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Enabling big data benefits across the oil and gas supply chain.

In the oil and gas industry, shifting prices mean shifting priorities. With oil at $100/ bbl, exploration and production companies emphasize speed. They do everything they can to get new wells online in the least possible time. Anything else is a lower priority. At $50/bbl, the emphasis shifts to performance. Everything counts. That applies not only to every drop of oil, but to every asset and every process, from initial exploration to crude oil transportation to final distribution to end-users.

The industry has been through this before. Oil and gas asset owners know they can realize a return on investment in technology that applies analytics, modeling, and optimization to their assets and processes in order to increase efficiencies and reduce costs. This allows them to operate a more effective, competitive business and better position themselves for profitability when prices rebound.

Management today is embracing the potential of Big Data systems, which integrate information flow across a company's myriad divisions, departments, and operations to improve decision-making, optimize resources, and ultimately increase margins.

According to numerous case studies, technologies including asset management, energy management, and optimization have decreased lifecycle costs by 20%, increased asset life by 20%, and decreased energy consumption by 15-20%. Big Data systems take these concepts further by applying new information management technologies to help achieve near-term and long-term efficiencies.

Big Data systems provide the capability to collect, index, and analyze data quickly and display it on executive dashboards. They draw information from sensors and meters across the entire operation and feed it into analytic, modeling, and visualization tools for conversion to actionable knowledge.

By gathering extensive information on assets and processes, Big Data systems enable oil and gas operations to more effectively monitor operations, save energy, conserve resources, control and protect infrastructure, reduce asset downtime, and accelerate emergency responses.

Big Data applications change the focus from reaction and response to anticipation and prevention. By incorporating these technologies in operating and business systems, the oil and gas supply chain can leverage information to anticipate problems, cut costs, and coordinate resources in one effective process.

But implementing Big Data solutions can be difficult, particularly when the information that is needed must be extracted from countless remote meters, sensors, and SCADA systems. This highly complex effort can require considerable cost, time, and engineering expertise. These can be major hurdles to the deployment of Big Data solutions.

The Integration Challenge

Ideally, a Big Data system incorporates an architecture that is based on the Industrial Internet of Things (IIoT), which leverages the Internet Protocol (IP) down to the sensor level. While IIoT is gaining momentum, the reality is that Big Data systems must interface with older technologies.

A typical SCADA system uses an architecture that is essentially a wide area network adaptation of ISA95. The following table defines the levels in the ISA95 layered model and includes the corresponding levels in an oil and gas production SCADA system.
LEVEL   FACTORY AUTOMATION            OIL & GAS PRODUCTION

5       Business Systems              Business Systems

4       Plant Level (ERP, MRP, and    Field Level (Measurement and
        MES)                          SCADA)

3       Operation Unit Level          Well Site Level

2       Machine/Process Automation    Process Unit (e.g. separator,
        Level                         tree) Level

1       Controller Level              Controller/Flow Computer/ RTU
                                      Level

0       Sensor/Actuator Level         Meter/Sensor/ Actuator Level


The multilevel computing model is complicated, expensive, and requires ongoing configuration control and lifecycle investment. Fortunately, this model is changing to enable a more efficient and streamlined architecture. (1)

Meanwhile, operators are maintaining multi-layer systems. Despite the adaptation of wireless sensor networks, the architecture differs significantly from IIoT. In these applications, most wireless sensor networks use protocols such as ASCII or Modbus over proprietary radio networks.

The wireless sensor networks interface with controllers, flow computers, or remote terminal units (RTUs) via dedicated base units. The former devices reside on wide area SCADA networks, which use a variety of physical media and, often, non-IP communications protocols.

Pipeline SCADA systems can include hundreds or even thousands of controllers, flow computers, and RTUs. Complicating matters is that many asset owners are attempting to merge information from multiple SCADA systems due to acquisitions. In many cases, the systems are from different suppliers and use different communications protocols, hardware, and software at every level. In the IT world, these are known as "siloed systems" in which massive amounts of information could be locked away.

Finally, few SCADA system designs were implemented with Big Data analytics in mind. SCADA servers can interface with these systems by employing extensive, after-the-fact project engineering, but efforts to achieve data transfers, recover missing information, and change data formats can be very expensive.

Until now, collecting data from sensors, controllers, RTUs, and SCADA systems has been a laborious and costly effort in Big Data system design and implementation.

Combatting Challenges with Intelligent Data Aggregation

Innovative suppliers are meeting this challenge with unique, easy to use, and cost-effective technologies that enable Big Data solutions to collect and organize data. Called intelligent data aggregation, it is revolutionizing the way Big Data systems can be implemented.

Intelligent data aggregation economically gathers and presents data in the format users want, when they want it. The technology feeds data directly and securely into the oil and gas supply chain in an independent and agnostic manner, allowing for continuously current technology with seamless migration.

An ideal aggregator should use IP-based protocols and secure automation industry standards (including OPC Unified Architecture [UA], which facilitates open connectivity for a variety of systems and bridges all types of data across remote networks) to link to Big Data applications. Doing so eliminates the shortcomings of SCADA protocols in the transmission of large volumes of events, live information, and siloed data.

Additionally, an aggregator with real-time clock support will ensure a consistent time base for all information, and supporting an array of SCADA protocols allows the aggregator to communicate with existing controllers, flow computers, and RTUs. These important functions will help consolidate and transfer information directly to a Big Data solution for analytics, modeling, and optimization.

Benefits of Big Data Analytics

While there are a number of ways in which the oil and gas supply chain can utilize the information provided by Big Data solutions, there are four common-use cases that apply to companies of all sectors.

Measurement, Verification and Constant Commissioning: Big Data solutions can compare all process operations across oil transport systems, immediately find exceptions, and enable users to view the site, sensor, and actuator levels. The platform illuminates issues (such as a compressor or power generator exhibiting a minor change in fuel consumption, loading, runtime, or vibration). Such early indicators lead to timely corrective actions, which could prevent costly downtime events.

Root Cause Analysis and Remote Troubleshooting: Operations staff can act on situations that would have otherwise gone undetected or required considerable time for manually sorting through data. For example, an early indicator of a pipeline leak could be a subtle pressure change that differs from surrounding parts of the pipeline. Once operators address the issue, Big Data software can continue tracking and optimizing that process.

Capacity Planning: Certain software has the ability to create an advanced model that allows operators to continually analyze lifecycle production, transportation, and distribution trends in order to refine forecasting. A high-resolution view can be critical to operations, especially given the ever-changing nature of the oil and gas industry.

Safety, Security, and Compliance: Big Data technology can improve detection of warning signs (such as pressure trends) and ensure compliance with safety regulations.

Capitalizing on Big Data

Intelligent data aggregation helps fulfill the exciting potential of Big Data. By gathering extensive information on assets and processes, Big Data systems enable oil and gas supply chain operations to save energy, conserve resources, control and protect infrastructure, reduce asset downtime, and accelerate emergency responses. Oil and gas companies can leverage Big Data to coordinate resources across the supply chain in one effective process.

Intelligent data aggregation will accelerate the acceptance of Big Data in operations that use SCADA systems. It ensures system connectivity today and tomorrow while "future proofing" infrastructure investments to reduce risk and eliminate re-engineering. Data will be more secure and aggregated in ways that enhance information management and presentation.

REFERENCES:

(1)--"Simplifying Automation System Architectures," Bill Lydon, Automation dot com, September 2012

By Steve Sponseller, Business Director--Oil & Gas Solutions, Kepware Technologies

Steve Sponseller is business director for Oil & Gas Solutions at Kepware Technologies, a private software development company headquartered in Portland, ME. He is also a member of the Control System Integrators Association (www.controlsys.org).
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Author:Sponseller, Steve
Publication:Pipeline & Gas Journal
Date:Apr 1, 2015
Words:1416
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