Rapid Data Collection & Storage - LBCG€¦ · • Utilize AWS to minimize up-front investment and...

Improving Data Collection Efficiency:

Strategies For Quickly Gathering & Prioritizing

Data Points To Gain The Correct Fidelity Of Data

To Do Calculations & Run Models On

Rapid Data Collection & Storage

Results Driven Analytics Techniques

& Optimized Data Management

Infrastructure – Oil & Gas

SEPTEMBER24-25,2019| HOUSTON,TEXAS

#PIWorld ©2019 OSIsoft, LLC

AGENDA Introduction

Layers of Analytics and Efficiency

Data Collection Efficiency;Data and Integration of Modeling Software – Phillips 66

Effective Transfer of well site data in real-timeOperations & Advanced Analytics at the Well Site – Shell

Allocate the right data, to the right teams, at the right time for improved decision making

Advanced Analytics & ML in the Cloud – TransCanada/AWS

Layers of Analytics - Process Operations


Data Collection, Gathering and Processing Efficiency

Build Templates w/Initial Analysis

Expressions w/SMEs

Map Tags & Metadata

- Auto tag creation

- SQL sources

- Table Lookup

Visualize & Evaluate Anomalies

Exception Basis

Tune Anomaly Expressions &

BackfillTrack KPIs & Value

Use Cases

Create PI AF “Digital Twins”:

3 PI Vision Templates

Best Practices in

Integration of Modeling

Software with PI AF

©2019 OSIsoft, LLC

Data Flow

6

High Fidelity Historical Process Data

PI AF Used to perform Plan vs Actual (PvA) comparisons

Historized process data and model

results

RDBMS

Simulation model data

PI Vision used to provide self serve access to contextualized

operational intelligence.

7

Project Approach

Strong SME InvolvementThe Key to PI AF success!Represent sites and make key business decisions:

Hierarchy, nomenclature, UOM, analytics, displaysDefine unit-to-unit differences

Validate solutionTrain end-users

Align on an upfront design – forward looking vs specific project

Use robust processes for communication, testing, documentation

PI AF Expert/Partner collaboration – RoviSys and OSIsoft

PI AF Template-based Solution

• Leveraging the power of PI AF Templates of many flavors:Elements, Analyses, Event Frames

• Many commonalities across unit types

• Unit-to-unit variations captured

• Not just physical assets• Product Streams, Yields, etc.

• Dynamic and centrally managed design

• Standardized design library for deployment

• Adaptable design, can evolve with business needs

• Iterative approach to continuously improve the solution

8

9

• Hierarchy captures physical asset relationship

• Process, calculated, and modeled data side-by-side

• Each PI Tag mapped once at major unit level

• Hierarchy enables use of substitution parameters

• Only 10% process data tags with non-standard naming

• PI AF Table lookups and substitution parameters reduced 90% of manual attribute data source mapping

• Scalable and dynamic for long-term sustainability of solution

• Forward-looking Data Infrastructure• Supports current initiative

• Lays groundwork for future solutions

PI AF Hierarchy Design


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Scalable PI Vision Displays

• “Reports” PI AF Hierarchy branch created to reference critical values for displays

• Consistent for every instance across the enterprise

• All display values are tied to AF Element Template attributes

• PI Vision automatically enables Asset Relative Displays

• Create each screen once per unit type Element Template and it applies to all instances in the AF Hierarchy

• Standardized view of real-time data and KPIs

• No need to manage each person’s “version of the truth”

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RESULTSCHALLENGE SOLUTION

Integrating PI System data with model data and comparing to actuals

Integrate modeling data with the PI System to provide high fidelity, quality, rich dataset for trending, analysis, monitoring, optimization

Improved KPI monitoring, optimization, and model usage resulting in improved economic performance

• PI SDK to load high fidelity data in model• PI RDBMS: interface to bring in modeling data back

into the PI System • PI AF: templatize and standardize process data,

calculations, and analytics• PI Vision: standard KPI, economic, summary displays

• Different tools/spreadsheets with different data available

• Quality and consistency of monitoring varies• Level of effort to maintain

1

• Improved performance

• Data transparency

• Empowerment of SMEs with self serve access to model effectiveness

Best Practices in Integration of Modeling

Software with PI AF

Shell - A business perspectiveof Real-Time Operations and

Advanced Analytics

PI System Center of Excellence

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PI Systems(Super Collective)

ConsumersLegacy

Application

Advanced Analytics

SmartPerform

Engineering Hub

Smart Apps

Smart Foundation

Real-Time Architecture within Shell

Enterprise

Foundation

Platforms

Enterprise

Structure

Visual

Components

Public

ServicesCalculationsEvents Scheduling

Standard

Asset

Structure

SMM

MatLab

Quest

Alteryx

Orchestration

EDWhAlarm

Mgmt

MatLab

T-CAT

PI AFPI AF PI AF

PI AF

SmartConnect

EngineeringHub

PTM

NGT

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Leveraging the PI System at the heart of our Digitalization Roadmap

More integration between our core Digital Oilfield tools (PETEX, EC, etc) and the PI System

Leverage the real-time analytics and automation capabilities from within the PI System

Explore Opportunities for Machine Learning,AI & Advanced Analytics sitting on top of the PI System

Safe and Optimised

production

Improved Productivity Improved Availabilityand

Reliability

Integration with Digital Oilfield Tools• PI System integration with Digital Oilfield

(DoF) tools helped our Production Engineers increase production in 1st year of deployment

• Change Management and focus on improving ways of working is key to make this work!

Case Study: Impact of compressor suction pressures on well performance

GAP model predicted a loss of 500boe/d due to 10 psig increase inseparator pressure

Compressor-turbine system performance was evaluated using process simulation model

Review of Engine wash frequency

Frequent anti-foulant injection incompressor

More integration between our core Digital Oilfield tools (PETEX, EC, etc)and the PI System


Explore Opportunities for Machine Learning, AI & Advanced Analytics sitting on top of the PI System

Real-time Analytics andAutomation Use PI Data Archive and PI AF to start replacing some of the manual processes with automated tools and workflows,

giving engineers and operations teams the data they need in the right way at the right time => need to get data faster out of the PI System

Automation increases productivity and helps Engineers spend more time on translating data into meaningfulinformation and valuable decisions

Significant Productivitygain

More integration between our core Digital Oilfield tools (PETEX, EC, etc)and the PI System



Advanced Analytics, Machine Learning &AI

• Real-time analytics for equipment and operations is providingsignificant bottom line value

• Multiple predictive analytics underway to predict compressors and valvesfailure

• Reduce cost and increase uptime – Compressors trips / failure is one of the top Shell Operating Ventures Bad actors

Valves example:

• Valves maintenance mostly time-based:

• Too late => unscheduled deferment, HSSE risk

• Too early => scheduled deferment and cost

• Moving to condition-based maintenancewill help reduce OPEX and deferment

• There are thousands of valves in Shell,this is where Machine Learning comes in…. More integration

between our core Digital Oilfield tools (PETEX, EC, etc)and the PI System



Asset A

2015 control valve incident

led to $ 6 MM value loss.

There are over 20.000

valves installed, how can

this be prevented in the

future?

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Analytics framework

© Copyright 2017 OSIsoft, LLC#OSIsoftUCosisoft@

Building Analytics capabilities in Shell

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Makingthe most of existing data

Predictive Asset Maintenance

Shell Global Solutions International B.V.

Carbon Capture & Storage

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TransCanada's Journey

to Advanced Analytics

©2019 OSIsoft, LLC

Building a Foundation for Data Analytics

• Starting with the Basics

• Data Culture

• OTAnalytics

• Self Serve, Human Analytics

• Understanding Data as an Asset

• The world’s most valuable resource

• Create “pipelines” to collect, store and utilize that resource

• Take advantage of AF, Vision to create value

• Building Cross-functional Teams

• Subject Matter Experts

• Engineers and Data Scientists


Leveraging our Analytics Foundation

Reliability AnalysisPlatform Measurement Insights &Analytics



TransCanada

Manage and take action on anomalies identified by AF analyses and document findings

Redesign AF using modular approach and develop a custom platform to augment PI Asset Framework

• Modular AF redesign

• Developed web platform to manageanomalies and findings

• Ability to train and retrain statistical models

• Over 100% increase in findings from

AF redesign

• 2018: over 250 corrective actions

taken

• Significantly reduced time to

implement assets• Prioritization of anomalies

• Anomaly Management

• Take advantage of statistical models

• Document Findings

• Improve asset implementation time in AF


RELIABILITY ANALYSIS PLATFORM


TransCanada

Get more value out of our PI System by expanding beyond Compression assets and into Gas Measurement

Create a new AF structure and asset templates for full measurement system.

Improved measurement health andproblem discovery time significantlyreduced.

• Building existing knowledge into analyses

• Custom event frame and finding management

• Adding new measurement assetsinto existing Vision platform

• Able to more readily pinpoint when

problems begin

• Measurement data in PI more easily

consumable by business users

• Centralized equipment diagnostic data

interpretation

• Improve measurementaccuracy

• Provide insights into measurement equipment health

• Take advantage of PI Vision

MEASUREMENT INSIGHTS &ANALYTICS


Evolving to Advanced Analytics withAWS

Gas Day Forecasting

• Challenge

• Objectives

• Navigating the Sea of Data

• Feeding the Machine

• PI Integrator for Business Analytics

• Automating the Demand Forecast

• Consuming the Results

• What else?




Challenge

• Commercial operating decisions are currently made based on daily demand forecast.

• More accurate forecasting provides for optimal use of our assetsand more flexibility for our customers.

• Current method of forecasting does not take full advantage ofhistorical measurement, weather and load demand data.

• Scalable model to support full system load


Objectives

• Fully utilize our data to produce more accurate forecasts by modeling systemperformance against historic conditions.

• Utilize AWS to minimize up-front investment and on-going costs.

• Leverage key technology partnerships to prove out the concept in rapid fire fashion. Fail Fast, Succeed Faster.



Third

Party

Data

Metered

Flow Data

Commercial

Data

Weather

Data

PI Asset

Framework

Navigating the Sea of Data



S3 Machine

LearningKinesis SageMaker

Feeding the Machine

PI Integrator for

Business

Analytics



Automating the Demand Forecast

SageMaker Amazon API

GatewayLambda

Custom Data

Reference



PI Asset

Framework

Custom Data

Reference

Consuming the Results

PI Vision



What else?

Lost and Unaccounted ForGas Gas Measurement VolumeEstimation


Perspectives, Best Practices, & Lessons Learned

• Layers of Analytics Approach• Use tools already available• Enable self-serve analytics & visualization• Asset Framework, Vision

• Data Quality• Poor quality data is a damaged asset

• Bad results reduce confidence

• Feature Engineering• Can be done in AF andhistorized• Increase the value of your asset

• The Value of Experimentation• Data Science is experimental

• Be Agile



Best Practice Data, Infrastructure and Modeling Summary

Build Templates w/Initial Analysis

Expressions w/SMEs

Map Tags & Metadata

- Auto tag creation

- SQL sources

- Table Lookup

Visualize & Evaluate

Anomalies

Exception Basis

Tune Anomaly Expressions &

BackfillTrack KPIs &

Value Use Cases

Create PI AF “Digital Twins”:

3 PI Vision Templates

Modeling

Data

Infrastructure

Best Practice Process Summary


• Build an Analytics Foundation

• Start simple

• Data as an Asset

• Cross-functional teams

• Leverage the Foundation

• Build platforms with tools on hand

• Enable users to create analytics and visualize data

• Evolve with rapid prototyping

• Agile, Proof of Concept projects

• Fail fast, succeed faster

Rapid Data Collection & Storage - LBCG€¦ · • Utilize AWS to minimize up-front investment and...

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