Enabling Big Data Benefits in the Oil & Gas Sector
Stephen Sponseller, Business Director – Oil & Gas Solutions Kepware Technologies

The recent falling crude price and product devaluation have created huge revenue loss and business evaluation in oil & gas companies worldwide. When the industry goes through this type of phase, companies must be investing in technology to more effectively monitor operations, save energy, conserve resources, control and protect infrastructure, reduce asset downtime, and accelerate emergency responses as they change the focus from reaction and response to anticipation and prevention. Big Data systems and the Industrial Internet of Things (IIoT) are being implemented by companies as solutions to achieve these improved efficiencies in addition to their own limitations and challenges. The article explains more into this and focuses on new information management technologies to tackle challenges while working with these systems.

The Oil & Gas industry is constantly changing. Take the recent devaluation in product, for example. Almost overnight, companies were generating less revenue and business evaluation began to suffer. And while circumstances like these are certainly a challenge, they are nothing the industry hasn’t experienced before. When the industry goes through this type of phase, companies must keep in mind that the best way to deal with it is actually quite simple: priorities must change.

For example, when oil is at USD 100/barrel, upstream companies are focused on getting as many new wells online as quickly as possible; inefficiencies are accepted. But when oil prices decrease to USD 50/barrel, their focus shifts to quality performance. Every asset and process can be optimised to result in more efficient operations, from initial exploration to production, transportation, processing, and final distribution to end-users. By doing this, companies can lower business costs and, as a result, help off-set the financial burdens they are experiencing due to decreased product value.

One effective approach for enhancing operations involves investing in technology that features analytical tools, modeling, and optimisation components to help monitor operations and expose areas that require improvement. In addition to the short-term benefits of off-setting the aforementioned financial challenge, this approach also enables better long -term positioning. For instance, numerous case studies have shown that these types of technologies can decrease lifecycle costs by approximately 20 percent, increase asset life by 20 per cent, and decrease energy consumption by 15 to 20 per cent. With maximised operations, companies are better poised for competitive advantage once oil prices increase again and the focus returns to quickly onboarding new wells.

Big Data systems and the Industrial Internet of Things (IIoT) are being implemented as solutions by companies to achieve these improved efficiencies , which take these concepts even fur ther by applying new information Enhancing Productivity Enabling Big Data Benefits in the Oil & Gas Sector The recent falling crude price and product devaluation have created huge revenue loss and business evaluation in oil & gas companies worldwide. When the industry goes through this type of phase, companies must be investing in technology to more effectively monitor operations, save energy, conserve resources, control and protect infrastructure, reduce asset downtime, and accelerate emergency responses as they change the focus from reaction and response to anticipation and prevention. Big Data systems and the Industrial Internet of Things (IIoT) are being implemented by companies as solutions to achieve these improved efficiencies in addition to their own limitations and challenges. The article explains more into this and focuses on new information management technologies to tackle challenges while working with these systems. management technologies. Big Data systems allow for integrated information flow across all divisions, departments, and operations; and they also provide incredible data indexing and modeling programmes with visualisation capabilities that provide key insight into the current state of a company's process and identify areas for improvement. Furthermore, the predictive capabilities these tools offer enable companies to anticipate issues before they occur, providing the opportunity to address any potential problems before they impact performance. For example, a recent study from the ARC Advisory Group describes an offshore company that was able to replace a suspect seal on a water injection pump prior to failure, which ultimately saved the company USD 7.5 million in unplanned downtime.

By gathering extensive information on assets and processes, Big Data systems enable Oil & Gas operations to more effectively monitor operations, save energy, conserve resources, control and protect infrastructure, reduce asset downtime, and accelerate emergency responses as they change the focus from reaction and response to anticipation and prevention.

The Security Challenge
Clearly, there are benefits to implementing Big Data technologies; however, companies often face two common challenges when working with these systems, and the first is security. Assuming a company is able to collect and share data, this does not mean it is being done securely. It is understandable why companies experience security challenges since they want to get field data into the enterprise, but they don’t want to expose themselves to manin-the-middle attacks.

Also, they may want to share some of their data with partner companies, but not all of it. These difficulties often stem from the previously-utilised Host-Centric model for communications, where data requests go out across a network to the field from a centralised host.

This architecture typically comes with bandwidth limitations and high costs due to the significant amount of data it handles. To accommodate low -bandwidth availability, many traditional vendor-specific protocols were developed to the minimum requirements needed to access the data within field devices. At the time, security was not a concern or a top priority. Today, however, these protocols are considered inherently insecure and put data at risk of entering the wrong hands.

Enhancing Security with the Distributed Communications Architecture :
Companies can significantly reduce their security concerns by implementing the newly-proposed Distributed Communications Architecture, which features enhanced data security and operational efficiency. It removes data collection from centralised client applications, introducing an extra layer of security between vendorspecific protocols and the applications requesting data. In addition, it places the data collector as close to the device as possible, with less requests and responses going over the low bandwidth networks, thus improving communication efficiencies while also limiting the exposure of unsecure vendor-specific protocols.

With the Distributed Communications Architecture, companies only need to rely on one secure protocol to connect any and all applications and collectors that need to share data with each other, providing security in addition to efficiency.

The Data Collection Challenge
The second most common challenge to implementing Big Data technologies is data collection. Many Big Data applications originated in the IT world, where data collection challenges and solutions are much different than collecting data from the field. Industrial data, especially in the Oil & Gas market, is often sent through remote, off-site technologies that feature a variety of sensors, controllers, RTUs, and flow computers that house the data needed by these applications. In addition, some of the equipment in the field is significantly outdated—meaning it wasn’t designed to share data in an efficient manner. Finally, data collecting and sharing is often provided by each of the individual equipment vendors, whose solution works solely with their own equipment and systems, complicating the process even further .

Addressing Data Collection with Intelligent Data Aggregation :Ideally, all companies would have the means to start over and upgrade their outdated technologies and architectures in order to develop a model that addresses data collection challenges for Big Data systems. The reality, however, is that this type of investment is not currently feasible for most companies, and so they must come up with an alternative strategy for enabling Big Data systems to interface with their older technologies.

Fortunately, there are some suppliers, like Kepware Technologies, that are creating unique, easy to use, and cost-effective technologies that will allow companies to take advantage of Big Data solutions without making the significant financial investment needed to upgrade their entire system. We refer to this technology as 'Intelligent Data Aggregation'. It provides a sensible option for organisations that wish to reap the benefits of Big Data solutions and data collection without being forced into a substantial initial investment-which is especially helpful if a company is dealing with financial challenges due to the ups and downs of oil prices.

Intelligent data aggregation economically collects and shares data in users' preferred format, whenever they wish to access it. The technology shares this data in a secure fashion directly into the supply chain in an independent and agnostic manner, so users can access the most recent information at the instant they request it.

Ideally, an aggregator should provide data to Big Data applications by utilising IT-centric protocols (like SNMP, ODBC, and web services) and secure automation industry standards (like OPC UA). This allows for open connectivity between different systems to share data across remote networks. By working with these types of protocols and standards, companies will not have to deal with the challenges presented by traditional SCADA protocols, such as the transmission of large volumes of events, live unsecure information, and siloed data. Furthermore, these protocols and standards will provide the aggregator with the real-time support needed to ensure consistency in its performance and ability to communicate with existing technologies, which is key to consolidating and transferring data directly to a Big Data solution for analytics, modeling, and optimisation.

Benefits of Big Data Analytics
Once a company has deployed (or implemented) a proper aggregator and accompanying tools, they can begin reaping the benefits of Big Data solutions. There are a number of ways these solutions can positively impact organisations. Some of the most common benefits for Oil & Gas companies are :

Measurement: Big Data solutions can compare all process operations across oil or gas transport systems, and when they find an exception or potential issue, users are able to dig down further to examine the process, site, and or instrumentation. Measurement data can also be used for allocation and accounting purposes.

Remote Troubleshooting: On a similar note, having instant access to data and operational trends means that staff no longer has to manually collect/compile data and examine it before addressing a situation. This allows potential problems to be addressed sooner and gives staff the freedom to focus on other tasks after the fact, as the Big Data software will continue monitoring that specific issue.

Predictive Maintenance: As the aforementioned offshore company demonstrates, recognising and addressing equipment issues well in advance gives users the opportunity to fix them before they become a real problem, eliminating downtime that could cost a company millions of dollars .

Safety: Big Data technology can improve warning sign detection and ensure compliance with safety regulations and other regulatory reporting requirements.

Furthermore, an Intelligent Data Aggregator can be used to provide data not only to the Big Data/IIoT applications previously discussed, but also to traditional control applications like SCADA, Historian, and Measurement software-as well as to reporting tools for regulatory requirements. This 'one-stop shopping' approach to an enterprise’s data collection needs greatly simplifies the configuration and management of applications and provides optimal communication efficiencies across a network as it can reduce the number of redundant requests for data from devices by multiple applications that need their data. It also frees up devices to use their CPU processing power for their intended use—process control-and not for servicing needlessredundant data requests.

Conclusion
Big Data and the Industrial Internet of Things are certainly here to stay, and they are demonstrating their value to companies within all sectors of the Oil & Gas industry. Whether a company is primarily focused on short-term or long-term goals, utilising these technologies to analyse and address efficiency issues will put them in a better position to remain a competitive player in the industry, regardless of fluctuating oil prices. With the appropriate intelligent data aggregation technology in place, operators can reap the benefits of secure, optimised data sharing without redesigning a company's infrastructure.