom needs to maximize the utilization of energy to support economic growth.
•
False warnings about energy overconsumption.
The efforts required to diagnose the issues were costly in time, money and resources.
•
•
Gaps in knowledge and expertise between engineers in analyzing performance deviations.
•
At times the plant was running based on wrong targets as they were not accurately adjusted according to plant mode and states, and crude changes.
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Needs
•
Reduce cost and optimize energy consumption.
•
Identify the root cause of energy overconsumption immediately and accurately.
•
Empower most resources with expert knowledge online.
•
Automatic adjustments of targets according to plant conditions and operating modes (Dynamic Targeting).
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The Solution
Engineering Station
Manager
Supervisor
Abqaiq Intelligent Energy KPI System
Data Validation
Dynamic Targets
Root Cause Analysis
Expert Rules
Deviation Detection
Workflows
Complex Events
One Root Cause
KPI Calculations
Data Historian
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Objectives
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Monitor the entire plant energy consumption from site‐wide down to the equipment level.
Automatically detect the plant states and operating modes and accordingly adjust targets.
Early identification of the energy performance gaps.
Isolate the root cause behind the performance gaps.
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Scope of Work
Areas
Oil
(AL, AXL, GC)
NGL
Energy Index
 4 KPIs and more than 300 sub‐KPIs.
 5 complex root cause analysis models with drilldown capabilities and complex events detection.
 5 interactive Web dashboards.
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Abqaiq Plants KPIs Hierarchy
AL
AL
Finfans
Production Unit
AXL
AL
APS 6
AL UA
Spheroids
Stab 7
...
AXL UA
Spheroids
AL
APS 7
Equipment
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110-E7A
110-GM30
110-GM30
...
...
Deethanization
SNGL II
BlowDown
SNGL III
NNGL
Stab 9
TEG
Refrigeration
Compression A
Stab 2
110-E-7
S NGL I
GC UA
Spheroids
AXL
ATF 1
Unit
Stab 12
NGL
GC
Stabs
AXL
Stabs
AL
APS 8
AL Stabs
GC
...
Stab 8
Stab 10
Stab 11
Compression B
Stab 19
Stripping
120-E301A/B
120-E301C/D
120-GT301A
120-GT301B
499- E-18
110-E-2
Stab 2
stm inj
110-E2A
110-E-23
110-E-26
110-E-24
110-E-27
HRSG-B
I.S. coolers
(499-E-19)
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A.S. coolers
(499-E-20)
499-GM-702
A/B
FG to CGT
KT 701
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Approach
Historical Data
1
DB Cleaning
Real‐time Data
2
3
Data Exploration
Modeling
4
Calculations
5
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Rules & Complex Events
Root Cause Analysis
Online
Offline
Integration Object’s KNet
Analytics
• KPIs Calculations
• Performance Gaps Detection & Prediction
• Abnormal Conditions Diagnosis
• Operations Advisory
Integration Object’s KnowledgeNet
Data models based on historical data
knowledge
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Keys to Success
1
2
Dynamic Targets for Reliable and Accurate Energy Consumption Warnings.
Automated Root Cause Analysis for Immediate Resolution.
3
Online Visualization for Operations Performance Insights.
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Dynamic Targeting
• Dynamic targets takes into account operating modes, valves line up, and
equipment availability.
Historical Data Analysis
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Online Dynamic Target Calculation
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Cleaning Data
Detect bad text and outliers
3 years of historical
data
Clustering
Determines the data clusters for each operating mode, process scenario, etc.
Classification
Using decision tree to build an expert rule that identifies each cluster.
Modeling
For each cluster, model the energy KPI as a function of the inputs using a regression algorithm (linear, rational, polynomial).
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1
Clustering ‐ KMeans
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Classification – Decision Tree
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3
Rational Regression Model
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Linear Regression Model
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Online Dynamic
Target Calculation
Equipment Availability
Based on key parameters, detect if the asset is in process service or not. Operating Mode Detection
Determine the current operating mode (deep/shallow stabilization).
Process scenarios
Identify the process scenario based on the valves line up for swingable stabilizers.
Dynamic Target Calculation
The identified model based on historical data is deployed online.
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3
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Top 3 Keys to Success
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2
3
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RCA ‐ Drilldown Capabilities
Equipment Level
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Unit Level
Plant Unit Level
Overall Plant Level
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RCA ‐ Complex Events Detection
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Top 3 Keys to Success
1
2
3
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Main Dashboard
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Main Dashboard
Highlight the energy savings and costs with multicolor tiles. Summary table for energy KPIs statuses, values, targets, etc.
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Click here to drilldown.
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Overview Dashboard
When you click on the Index, the corresponding charts will be displayed.
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Energy Overconsumption Detection
Notification message ‐
Quick access to the equipment responsible.
The cause of the energy overconsumption.
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Drilldown to Production Units
YTD Energy
KPI bars 2013 vs. 2014.
Current KPI
value.
Production Units sub KPIs values, targets, statuses, etc.
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The Results
Energy savings were estimated to be in the tens of millions of dollars.
Increased visibility of the operations performance from the operator
level to senior management for faster and informed decisions.
The problem‐to‐resolution cycle time was significantly reduced.
Operations knowledge was retained and shared.
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Abqaiq Intelligent Energy KPI System Success Story

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