1. IBM Global Business Services
Petroleum Industry
Making the Intelligent
Oil Field a Reality
Executive Brief

2. Operations leadership envision a smarter way of running their business where
they are able to visualize their entire operation, call and retrieve data about
production effortlessly and seamlessly, and collaborate across the entire enter-
prise to decrease production costs while increasing recovery. They also have heard
many times that achieving their vision is just not possible: there are too many
systems, no consistency of data, and the technology needed to connect information
on assets and performance would be too costly and too difficult to achieve.
A new approach is needed that makes the vision achievable and affordable. With
this new approach, the business can be transformed into an environment where
production data and documents are readily available, delivered in consistent for-
mats despite their ties to diverse assets, sophisticated alerts are triggered based
on business rules, and it is all delivered in an interface that visualizes the entire
operation via intuitive hierarchies. This bold new vision is becoming a reality
enabled by new technologies and approaches from IBM.

3. I. An Impossible Dream? The Vision for the Intelligent Oil Field
Can Be Realized
Leaders in the oil business always want to perform better. Improving production
and yields, monitoring and improving business operations, improving quality, and
ensuring worker and environmental safety are all vital business objectives. The
largest and most powerful levers for achieving these objectives are in the field, where
the most valuable assets – experienced people, equipment, and producing assets –
must be continually optimized to deliver maximum value. In this day and age, leaders
are advancing their game beyond new machinery and better sites. The areas of
drilling and completion, reservoir and production management, and operations/
maintenance now all benefit from technology and data-driven functions, but few
are connected to show the big picture. Leaders have a vision for producing assets
more intelligently whereby they boost production, quality, and safety by seeing the
big picture and being “omniscient” about their operations, using data throughout the
production processes to drive how they make decisions and measure performance.
Call this vision the Intelligent Oil Field (or IOF). In the Intelligent Oil Field, oil workers
and engineers are able to visualize the entirety of their operations by being able
to instantly access key data about their assets, measurements, and documents in
consistent and intuitive formats. This information, in turn, provides them a consistent,
comprehensive and unified view by which they can make decisions. Beyond merely
referencing the state of the oil field, the environment is proactive and action-oriented
by means of alerts and events triggered or activated by key metrics and the ability
to act on these events as they occur. They are able to collaborate with experts
on problems and solutions effortlessly. The result is smarter and more effective
management of resources, ultimately improving production, reducing errors, and
compressing decision times.
The Intelligent Oil Field, at its heart, is an extension of business capabilities where
leadership can manage the value chain and not just equipment. New possibilities
present themselves across large production environments that have multiple
assets and multiple geographies. The Intelligent Oil Field enables production
optimization across sites, supports inter/intra-site regulatory compliance, and
enables collaborative manufacturing performance management. It creates the ability
to effectively maintain equipment relationships, track events and conditions across
multiple sites. It enables assets to operate closer to the optimum by improving
business processes, including real-time visibility and process collaboration, event
management in business real-time, and predictive analysis and optimization.
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4. Figure 1
Technology practitioners need their desires answered as well. In most operations,
the technology puzzle is too difficult. To start, like most long lived assets, each
field, acquired and produced over time and geographies, have their own standards
and methods for naming and delivering data. There are many incompatible and
inconsistent systems, from site-level production and asset systems to broad
enterprise systems, such as ERP and sourcing systems. On-boarding new facilities
or functionality is difficult, time consuming and costly. There is often weak process
and systems interoperability. There is no consistent data foundation, ownership, or
structure. The main barriers include:
• Numerous different applications deployed across the enterprise at both the busi-
ness and production levels to manage and record operations performance.
• Each application instance has its own unique reference and data model, creating
a lag time before data is available for comprehensive analysis.
• Process tag information and its context to equipment is not conveyed in real
time, thus placing a heavy reliance on engineering interpretation. Processes are
embedded within applications, equipment and employees.
• Cross location and cross work process transactions and events are not captured
in the context of equipment configurations or production relevant events.
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5. • Operational views are incomplete; overall analysis is sub-optimal and localized.
Integrating additional facilities or introducing new functionality is difficult, time con-
suming and costly.
• Process events, alerts cannot be distributed and handled across the enterprise to
initiate business processes or personnel collaboration or attention.
Traditionally, these barriers proved to be too much to overcome, or, at least, too
expensive or expansive to tackle. This has put engineering and IT groups at
unwitting, frustrating loggerheads, where the two can never come together to move
effectively towards the vision.
Making it real: A new approach makes the Intelligent Oil Field vision achievable
With a new approach to technology enablement, the vision for an Intelligent Oil
Field can now be achieved. This new IBM Integrated Information Framework (IIF)
utilizes Reference Semantic Model, Global Industry Standards, and a Service
Oriented Architecture (SOA) approach that enables data and connectivity without
rip-n’-replace updates of existing systems. It is an approach that uses the existing
technology and infrastructure base, regardless of its disparity, platform layout, or age,
and brings it forward as a foundation for the future.
By using this new approach, the asset owner is free to envision a new, more
productive oil field where they have visibility, collaboration, event management, and
better measurement. For IT, the new approach makes realizing the vision achievable
at the right cost, with lower deployment risk, faster delivery times, and flexibility for
the future to add new application function without re-tooling.
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6. Figure 2
Making it real: Partnership between engineering and IT is key
In the following sections, we will discuss the functions, capabilities and benefits of
the Intelligent Oil Field for both the end-user and the IT delivery team. For the end-
user or oil field engineer, the vision is best described by how it changes day-to-day
oil field operations, for example, describing the new ways in which engineers will
access information or new metrics to help improve performance and make business
decisions. For the IT practitioner, it is valuable to describe the underlying technology
infrastructure and strategy, describing how the different data and technology
components work together, and even expressing this in a reference architecture that
represents an idealized solution.
While it is important for us to describe the Intelligent Oil Field in two distinct
languages, one business-focused, and one IT-focused, it also important to describe
how these two views must work together. Because success in this endeavor requires
understanding and cooperation between both groups, it becomes vital that the oil
operations leadership and IT come together in partnership, mutually advocating for
its creation and forming a coalition to champion its delivery. Due to the large scope
and the equally impressive opportunity for benefits, companies who embark on this
journey must be prepared to bring different constituent types together to embrace
the cause.
This partnership will require different people, with different expertise, to examine,
analyze and plan for this endeavor using different mindsets: operational, behavioral,
technological, financial, etc. It may also require strong leadership to facilitate and
mediate the exchange of ideas and plans between the invested stakeholders in this
transformation. In many ways, the ability to work together becomes as important as
the new technology approach itself.
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7. II. New IOF Capabilities Enable Operations to
Work Better
The best way to understand the new vision of the Intelligent Oil Field is
to understand what new information and new actions the oil practitioner
can see and make. Sometimes this is describing the new process by
which people work, other times describing software or tool functionality,
and other times describing new entities such as alerts or reports that
are created. We will attempt to describe all of these while putting a
human face to how a workday may change in context of the new vision.
The users in our oil field operation are not limited to just the analysts
or crew. We include the production supervisor, platform engineers,
engineering technical support, quality engineers, remote expert
collaboration groups, and business users outside of the oil operations,
Figure 3 such as the COO, finance and supply chain practitioners.
Each of these users is empowered through computer interfaces that provide new
views to information about the oil field and the oil field assets previously unavailable.
This information is gathered and presented in new ways, in right-time, and converted
into analysis and intelligence enabling the users to make better decisions and take
better action in their daily operations. The new functionality can be described in three
top-level categories:
Real-time visibility and process collaboration: Provide a single source access
point for users to manage all applications, services, and functions across the
enterprise using an intuitive, hierarchal visual interface. This tool and its underlying
technology enable the user to:
• Create situational awareness and understanding to make better and faster deci-
sions on factors such as production efficiency, drilling efficiency, or overall cost
control
• Obtain a single point of information access for the entire well platform or field
• Be able to provide a defined synoptic or graphic for all aspects of operation associ-
ated with an area
• Be able to drill down to lower levels of an aggregated area to look at measurement
details
• Create and modify graphical displays to best fit business needs
• Seamlessly share data and collaborate with other knowledge workers, including
technical resources, management, expert advisors, and supply chain practitioners.
• Operate in this mode in well equipped business control rooms optimally designed
to support these functions
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8. Sophisticated alerts and event management: A sophisticated system of gathering
data real-time, applying complex business rules and predictive models, and
executing business actions in the forms of alerts and system events to consuming
users, services or applications. This includes:
• Driving business services based on problems or opportunities discovered in real time
• Enabling alert mechanisms for all users based on present profiles and deviations
from trend norms
• Enabling the assignment of priorities to the alerts by the user
• Sending of alerts and alarms to notify people or applications of specific events
• Providing animations of associated graphical displays to better indicate and illus-
trate the alarm occurrence
• Providing a means to add comments to the alert and to acknowledge the alert
within the system
• Providing the ability to capture and archive alerts and events for future modeling
and occurrences
Measurement, predictive analysis, and optimization: The ability to create
business-driven KPIs and performance metrics that enable measurement, reporting,
forecasting, operational planning, and decision-making on making improvements to
the operation. Features include:
• Providing a graphical means to generate, view and manipulate key performance
indicators
• Enabling the definition of inputs to be real-time process measurements or derived
calculations in emitting systems such as historians (i.e., a system that collects,
contextualizes, analyzes and stores information as a snapshot for future analysis,
such as trend identification or model building)
• Being able to specify many property parameters, such as frequency of calculation
• Perform data validation and data smoothing on input data to KPIs
• Being able to write the production, asset, and quality KPI’s back to a historian
• Be able to specify the input of pseudo-static data (i.e., benchmarks provided by
equipment manufacturers) for comparison to the calculated KPI
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9. Envisioning the Intelligent Oil Field:
A Day in the Life Transformed
We can describe the new vision by describing how an engineer’s
or production supervisor’s workday may change. In a given
day, production supervisors may have to perform well tests
and monitoring, manage rotating equipment, manage reservoir
performance, or monitor down-hole equipment. Their ability to gather
information, act on events, and make decisions often determines
how quickly breakdowns are fixed, how accurately equipment is
calibrated, how well the oil field performs, and how quickly downtime
is reversed into profitable production time.
In many current environments, these functions are executed sub-
Figure 4
optimally. The varying assets in the field may all collect data in different formats
and supply it at different cycles and on different systems. Rectifying or comparing
data across different areas may require manual calculations done by hand or in
spreadsheets. A well may have performed poorly for hours or even days as the
team identified the problem in very delayed manual reports. Alerts and event
management is handled by crude alarms and slow voice alerts by people near
the equipment. Getting help or accessing expert knowledge can be slow and
difficult, and getting the data into the hands of supply chain managers who need
production data to build their plans and forecasts has a latency that drives manual
data entry, delays, inaccuracy, and rework.
In summary, the current environment is slow and unresponsive. And unresponsive
means that production is lower, sub optimal business decisions are made, and the
enterprise as a whole suffers from lost opportunity.
In the new environment, the production supervisor is able to view and take action
from the business control room, receiving accurate information quickly, and being
to able to spend more time making better decisions with the facts. Shown below
is a “day in the life” of Clint, a production supervisor, who monitors his well
operations, deals with emergencies, and excels at continually improving his
production operations.
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10. 8:30 AM - Clint starts his shift with a well performance management scan
Clint, a production supervisor, begins his shift by logging into his IOF interface that
has all of his well production equipment and separator train connectivity visualized
using a graphical, hierarchal display.
The system displays key metrics across the equipment he is in charge of, acting
as single point of access or overview for the well platform and field. Through this
interface he is able to monitor daily production and injection well performance and
Figure 5
compare that to planned production and oil/gas ratios.
In addition to making sure the operation continues to run smoothly, he also plans
to gather active well data and well history with the intent of identifying some
opportunities for performance improvement and to develop an optimization plan.
He initiates a well monitoring visual and scans attributes across all of his wells,
including production manifold/separator operation; well / riser test processes; well
bore down hole pressures, virtual flow metering, choke and wing valve telemetry;
and indicated gas-oil ratio properties measured versus aggregate gas-oil ratios
predicted by the well three phase virtual flow meters.
9:00 AM – Clint requests a fix
During the well performance monitor scan, the system checks each well’s valve
opening versus various pressure sensors down hole and across equipment. The
scan detects a well pressure profile not in compliance with the flow throttling position
to achieve an indicated flow of oil/gas/water.
A well retest is recommended and Clint initiates this through the platforms control
system. Analysis of the well test occurs automatically and in real-time immediately
Figure 6 as it completes. Results of the analysis are presented visually in the form of several
graphs and which are displayed and distributed to subscribing systems and people
on and off platform.
Well 14’s test indicates a distinct change in supplying reservoir characteristics. And
the IOF automatically sends a trouble report to the field engineer with detailed
metrics and documents via e-mail and SMS (the field engineer’s preference) which
he receives on his iPhone. The Field engineer makes it his priority to check the
well’s testing history. Based on the finding, he uses the design interface to tweak the
performance measurements and alert criteria, for the well and applies the model to
other wells adjacent in the reservoir. Based on this the field engineer collaborates
with others to change the injection well strategy.
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11. 10:30 AM – Multiple alerts! Equipment not performing!
Unexpectedly, an event alert triggers on the “A” separator train’s Turbo-Compressor
set, popping up on Clint’s overview of platform equipment, showing that the
compressor’s Polytropic Efficiency has trended beyond an acceptable “rate of
decrease” from vendor specifications. The alerts are based on a sophisticated
pattern of performance deviations from trend norms based on the compressors’
vendor specification. A summary of historic measurements for the compressor is
brought on a drill down graphic confirming the compressor been steadily slipping
Figure 7 away from standard performance.
Clint remains calm knowing that this information was automatically posted to a
company “Compressor” collaboration group and the compressor’s vendor service
group for analysis on the occurrence of the event detection.
Clint brings up longer range trends of the compressor’s operating data for the
imminent collaboration session. Also, he checks the production schedule for
possible outage periods that will minimally impact production plans.
10:50 AM – Expert consortium helps with a solution
The IOF has automatically sent notifications to a company ‘Compressor”
collaboration team, including a notice to supply chain planning management, field
repair staff, and logistics management.
The system also notifies and assembles an off-site vendor support expert
consortium based on availability. They are dialed in to the problem to discuss
solutions. The remote experts are able to instantly view the field’s compressor
Figure 8 equipment and comparable compressors running at other platforms through the
same graphical interface that Clint sees, as well as the documentation and notes
that Clint has added/recorded in his analysis.
The team decides on a likely solution for the problem. The vendor support expert
feels sure that the compressor can safely operate until the next maintenance
window but will require a replacement of certain parts beyond simple inspection
maintenance. Clint is kept in the teams deliberations in real time.
Detailed instructions and maintenance plans are sent via email to a field engineer
tasks which are immediately reprioritized to get the compressor fixed and back
online at vendor specifications.
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12. 3:00 PM - Production delay averted, Clint goes back to work
The compressor problem solution plan is resolved by the time Client finishes a late
lunch. Leveraging an expert team and sensing a complicated but deserved analysis
of performance might well have been over looked or brought to others attentions
for many hours or even days. The IOF has improved the team identification of the
problem side stepping reliance on manual reports.
Clint logs the problem into the knowledge system log, with a detailed account of the
Figure 9 situation, the triggers that were implemented, and the team’s solution, so that next
time the problem will be better proceduralized and added to the predictive modeling
alert system for Event Early Warning.
Using the interface, he graphically edits the rules for this event and adds a new
expert to the event subscription list. He also “publishes” his findings within the
system to share with other production supervisors at shift or crew turn over.
Based on the event response a maintenance work order was generated in the
MRO system.
With the compressor and separator train event resolved, Clint turns back to platform
surveillance and searching for improvement opportunities.
3:30 PM – Stan updates supply chain forecasts
Back on shore, Stan the supply chain analyst receives automatic updates on the
performance of the field, the status of the platform, and the Event signaling the
decline in compressor performance that happened.
Stan is able to update his supply and demand curves automatically in his ERP
interface based on the information he received. The company’s production level
reports and forecasts are automatically updated to supply chain, finance, economic
models, and logistics. In the past, he may not have known about the possibility of a
Figure 10
compressor failure until the window to acquire spot cargo’s to cover production short
fall was closed. Now the company is making accurate reports and forecasts, and
is able to nimbly respond to inventory demands, logistics planning, and anticipate
equipment failure.
Stan e-mails his finished forecasts to the COO’s staff office. Today, the Intelligent
Oil Field system has made many people’s day more effective, from a production
supervisor in the field all the way up the executive office.
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13. What makes a good collaboration
environment?
Clint’s story only shows a few of the
many collaboration features within
the Intelligent Oil Field. A more
detailed view into his control room
illustrates some of the breadth of the
Intelligent Oil Field environment.
Figure 11
This story illustrates how just a few of the features in one specific area of oil
production can deliver big improvements. The Intelligent Oil Field, though, spans
across many different phases and areas of oil production, and has much more
functionality than described above. Some of the other areas of impact include:
Supported Well Well Performance and Well Performance Conditioned Based
Measurement Functionality Equipment Monitoring IO Applications Monitoring - Rotating
Equipment
• Down Hole pressure and temperature • Production wells • Well Test Orchestration • Centrifugal
transmitters • Injection wells (gas and • Riser Test Orchestration Compressors
• Pressure and temperature transmitters water) • Steady State Analysis of • Gas turbine for Turbo
before and after all major shutdown or well • Test and production Production Operations Compressor
element devices master, wing, choke valves. separators • Manifold operations and • API pumps
• Differential pressure transmitter reading • Production loops/branches optimization • Gear boxes
of bore multi-phase flow measurement (flow line, trunk lines and • Reservoir parameter inference • Lube oil
device to the manifold and test separator, risers) and Surveillance • Seal gas system
• Annulus pressure and temperature • Injection lines • Field and Well productivity • Scrubbers and Air
transmitters • Water injection pumps calculations Coolers
• Wet gas meter off the primary and test • Methanol pumps • Lower well completion
separators • Methanol storage tanks integrity
• Water fraction meter off the primary and • Gas train elements (dryers, • Erosion monitoring
test separators compressors, turbines, • KPI calculations
• Oil flow meter primary and test separators metering) • EBOD - Equivalent Barrels of
• MEG flow, pressure and temperature if a • Oil elements including Oil per Day
gas well metering to pipelines • Synoptic views and Reporting
• Acoustic sand detector
• Erosion Probe
The benefits of the Intelligent Oil Field described in business terms become readily
apparent: more information, better access to intelligence, better business decisions,
more equipment uptime, improved oil production, and a more nimble enterprise.
With this vision, business and operations leadership bring a powerful story to the
table and can likely make a compelling case for why they should begin the journey
towards achieving it.
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14. III. Building the IOF: New Approaches for IT
Achieving the Intelligent Oil Field vision technologically requires some
new thinking and some innovative techniques. At first blush, meeting the
challenge may seem prohibitively expensive. Even seasoned IT leadership
could look over the vast array of equipment, sensors, databases,
applications and systems and see how their different data definitions,
lack of integration, unruly governance, and disparity in technologies could
never come together.
The idea of “integration” may be the most deceptively problematic here. In
most instances of integration, we might think of how we need to transform
all of the existing systems and data sources as they have been deployed
Figure 12 through the oil operation’s long history. This would include standardizing
or reinventing application databases, creating extensive middle tiers and
translators, or needing to codify hundreds of different data sets. Because of this,
we must rethink about how we achieve “integration” in a way that doesn’t require a
wholesale and expensive transformation of our existing technological environment,
but instead leverages that chaotic environment to achieve our desired benefits.
With this in mind, we highlight three very important aspects of the Integrated
Information Framework (IIF) that achieves integration in a way that looks forward,
not towards changing the past. These concepts are the reference semantic model,
global standards, and service oriented architecture (SOA) approach. Two additional
concepts complete the technological basis for the Intelligent Oil Field. These are
more application-focused, creating the interface and functionality the users interact
with to perform analysis and take action. These are visualization and analytical
toolset and configurable event rules engine. All of these concepts are popularly
discussed in the practices of business intelligence and master data management,
and the understanding of these broader practice areas certainly help us understand
what is needed for the Intelligent Oil Field solution.
Following are the five major concepts and functions that support the Intelligent
Oil Field. There are many other foundational aspects that are employed when
actually enabling this solution (things such as database design, technology design,
infrastructure, or data quality), but these five represent the distinguishing factors our
new technology vision.
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15. Reference Semantic Model (RSM)
The RSM is one of the distinguishing technology components that makes the IOF
possible. Because data is kept in different formats across the assets and systems,
it is necessary to create a universal translation device that enables the data to be
captured “apples-to-apples” without actually modifying how the source machine or
system is designed to deliver it. The RSM repository maintains meta data (instance
data) of enterprise components, providing a consistent data and object naming
service. We can think of it as technology neutral lingua franca or Rosetta Stone to
reference the framework plant model based on recognized global standards. The RSM
ontology distinguishes itself from a traditional ETL type tool in some interesting ways:
• It is a federated access to data, not a “replication” meaning that data remains in
,
emitting systems avoiding expensive replication in data warehouses.
• The RSM is not a data model and does not constrain the way applications
implement the information contained within the model. The RSM facilitates the
exchange of information.
• The RSM provides multiple enterprise hierarchies that models process equipment,
measurements, and document connectivity for visualization and processing, and
thus provides role based information that traverses different capabilities.
Global standards
Central to the RSM’s ability to translate data is the use of global standards. Using
global standards, as opposed to using a proprietary set, speeds the creation and
upkeep of the RSM across the hundreds of diverse data sources. By using global
standards, we are able to quickly bring in the translation criteria by placing the
burden of establishing that among the many equipment manufacturers or software
vendors, with the hopes that custom systems that don’t prescribe to one of the
global standards will be few and far between. In many organizations it may be
tempting to eschew global standards under the belief that the environment is too
specific and custom, and that it would be a hardship to change internal proprietary
standards towards global ones. It is our belief that it ultimately makes things easier
to move to global standards, even if some of the changes are uncomfortable in the
short term.
The mix of standards may be applied to best represent the varied aspects of oil
operations. For example, a high quality solution may use OPC & WITSML/PRODML
for information mapped to the RSM ontology, Measurement Values functions,
and classes; S88/S95, ISO 15926, IEEE 61970/68 for asset and physical hierarchy
representation; and ISO 15926, & Mimosa for asset life cycle management.
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16. Smart SOA
A smart SOA approach is an architectural strategy that enables new functionality to be
rapidly deployed without a “rip n’ replace” approach to old systems. It creates a win-
win for technology and business stakeholders alike: new systems and capabilities can
be enabled while technology investments from the past are leveraged forward. SOA
makes the Intelligent Oil Field obtainable; without it the premise of integrating so many
disparate assets and systems becomes impossibly difficult.
In a SOA model, discreet bundles of software functionality are componentized
and delivered to other functions and systems. This enables different applications
to use common parts, and in turn, new applications can be built by assembling
these reusable components. This enables companies to build new technological
capabilities more quickly, change their operations more rapidly, and better preserve
the existing value of current systems.
The fundamental building blocks for SOA are services. Services are pieces of
application functionality that represent a repeatable, categorically containable
business task. In the oil field, an example may be “check valve pressure” or
“calculate KPIs for EBD- Equivalent Barrels per Day” .
Services are only built once and maintained in one place with interaction via an
Enterprise Service Bus (ESB). Other applications can access service(s) and
incorporate them into its own functionality. This usage is considered service
orientation. In turn, a service oriented architecture is a technological design
discipline that uses a service orientation to plan, build, manage, and enable systems
and technology.
A smart SOA approach also allows for greater flexibility moving forward. Besides
being faster to deploy, SOA does not require wholesale replacement of existing
systems when it needs improvement. This eliminates the need to constantly start
from scratch when deploying new systems and enables companies to realize the
value and ROI of past investments more quickly. Customization and expansion of
systems is also easier. As new standardized services are developed externally by
software vendors, companies will be able to quickly adopt and ‘snap in’ pre-made,
best-in-class services and quickly gain new functionality, providing greater oil field
operations transformation and business agility.
SOA in terms of this discussion of Intelligent Oil Field mostly relates to how business
and operational intelligence are used in the application we describe of measuring
and acting on oil field data. SOA and the enterprise service bus can be deployed
beyond this application to support other functions. This extensibility is what can help
bring the IOF data into the hands of other business users, such as those using ERP ,
financial, and transactional systems.
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17. Visualization and analytical toolset
While the RSM, global standards, and SOA represent the key technologies that
enable the feasible integration of the oil field data, the visualization and analytical
toolset is the primary interface to the business user, and the only visible, interactive
part of the Intelligent Oil Field platform. It is the user’s portal and metaphor for the
field, with visual, clickable representations of the field’s assets. The user can view the
entirety of his oil field metrics by looking at animated summary icons, each with the
ability to drive down into further level of detail.
Figure 13
The interface also provides customization tools so that the user can “build their own”
oil field, adding new assets, establishing new KPIs, creating business rules, making
comments, and logging information into knowledge bases and historians. Top-level
functions include:
• Visualization / Manufacturing Intelligence
• Process and instrumentation views
• Complex event definition and subscription
• KPI definition
• Operations performance views – Inter/Intra-Plant
• Conditioned based monitoring of process equipment
• Reporting, dash board, and analysis
• Integration and inclusion of extra enterprise businesses & market-facing-units
The interface itself is flexible and extensible. It supports Web 2.0 Visualization. The
very thin client interface itself is unobtrusive from a system resource stand-point, and
can work in nearly environment, including through SAP and SharePoint portals.
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18. Configurable Event Rule Engine
The Configurable Event Rules engine enables business process and web services
initiation acting as a non-human, intelligent agent. The rule engine monitors data and
KPIs based on particular thresholds as well as on sophisticated historical models.
The engine has pre-defined actions it takes when particular criteria is met, such as
launching an alert, notifying different users, provisioning information, or launching
other system activities.
The Configurable Event Rule Engine is completely customizable from the operational
leader, meaning that events and criteria can be defined without IT intervention.
Figure 14
Going deeper: The IOF Reference Architecture
So far, we have only highlighted the “what’s new” aspects of both the user
experience and the enabling technology principles. While this tells a succinct
story, it is important to know that the IOF is not a theoretical vision; there are active
implementations at leading oil and chemical companies and we have tomes of
technical details, designs, data models, technical specifications, and best practices
that describe this solution in executable detail.
One important tool in building the detailed understanding of the IOF platform is the
IOF Reference Architecture. The IOF Reference Architecture provides a generic, but
categorically-complete depiction of the Intelligent Oil Field. This provides a valuable
tool to analyze and plan for the IOF implementations, providing a distinctive check-list
of items that should be evaluated and planned for, ensuring no rock is left unturned.
This view is synoptic, with the underlying detail available under separate cover.
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19. IV. Forging the Partnership: How Business and IT Must Come
Together To Succeed
Both the operational experience and the fulfillment of the information technology are
significant undertakings that will require detailed planning, significant investment,
and dedicated resources. The most important first step is mobilizing the organization
to begin the change, and the most important part of this activity is forming a vital
partnership between operational leadership and technology leadership to shepherd
the project forward.
This partnership must manifest itself in tangible ways and needs to be more
than a tacit agreement. The partnership should have equal representation from
all stakeholders. It is too simple to say there are only two groups at the table, as
in reality leadership from the various geographies, operational groups, and even
representation from the likes of supply chain functions, finance, human resources,
and executive leadership will need to participate. At the center of this partnership is
a value proposition and business case that makes sense for all parties.
While not exhaustive, listed below are some good first steps in ensuring the
partnership is a success:
• Building multi-disciplinary teams
• Understanding “what’s in it for me” for all stakeholders
• Building a joint strategy, blueprint and roadmap
• Developing a robust, ROI-based business case
• Establishing success metrics
• Using a neutral facilitator and external expertise
Change management driving the success partnership
Managing the change within the organization will be a key to success of the
partnership. At the top level, this means visible advocacy from leadership, behavioral
changes, constant communication, and a formal investment in a change program.
Tactically it will require training and mentoring, Nationalization agendas, and formal
knowledge transfer between groups and functional divisions.
Oil industry leadership should understand that this partnership will be challenging
to forge and complicated to manage. For this, special care and investment should
be expended to establish a formal process for maintaining the positive inertia of the
partnership to ensure the greatest chances of success.
19

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V. Conclusion IBM Global Services
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