BEYOND DIGITIZATION: THE CONVERGENCE OF BIG DATA,ANALYTICS AND INTELLIGENT SYSTEMS IN OIL & GAS
October 9, 2015

IIC:IND:E:V1.0:PB:20151009

October 9, 2015

Beyond Digitization: The Convergence of Big Data, Analytics, and Intelligent Systems in Oil & Gas

TABLE OF CONTENTS
Table of Contents .................................................................................................................. 1
Summary ............................................................................................................................... 2
The Digital Oilfield – A Decade in the Making ......................................................................... 2
Lagging Data Communications Limits Data Analytics .............................................................. 2
40-Year-Old Technology Still Dominates ................................................................................ 3
Classic Historians Becomes Unfulfilling Solutions ................................................................... 4
The Industrial Internet - More Data, More Benefits ................................................................ 4
Security Implications ............................................................................................................. 5
Asset Management ............................................................................................................... 6
Human Capital Remains Critical to Decision Support .............................................................. 7
The Convergence Vision......................................................................................................... 7
Investment Opportunities for Analytics in Oil and Gas ........................................................... 9Conclusion........................................................................................................................... 10
About the Industrial Internet Consortium ............................................................................ 10

IIC:IND:E:V1.0:pb:20151009

1

Beyond Digitization: The Convergence of Big Data, Analytics, and Intelligent Systems in Oil & Gas

SUMMARY
By 2020, 25 billion assets will be connected1 - working smarter, faster and more efficiently than everbefore. The oil and gas industry is beginning to experience the convergence of big data, analytics andintelligent systems. This movement towards convergence challenges the current silos of disparate
technologies, from legacy SCADA and asset management, to historian and decision support systems.
These challenges are paving the way for new, emerging solutions that drive data and predictive analytics
into the edge, the data center, and the Cloud. The Result: The industry is realizing higher levels of
performance and optimization that can result in outsized return on investment for oil and gas companies
leveraging these new capabilities.
This white paper will highlight how the oil and gas industry has coped with the evolution of technology
from field operations to the data center, exposing the need for the industry to consider dramatic changes
to leverage big data and to advance the use of analytics.
In this paper, we focus on:•••

The limitations of oil and gas asset management, big data, and communications systems;
The evolution of next generation "digitalized" industrial assets;
The convergence vision where enterprise-wide analytics will drive all elements of industrial
operations.

THE DIGITAL OILFIELD – A DECADE IN THE MAKING
Over the past decade, the oil and gas industry has experienced significant shifts in how production
operations have been improved through the application of new, emerging technologies. This drive toward“digitization” has been driven primarily by the proliferation of new software applications, new data
formats, and the availability of massive amounts of real-time data. New analytic-centric technologies are
now revealing operational nuances that were previously captured manually, or not at all. During thisperiod, oil and gas companies have created remote operations centers focused on optimizing operations
based on these analytic insights. However, these centers have principally focused on larger assets, wheresuch investments were cost-effective or practical. To truly achieve the Digital Oilfield or “Digital
Operationalization” with embedded analytics, oil and gas executives must overcome the challenges of theinherent limitations in today’s infrastructure and systems. Next-generation, real-time analytic platforms
require new ways of thinking on how data processing, acquisition infrastructure, and analytics platforms
are: designed, deployed, maintained, and used in a distributed and robust manner.

LAGGING DATA COMMUNICATIONS LIMITS DATA ANALYTICS
In the past, robust data connectivity in plants has been a real challenge to design, deploy, and maintain.
For upstream plants (e.g. offshore rigs or mature fields distributed across large, remote areas), thefundamental telecommunications infrastructure is extremely limited or simply non-existent. Additionally,for downstream plants, such as refineries, the large amount of various metals and other materials cannegatively impact the robustness and reliability of many telecommunications technologies. Historically,1

Gartner Symposium/ITxpo 2014, November 9-13 in Barcelona, Spain

IIC:IND:E:V1.0:PB:20151009

2

Beyond Digitization: The Convergence of Big Data, Analytics, and Intelligent Systems in Oil & Gas
approaches to legacy communications tend to inhibit the advantages associated with real-timeconnectivity. These legacy systems were never built to support high fidelity data streams and analytics.
For example, leveraging new analytics and methods of insight has meant layering different
telecommunications, data processing, and data storage technologies, which in turn has created many silosof data. Accordingly, these silos have only resulted in more complexity in achieving meaningful data
insights, since a considerable amount of the data was unusable (or simply unavailable at the right point
or in the right silo) for more advanced applications and data processing. Communication approaches thatare used by oil and gas companies today include: 3rd party data transfer devices; hard-wired connected
equipment; radio devices + towers for line of site communications; wireless broadband; satellite; as well
as no connectivity to the central system.
Due to the remote location of most oil fields, telecommunications systems were developed and deployedbased primarily on availability and cost, and not necessarily on driving business outcomes. Why? Thisoccurred because, historically, oil and gas companies were simply not data-centric enterprises and largely
did not consider the opportunities, advantages, and impacts of the Industrial Internet (or Industrial
Internet of Things, IIoT) and the consequential demand and growth of data and data processing needs.
Additionally, the industry relied heavily on subject matter expertise (SME) of seasoned operators and theirability to react and communicate immediately and effectively regarding real-time issues. These
approaches are neither scalable nor proactive nor capital efficient.

40-YEAR-OLD TECHNOLOGY STILL DOMINATESSupervisory Control and Data Acquisition (SCADA) systems are commonly used as the central data andcontrol system at plants, leveraging legacy telecommunications platforms facilitating remote terminal
units (RTUs) and programmable logic controllers (PLCs). SCADA is typically deployed in highly distributed
architectures, making it difficult to perform analytics across the entire dataset. Over the years, many
SCADA systems have evolved in capabilities; yet, many still lack in some critical areas that can help enable
oil and gas companies to gain and optimize data insights. Some of these limitations include:

Underlying data structures are typically not set up optimally to handle complex statistical
approaches, critical for recognizing and predicting insights;
 Most SCADA systems have relatively low fidelity of measurement and data storage ranging froma data point every 10 minutes to an hour or longer;
 In some cases, SCADA systems cannot reach the field devices due to communication issues,
resulting in the inability to capture high fidelity data for that specific period of time; In the field, SCADA systems require manual mapping of register values resulting in a large
number of improperly mapped values as well as a large number of values that are never sent
back or received;
 SCADA systems also pose a huge barrier to real-time scientific monitoring of critical assets, given
its current state of data availability and quality.
Established in the 1970’s, Modbus is a popular and widely deployed, standardized communication
protocol, predominately used across many existing PLCs and RTUs. Much like the overarching SCADA
system, Modbus comes with some inherent challenges, such as its Master/Slave architecture, lack ofindexed registered values, limited tracking of devices (only 250), and the limited number of data types.
Newer protocols have been designed to help work around some of the legacy challenges of Modbus, but

IIC:IND:E:V1.0:PB:20151009

3

Beyond Digitization: The Convergence of Big Data, Analytics, and Intelligent Systems in Oil & Gas
their implementation have been limited, usually specific to the vendor that created them, and generally
proprietary.

CLASSIC HISTORIANS BECOMES UNFULFILLING SOLUTIONS
To combat the challenges with many SCADA systems, oil and gas companies have turned to commerciallyavailable historian platforms because they offer better user experience and visualization capabilities.
Additionally, these historian systems ingest cross-functional data into a single centralized location. Whilehistorians have enabled a new level of data analysis for many companies, these systems do have several
limitations, including:Many historian systems are built on proprietary or traditional row-based databases, requiring
the partitioning of data across multiple servers, which in turn can limit complex data analysis
across the entire dataset in an expedient and robust manner.
Some historian systems archive and expunge data frequently, limiting the accessibility ofhistorical raw data for analytics.
Historian systems are dependent on the quality of the data provided by source systems, such as
SCADA and Asset Management platforms; when provided with poor quality, inconsistent data,
most historian systems will merely store that bad data along with good data.

The underlying limitations of this commonly deployed legacy architecture compromise the overallbusiness impact of these applications, even if the analytics capabilities were present. Simply put, theselegacy architectures were not designed or implemented to address the needs of complex analytics andmachine learning.

THE INDUSTRIAL INTERNET - MORE DATA, MORE BENEFITS
The widespread availability of inexpensive sensors has driven unforeseen possibilities in the Industrial
Internet. Modern assets are now IIoT-enabled, shipping with imbedded smart sensors. The popularity of
the Industrial Internet and the solid business outcomes realized by its implementation has pushedcompanies and vendors to retrofit older legacy equipment with inexpensive and robust sensor
technologies. Naturally, the more IIoT-enabled assets deployed and connected to a robust
telecommunications fabric, the more data is generated and available. This gives oil and gas companies,
much like any other industry sector, the potential to perform greater analytic analysis and obtain businessinsights from such data.
One example of these new opportunities lies in remote oil and gas operations. Many remote operations
centers now have rich operation capabilities that extend well beyond mere monitoring to include broad
remote management and onsite reporting capabilities. This in turn allows for much more timely reaction
to problems and opportunities, and enables a more proactive management approach to assets.
To add to this, greater automation of asset performance can be achieved as these assets become smarterand control logic is pushed closer to the asset. New sensors and technologies, such as video analytics andstreaming data analytics, are enabling an increased number of assets in the field to demonstrate thecapability of executing discrete autonomous decisions. Over time, much of the remote management ofassets can be delegated directly to those smart assets that are capable of performing these edge analytics

IIC:IND:E:V1.0:PB:20151009

4

Beyond Digitization: The Convergence of Big Data, Analytics, and Intelligent Systems in Oil & Gas
and autonomous decision-making capabilities, rather than solely relying on the remote operations centers
for the decision execution.

SECURITY IMPLICATIONSIIoT edge devices will increasingly have a profound impact on the demands of security infrastructure, as
the Industrial Internet becomes an integral part of the oil and gas technology ecosystem. From a security
standpoint, IIoT-enabled assets present an expanded attack surface with new vectors of vulnerability
across connected systems and distributed devices. Unlike traditional computing devices, IIoT edge devices
are generally embedded sensors and controllers with fixed functions designed to perform specific tasks.
Security concerns can shut down device-cloud-device analysis and feedback loops. In turn, this canseverely impact scenarios where sub-second responses for control loops or data partitioning are required.
Many of these IIoT-enabled industrial devices and sensors simply were not designed to operate in aconnected ecosystem in a secure, robust manner. Many device designers simply did not anticipate thethreats inherent in a connected future.
Fundamental security controls like device authentication, authorization, and encryption controls are often
missing from IIoT devices. Retrofitting them with appropriate security controls (or replacing them with
devices with these embedded and updatable controls) needs to be part of organizations’ deployment
strategies moving forward. A holistic end-to-end approach to security that spans information technology(IT) and operational technology (OT) is fundamentally required.
As companies look for new ways to gain insights from connected assets, they must ensure they can relyon the integrity of the data to make decisions. Decision-making (especially autonomous decision-making)based upon streaming telemetry from IIoT-enabled equipment will have far reaching positive impacts on
all facets of the organization’s business. However, decision systems must be robust enough to detect andisolate from bad, corrupted, or injected telemetry data and attacks. A cohesive strategy applying a data
assurance program will be critical. Organizations will need to implement data-level security measures andimplement a framework that governs data assurance across the edge infrastructure. In turn, this must
instill a higher level of confidence in data-driven decisions. IoT communication protocols and data formats
such as MQTT, CoAP, DDS, 6LoWPAN, ZigBee, ModBus, WirelessHart, and others offer different security
capabilities and strategies, each having their own data assurance limitations. These limitations will need
to be taken into account as new edge devices and management platforms are architected and deployed.
Additionally, it will become increasingly critical to leverage analytics to proactively address security andintegrity threats across IT and OT systems.
IT and industrial control systems (ICS) also present their own set of security challenges, especially as theseplatforms are increasingly converged. The traditionally long technology lifecycle of ICS can result in issues as
simple as the running of outdated firmware and control software no longer supported by the vendor, or
simply have well known or obvious security or reliability/integrity vulnerabilities. Physical isolation ofthese systems, commonly called “Air Gapping,” can isolate control applications, such as PLC and SCADA
platforms from connected networks. However, this is becoming increasingly difficult to maintain andjustify as organizations embrace increased connectivity and its associated benefits. Attacks can originate
across connected networks and onto an ICS domain by utilizing limitations in legacy networking, data
protocols, and methods of security that simply were not designed for an always-connected environment.
When combined with a typically cautious approach operators take towards patching and updating
IIC:IND:E:V1.0:PB:20151009

5

Beyond Digitization: The Convergence of Big Data, Analytics, and Intelligent Systems in Oil & Gas
systems, many systems can be unpatched or updated for months, or even years, which only increases theavailable attack surface.

ASSET MANAGEMENT
Today, oil and gas companies have the opportunity to implement better asset management capabilities.
In the past, the low levels of data accuracy associated with incorrect or outdated information inhibited acompany’s ability to evolve toward being a “digital operationalization”- focused organization. Some
examples of how existing asset management has limited the advancement of IIoT and analytics include:








A lack of integration between procurement systems and asset management systems requiring
duplicate entry, which typically results in inaccurate or outdated data being stored andaccessed.
Assets frequently encounter problems and require maintenance or replacement, but rarely arethese maintenance processes linked to the asset management system to track all the historicalinteractions for a given well/asset.
Microsoft Excel is still one of the biggest tracking mechanisms in the field. Often, various formsof Excel files are utilized across organizations to obtain critical data on Well Tests, MaintenanceLogs, Failure, Tear Down Reports, and other needs. This valuable information is rarely
centralized and analyzed because of the lack of accessibility within a system. In effect, manyorganizations have islands of Excel-based data, residing (in many cases) on individual users’
computer systems.
Vendor test, performance, and commissioning reports are commonly provided in hardcopy
format. Those reports are rarely uploaded into a central system, and in some cases simply sit ona shelf awaiting later retrieval (if ever). In turn, this makes it difficult to know how current
performance contrasts against commissioned performance and the specifications of the shipped
asset.

Knowledge of the asset gives critical context for engineering models for determining asset performance,state of health, and proactive maintenance needs. Without accurate asset and well profile data, it is very
difficult to recognize, predict, and appropriately act on issues impacting production. This results in millionsof dollars in lost operational performance, misinformed decisions on well productivity resulting inincreased costs, planning and maintenance costs, and more.
Additionally, with the availability of data from IIoT-enabled assets, analytics can be applied to predict andimprove maintenance, enable remote monitoring of equipment, and implement planned (rather thanunexpected) shutdowns. A lack of information about the reliability of a drilling rig, for example, has asignificant impact on cash flow when it results in indecision, or unnecessary risk overcompensation.
Companies need to close the loop across associated processes. Asset maintenance insights need to becommunicated proactively and traceably to multiple parties, while being integrated into the workflowsthat are implemented to ensure operationally effectiveness. For example, spare and underutilized
equipment and spare service parts can be made more readily available through greater sharing of
information in near real-time. Further, inventories of spare assets can be reduced when the status of all
assets and their locations are well known, and the data is accurate when viewed. Asset maintenance

IIC:IND:E:V1.0:PB:20151009

6

Beyond Digitization: The Convergence of Big Data, Analytics, and Intelligent Systems in Oil & Gas
analytics can also affect other processes, influencing outcomes not only for capital assets, but also forscheduling and utilization of these assets and associated human resources.

HUMAN CAPITAL REMAINS CRITICAL TO DECISION SUPPORTWorkflow and decision support systems are not immune to the challenges of elevating operational
systems and processes to garner data analytic insights. Human intelligence and intervention remains ahuge factor in interpreting real-time information, and executing appropriate decisions and actions. These
scenarios can be as critical as identifying the specific root cause of equipment failure, to evaluating andresolving the entire workflow of production failure. Many scenarios include the involvement of multiple
decision stakeholders, as well as process routing and operational procedures, complicating the overall
effectiveness of the analysis. Without well-established decision support and event escalation processes
and systems, many oil and gas companies continue to rely on operator expertise as opposed to systemized
best practices for the treatment of specific events.
In the report2, “The Future of Work in the Oil and Gas Industry,” Accenture argues that the convergenceof data, machines, and people, coupled with analytics powered in part by the emergence of the Cloud,can enable smarter decision-making by people at the edge of the organization. Accenture envisions acontinuous stream of data coming from sensors that, in turn, is analyzed by a central onshore command
center, leveraging the results from offshore (edge) analytics. In the immediate future, intelligent data andcommands will also be fed back to the wells from onsite and offsite analysis, enabling field personnel tomake wiser and more data-driven decisions based on local conditions. Enterprises may even be supplied
near real-time information regarding a specific pump and its maintenance history on a wearable device
that recognizes the equipment near a specific technician, operator, or engineer.
As oil and gas companies continue to digitize work and compete more on data management and analytics
proficiency, this shift will transform the nature of the industry’s workforce. Companies will rely less on the
personal experience of specific people and rely more on science and data understood and shared by all.
Digital literacy skills are becoming increasingly important across the workforce. Such literacy includes anunderstanding of applied analytics, use and development of new software and hardware platforms, andthe ability to effectively use new tools such as live collaboration technologies. According to a benchmark
study3 by Accenture, the oil and gas industry needs to hire 11,900 new data analysts to just meet increased
demand. As work becomes increasingly virtualized and analytics-based, organizations will be able to tap
into new pools of workers. These new pools may include those with experienced professionals either
working part time or retired, experts in different geographic locations, or those simply seeking a greater
work-life balance. Such new pools of human resources could be permanent employees, or a new global
digitally connected workforce of experts available on demand, or some combination of both.

THE CONVERGENCE VISION
The convergence vision is where pervasive analytics will drive all elements of industrial operations. In thisvision, the gap between operations and information technologies (traditionally characterized by different
systems, standards, and manufacturers) is bridged, and the integration of data from sensors and devices
2 Accenture, “The Future of Work in the Oil and Gas Industry” 2015
3

Accenture Digital: “Oil and Gas companies: How Digital is Transforming Work as We Know It” 2015

IIC:IND:E:V1.0:PB:20151009

7

Beyond Digitization: The Convergence of Big Data, Analytics, and Intelligent Systems in Oil & Gas
with control systems (SCADA), middleware (e.g., manufacturing execution systems), and back-end IT
systems (e.g., ERP) is complete. Data from machines can now be captured and integrated into IT systems
for analysis, frequently occurring in real-time, and tiered between distributed edge (on premise) andcentralized layers. This convergence between IT and OT systems will greatly enhance the operational
excellence and the subsequent profitability of oil and gas companies. This new approach will leverage
new edge device and communications capabilities with data quality and assurance, as well as enhanced
analytics-driven control capabilities.
This vision will be underpinned by a foundation of big data technologies, which will be necessary to handle
the volumes of data with real-time and near-time analysis driven by scientific principles. The data
structure necessary to enable complex scientific exploration will be the primary design focus of thesenext-generation architectures. It is important to note that these next generation solutions will drive thepractical elimination of data silos across areas of critical information.
This information includes asset and well profile information, sensor data, well test records, maintenanceand failure reports, and many other sources. Each one of these data sources is critical to recognizing andpredicting real-time performance optimization opportunities.
Oil and gas companies will, in this vision, have the ability to leverage a holistic view of the enterprise, fromfield operations to the data center, and obtain a level of specificity in analysis and operations that will
greatly improve organizational performance.
Emerging new thinking on convergence explores how complex data science and machine learning will be
run across all critical data attributes in real-time. This new thinking includes leveraging derived calculated
sensors, statistical classifiers for recognition and prediction of performance optimization, andmaintenance and failure event analysis at a scale unimaginable given the disparate systems deployedwithin oil and gas today.
Built-in workflow and decision support capabilities will ensure the increasingly efficient and effective
routing and handling based on specific realized events and predictive trends. Such workflow and decision
support capabilities will evolve over time to include both work order and operator involvement, as well
as automated control to continually adapt the systems to their most systems-level optimal settings,
maximizing overall production while reducing cost. These new capabilities will have a significant andmaterial impact on unplanned downtime and overall production performance, while improving resource
utilization, efficiency, and safety.
So, why is convergence happening and why is it important to oil and gas?
Cloud computing, inexpensive sensors, progressive network availability, and big data analytics (centralized
at distributed at the edge) have paved the way for this convergence. In the past, oil and gas companies,
like many other industrial sectors, were able to ignore many of IT enterprise technology and process
trends, relying on technology advancements within the organizations’ current context that wereperceived as “necessary” and cost effective. As these technology trends collide, driven by increased
demand for high fidelity, higher-quality data, an environment has emerged that is pushing transformativechange. Oil and gas companies can work collaboratively, using the Industrial Internet to unlock new value
from device data generation, robust connectivity, and interoperability. This information, in turn, will help
them to cut costs and to improve efficiency, productivity and profitability.

IIC:IND:E:V1.0:PB:20151009

8

Beyond Digitization: The Convergence of Big Data, Analytics, and Intelligent Systems in Oil & Gas

INVESTMENT OPPORTUNITIES FOR ANALYTICS IN OIL AND GAS
In today’s environment, where instant response is expected, oil and gas companies need advanced
systems that enable them to do the things they already do but faster and with enhanced flexibility.
Furthermore, advanced analytics solutions are needed to manage the volume of data (structured andunstructured) in upstream exploration and production (E&P), downstream applications and in corporate
functions.

IIC:IND:E:V1.0:PB:20151009

9

Beyond Digitization: The Convergence of Big Data, Analytics, and Intelligent Systems in Oil & Gas

CONCLUSIONAs next generation, smart assets are deployed, oil and gas companies will evolve their operations andprocesses to accelerate the rapid growth and volume of data. As this data is consumed, performancebased, predictive solutions will leverage current and emerging infrastructures and technologies to provide
material improvements in field operations. Analyzing the data derived from assets across the value chain
will not be reserved for just operators in the field. Rather, performance optimization and big data analytic
solutions will automate and enhance how valuable insights are communicated throughout the enterprise,from field operations to finance, and be used to address problems as downtime, system failures, andfinancial projections.
Security, safety, and the interoperability of platforms interfacing or creating data and information are justsome of the challenges facing the oil and gas industry as it begins to leverage these new capabilities. To
accomplish this, the industry must collaborate and share best practices, in order to learn, adjust and adapt
going forward. New risk scenarios need to be rigorously tested through the application of use cases andtestbeds. More recently, collaborative work has been facilitated across a broad ecosystem approachthrough organizations such as the Industrial Internet Consortium. Using a public-private partnershipmodel to solving the challenges in Oil & Gas, the Industrial Internet Consortium will be addressing
interoperability, use cases, testbeds, and security/safety/privacy in the coming months, to provide
recommendations and best practices as Oil & Gas companies begin to understand the impact of theconvergence of big data, analytics and intelligent systems on their businesses.

ABOUT THE INDUSTRIAL INTERNET CONSORTIUM
The Industrial Internet Consortium® (IIC™) is a global, member supported, organization that promotes theaccelerated growth of the Industrial Internet of Things by coordinating ecosystem initiatives to securely
connect, control and integrate assets and systems of assets with people, processes and data using
common architectures, interoperability and open standards to deliver transformational business andsocietal outcomes across industries and public infrastructure. The Industrial Internet Consortium ismanaged by the Object Management Group (OMG). For more information, visit www.iiconsortium.org.

IIC:IND:E:V1.0:PB:20151009

10

Beyond Digitization: The Convergence of Big Data, Analytics, and Intelligent Systems in Oil & Gas© 2015 Industrial Internet Consortium. Published October 2015. All rights reserved. The Industrial Internet Consortium is a
registered trademark of the Object Management Group (OMG). For a listing of all OMG trademarks, visit
http://www.omg.org/legal/tm_list.htm. All other trademarks are the property of their respective owners.

IIC:IND:E:V1.0:PB:20151009

11