Kepware Whitepaper. Enabling Big Data Benefits in Upstream Systems. Steve Sponseller, Business Director, Oil & Gas. Introduction

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1 Kepware Whitepaper Enabling Big Data Benefits in Upstream Systems Steve Sponseller, Business Director, Oil & Gas Introduction In the Oil & Gas Industry, shifting prices mean shifting priorities. With oil at $100/barrel, 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/barrel, the priority shifts to performance. Everything counts. That applies not only to every drop of oil but to every asset and every process. The industry has been through this before. Upstream operators 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 percent, increased asset life by 20 percent, and decreased energy consumption by 15 to 20 percent. 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

2 enable upstream Oil & Gas operations to more effectively monitor production, 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 Big Data technologies in their operating and business systems, upstream Oil & Gas companies can leverage information to anticipate problems, reduce 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. Big Data applications change the focus from reaction and response to anticipation and prevention. Big Data is up to the challenge of optimizing assets and production processes in Oil & Gas well fields. 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 today is that Big Data systems must interface with older technologies.

3 A typical SCADA system uses an architecture that is essentially a wide area network adaptation of ISA95. The accompanying table defines the levels in the ISA95 layered model and includes the corresponding levels in an Oil & Gas production SCADA system. LEVEL FACTORY AUTOMATION OIL & GAS PRODUCTION The wireless sensor networks interface with controllers, flow computers, or remote terminal units (RTUs) via dedicated base units. Level 5 Business Systems Business Systems Level 4 Plant Level (ERP, MRP, and MES) Level 3 Operation Unit Level Well Site Level Level 2 Level 1 Machine/Process Automation Level Controller Level Field Level (Measurement and SCADA) Process Unit (e.g. separator, tree) Level Controller/Flow Computer/RTU Level 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 systems that could employ as many as three layers within a wellsite. 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. Well field SCADA systems can include hundreds or even thousands of controllers, flow computers, and RTUs. Further complicating matters is that many upstream operations 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. Illustrating another recent phenomenon is that more upstream companies operate multiple production systems and track a variety of hydrocarbons including natural gas, natural gas liquids (NGLs), and oil. Among these operations, the measurement technology and information delivered from the field differ significantly.

4 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. Intelligent Data Aggregation Innovative suppliers are meeting this challenge with unique, easy to use, and cost-effective technology that enables Big Data solutions to collect and organize data. Called intelligent data aggregation, it revolutionizes 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. 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 enterprise systems in an independent and agnostic manner. Intelligent data aggregation is ideal for multi-system, multi-sensor environments in which integration is the greatest challenge. Finally, intelligent data aggregation allows for continuously current technology with seamless migration. The aggregator uses 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. With real-time clock support, the aggregator ensures a consistent time base for all information. Supporting an array of SCADA protocols also allows the aggregator to communicate with existing controllers, flow computers, and RTUs. It consolidates information and transfers it directly to a Big Data solution for analytics, modeling, and optimization. As an intelligent data aggregation example, the KEPServerEX communications platform from Kepware Technologies supports over 150 communication drivers for data collection from meters and sensors via controllers, flow computers, and RTUs, and uses the OPC UA standard as the link to SCADA, HMI, and other automation systems. In parallel, KEPServerEX also forwards this information to Big Data technology from Splunk, the industry-leading platform for collecting and indexing machine data. This enables the optimized searching, monitoring, alerting, analyzing, and reporting of the data.

5 Big Data Oil & Gas users Intelligent Data Aggregator benefit from the same network and operations monitoring, security monitoring, and analytics that were once reserved for enterprise-level Scada Systems Controllers and RTUs Controllers and RTUs Actuators and Sensors data centers. As an intelligent data aggregator, KEPServerEX supports over 150 protocols to enable communications between Big Data applications and devices at any level of a SCADA network. KEPServerEX with the Industrial Data Forwarder for Splunk Plug-In enables Splunk Enterprise and Splunk Cloud to securely and reliably collect, forward, and store information from meters and sensors at scale so users can identify anomalies and perform statistical calculations. Oil & Gas users benefit from the same network and operations monitoring, security monitoring, and analytics that were once reserved for enterprise-level data centers. The massive amount of information that a SCADA system can collect from flow computers and RTUs in a production well field is not a problem for the Splunk machine data platform. For example, in a metering application that is compliant

6 with American Petroleum Institute (API) 21.1, API 21.2, or Alberta Energy Regulator Directive 17, a flow computer or RTU can output well over 100 live data values, hourly and daily logs with a dozen data items per generation, and dozens of alarm and event messages. Live process variables and trend data from compressors, power generators, pumps, separators, wellhead trees, and other process units are collected in addition to meter information. Multiplying this amount of information by hundreds or even thousands of wells allows prospective users to appreciate the environment in which Big Data thrives. KEPServerEX also forwards this information to Big Data technology from Splunk... This enables the optimized searching, monitoring, alerting, analyzing, and reporting of the data. There are four common-use cases for KEPServerEX and Splunk that have specific applications for Oil & Gas operations. 1. Measurement, Verification, and Constant Commissioning: Splunk software can compare all process operations across thousands of wells, immediately find exceptions, and enable users to visually drill down to the site, process unit, sensor, and actuator levels. The machine data 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. 2. 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 liquid loading at a gas well could be a subtle casing pressure trend that differs from those in similar wells. Once operators address the issue (such as by implementing a plunger lift at the well), Splunk software can continue tracking and optimizing that process. 3. Capacity Planning: The ability to create an advanced model allows Splunk software to continually analyze lifecycle production trends in order to refine forecasting. A high-resolution view can be critical to operations (such as a shale gas production field in which wells are characterized by rapid production changes of up to 70% in one year). 4. Safety, Security, and Compliance: In light of unconventional well dynamics, it is critical to identify the operating parameters that could present higher risks to personnel who are planning to visit the site. Big Data technology can improve detection of warning signs (such as pressure trends at the well, separators, or vapor recovery units) and ensure compliance with safety regulations.

7 Conclusion Intelligent data aggregation helps fulfill the exciting potential of Big Data. By gathering extensive information on assets and processes, Big Data systems enable upstream Oil & Gas operations to more effectively monitor production, save energy, conserve resources, control and protect infrastructure, reduce asset downtime, and accelerate emergency responses. By incorporating Big Data technologies in their operating and business systems, upstream Oil & Gas companies can leverage information to anticipate problems, reduce costs, and coordinate resources in one effective process. Intelligent data aggregation will accelerate the acceptance of Big Data in operations that currently 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. Intelligent data aggregation will accelerate the acceptance of Big Data in operations that currently use SCADA systems. References 1. Bill Lydon, Simplifying Automation System Hierarchies, Automation.com, November 2012, com/automation-news/article/simplifying-automation-system-hierarchies.

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