On the edge, the next wave of digital transformation

Over the last several years, the oil and gas industry has benefited greatly from new digital capabilities that have delivered numerous step changes in performance across the E&P value chain. For example, cloud computing has enabled the industry to harness the combination of big data, scientific knowledge and domain expertise to make large-scale petro-technical applications and workflows accessible to all users. The next step in the industry’s transformation is the application of digital technology in field operations, or “on-the-edge” computing.

In today’s marketplace, it is imperative that companies leverage digital innovation to improve efficiencies and achieve higher performance levels. The industry has been challenged with developing innovative technologies to reduce total operating costs, and enhancing the productivity of both people and oilfield equipment, all while minimizing HSE exposure and environmental footprint.

TRADITIONAL APPROACH

Decades ago, the industry took a major first step toward a digital oilfield concept, with the instrumentation and implementation of traditional SCADA systems. However, this approach did not scale to all fields, thus leaving significant opportunity to enhance how instrumented oilfield equipment is managed, and how the data generated by this equipment are utilized in real-time.

While instrumentation of oilfield equipment has provided a significant step forward in generating data, as well as insights, in most cases, there is little means of converting those insights into actions. As a result, we still are dependent on human awareness and human interventions in remote and risky environments. Field visits are the standard means for checking and adjusting field equipment, which often translates to periods of costly downtime, and significant HSE exposure, due to the number of miles driven to remote wellsites.

In addition, the industry has failed to fully leverage the massive amounts of data generated by machines during oilfield operations. A single wellsite may generate up to 2 TB of data per day. However, transmitting these large datasets back to the office can be costly, especially when considering low bandwidth and latency issues, and the remoteness of many oilfield operations that often rely on satellite or radio communication for connectivity.

NEW TACTIC

Recently, we have seen the emergence of edge computing and industrial internet of things (IIoT) technology, which has positively impacted manufacturing processes. These emerging technologies are defining the next wave of digital transformation in the oil field. With these innovative new technologies, we can gather more data types, in addition to the sensor-generated information (i.e. video, acoustic vibration), to derive intelligence from a variety of data, while also enabling the ability to act in real time.

Powerful edge computing enables data contextualization, where the data are generated, thus delivering relevant actionable insights on site. As a result, data transmission and storage costs are reduced significantly. Additionally, the raw data and/or actionable insights can then be transmitted back to big data environments like the cloud, or be used at the edge for automation—thus creating a distributed intelligent environment, or the Internet of Intelligent Things.

Automation and/or control is achieved through the use of edge applications—performance-boosting domain-specific workflows and algorithms based on machine learning or artificial intelligence. These applications provide intelligent set-points or commands to the controlling devices, like PLCs, to enable fully autonomous operations. These significant technological innovations are having a direct impact on the overall productivity of personnel. Through data contextualization, machines manage the repetitive and mundane tasks, so that data analysts can focus on increasingly complex or anomalous challenges. With edge applications that enable automation or remote control, technicians and engineers can limit trips to the field, while reducing overall downtime.

Edge-empowered wearable and AR/VR devices are also playing a role in enhancing the productivity of oil and gas workers. These wearables provide wellsite workers with access to real-time data and intelligence, while also allowing real-time collaboration and communication with an enterprise. A worker using edge-empowered wearables can quickly access standardized work instructions, schematics, or many other types of resources, while also sharing images and video in real time with co-workers in the office.

EDGE COMPUTING

Launched in mid-2018, Agora is a Schlumberger start-up venture focused on providing edge computing and IIoT solutions to the oil and gas industry. Agora delivers dynamic intelligence to the field through its edge computing and IIoT platform, which is built on years of domain expertise and operational experience. The edge computing platform, designed specifically for the oil and gas industry, is built on three main pillars: 1) openness; 2) security; and 3) ability to scale.

Openness refers to the ability to work with field equipment from any manufacturer. Additionally, data generated from field equipment can be transmitted to open, public or proprietary data ecosystems. Within this context, openness also refers to open innovation. Agora provides a software development kit that can be used by any company looking to bring innovative edge computing and IIoT solutions to the oil field. Through its growing partnership ecosystem, Agora is bringing digital innovation from both inside and outside the oil and gas industry.

Ensuring edge computing and IIoT solutions are secure is paramount. These solutions must be secure by design and in operation—from the edge to the cloud to the enterprise—covering data security and asset integrity.

Due to the complex nature of the industry, scalability is critical. An edge computing and IIoT platform for oil and gas must be agile, with the ability to independently manage multiple operational use cases, while also enabling edge device management through remote tools and over-the-air updates.

OPERATIONAL USE CASES

Through its marketplace, Agora offers applications that utilize tools, such as workflow automation, artificial intelligence or machine learning, for a wide range of operational use cases at the edge.

Workflow automation to prevent gas lock for ESPs. By using the intelligent Agora Gateway ruggedized computing device, data from ESPs and wireless gauges can be acquired, standardized and then made available to multiple users via a single application. To prevent gas locking in the borehole, an automated skid is used to control the well’s annular pressure and gas flow, based on a dynamic well model running autonomously at the wellsite, while continuously improved in the cloud environment.

Artificial Intelligence for visual analytics. Video is becoming a new way of sensing. Visual analytics can deliver a step-change in wellsite safety and productivity of assets by facilitating a number of applications, from H2S or flare monitoring, to remote inspection. Using artificial intelligence, edge-empowered video cameras can be used for multiple applications, thus reducing human footprint at the site.

Fig. 1. Machine learning algorithms identify pump health and lift efficiency and enable engineers to manage them remotely.

Machine learning at the edge for rod lift optimization. Through the Agora Rod Lift Advisor, operators gain access to intelligence by running machine learning algorithms to identify pump health and lift efficiency. Fig. 1. The application enables operators to monitor a vast amount of rod pumps in real time, while also leveraging the ability to manage them remotely.

EDGE INTELLIGENCE

By driving dynamic intelligence through an open, secure computing and IIoT platform, oil and gas operators can achieve a step change in performance. Using a digitally empowered environment and domain-specific workflows and algorithms, companies are enhancing operational efficiency and productivity, while also limiting safety exposure and minimizing overall environmental footprint.