With digital oilfield programs focusing on optimizing key E&P work processes, two of the hottest emerging digital technology trends, Big Data and the Internet of Things (IoT), are poised to make additional digital inroads into the business. Google Books defines Big Data as, “A blanket term for any collection of datasets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications.” The IoT and its counterpart industrial IoT (IIoT) are defined by Autodesk Fusion Connect as, “The interconnection of uniquely identifiable embedded computing devices within the existing Internet infrastructure.” Key issues surround the emerging role of Big Data and the IoT in the industry.
Consumerization of IT
Underlying the industrial application of Big Data and the IoT is that today’s digital technology market is increasingly driven by consumer applications of IT. This is forcing cultural changes into industrial business environments, including increased expectations for personal use of hyper-connected, continuously advancing products and real-time access to information. Five years ago “bring your own device” would have been viewed as a naïve and unrealistic expectation, primarily driven by the “new generation” of oilpatch workers. Today companies acknowledge the reality of personal choice, and management teams are struggling with how to make “bring your own device” work in a secure manner.
There are going to be many more issues driven by consumerization of IT. For example, will the exponential growth in consumer IoT outflank IIoT to become the primary technology driver? The industry has seen this play in other digital technology areas. Consumerization is powerfully enabled by its ability to be distributed and to scale both up (through connectivity, clouds, etc.) and down (lower costs, ever greater permeation). Rapidly declining costs with rapidly expanding capabilities can create business imperatives for adoption of consumer- based technology. Moreover, distributed information systems are a better match to the physical world, distributed assets and real-time operations such as oil and gas. The practical application of Big Data and IoT to E&P will inevitably need to account for this fundamental technology trend.
Increasing scale and complexity
As oil and gas companies take in petabytes of data daily, improving business performance will critically depend upon the ability to identify and understand trends; correctly interpret geotechnical, production and operations data; and make effective operational decisions. The ability to access and draw insights from datasets is at the heart of profitability in an industry where success relies on properly forecasting future performance and keeping costs low. The challenge is that the Big Data volumes, potentially greatly expanded by implementation of the IoT, will produce an information system of such scale and complexity that it threatens to overwhelm existing systems. The data volume is growing, but a company cannot afford to get lost in datasets.
Companies face big challenges in quickly manipulating large volumes of data and mining them for relevant information. Analysis and insights must be delivered in near real time to support decision-making in key areas such as drilling and production optimization. Companies are tracking both structured data and unstructured data. Oil and gas companies must also deal with data from external sources to track service crews, truck traffic, equipment and water usage. The requirements for Big Data and the IoT are not coming from just one part of the industry but pose a challenge across the entire spectrum.
The oil and gas industry has always managed large volumes of data (especially seismic) and operated assets through interconnected sensors and industrial control systems. However, the scale, diversity and complexity of systems is rapidly growing. Today a new deepwater drillship may have more than 20,000 sensors and be able to generate more than 4 petabytes of data daily. The production platform for a major offshore field could have as many sensors as a large refinery (nearly 100,000), including both downhole and topside equipment. Implementing a factory-drilling operations model for large-scale shale development can yield similar data volumes.
Much of the data being collected by this rapidly expanding array of sensors are often not retained due to the limitations of current data infrastructures. Some estimates suggest that companies only process 20% of the data they collect. Another survey found that an operator only uses 5% of the data collected on an offshore drilling rig due to data storage, transmission and commercial constraints.
Impact of real time
The trend of moving more operations and decision processes to real time continues to grow. The drive to real time is at the heart of application of the IoT and Big Data. Since operational groups are more responsible for real-time process control and field automation, Big Data and the drive to real time is likely to alter technology development processes as well. Several things need to happen for a company to successfully take advantage of Big Data, including increasing the interconnectivity of sensors and smart equipment and improving data management systems. Successfully addressing the challenges of Big Data in oil and gas is about more than selecting the right tools but is also about improving access and interpretation processes of the data by domain experts.
Next-generation digital oil fields
“System of systems” is the essential framework of the real world, both physical and digital, and with it comes a staggering growth in scale and complexity. How we learn how to handle this complexity and its co-equals— security, reliability and resilience—will likely decide the fate of the IoT. More than technology, the real issues are around the data environment and the growing value of data in business terms, the combined effect of deployment and utilization at scale. The empowerment of local users facing real-time demands will consistently (perhaps permanently?) outflank traditional IT organizations, processes and cultures.
To evolve and execute the digital oilfield vision for oil and gas, enterprise and functional systems must be modified so they can interface with IoT sensor-based technology along with a host of other disparate systems such as engineering, logistics and procurement that must be integrated into a holistic data foundation. Enterprise architecture must mature from an aggregation of point solutions into an enterprise view of critical business processes. From a vendor perspective, the challenge will be to develop products and services that can both leverage open standards and offer an element of differentiation.
Equally important will be the need to bring together new analytics centers of excellence with traditional data management. Data teams must be engaged in projects so that architected information flows lead to data-driven automation. As Big Data and the IoT remake digital oil fields into optimized and highly automated systems, production targets will be met more often, operating costs will be lower, and producing fields and reservoirs will be optimized, with higher profits for companies and shareholders.
Overcoming “barriers to adoption” of new digital technology has been a natural part of managing the E&P business. The challenge, as always, is balancing the potential performance gains of new technology against the proven capabilities of established practices. However, the advancing technology of the IoT and Big Data, combined with the business imperatives for sustained cost management and productivity gains, are making this old problem new again for E&P organizations and cultures. Big Data and the IoT are here to stay and likely to expand significantly.