Edge Computing: A Powerful and Agile Platform for Digital Transformation in Oilfield Management

Abstract

Abstract In Production operations, asset performance depends greatly on maximizing the run life of equipment while reducing the cost of maintenance. Often, E&P operators have a reactive approach to field maintenance resulting in uneccessary downtime in logistics, inventory management, diagnosing the issues, and in taking the recommended actions. This can lead to higher operating costs and non-productive time. E&P operators are aggressively looking to increase production with operational efficiency gains. In the unconventional fields, a large number of wells have been drilled and put in production with various artificial lift techniques. Proactive well and field production management requires digital enablement of operations, with no data silos and data flowing seamlessly from the subsurface to the hands of the operator. With huge amounts of data being collected, it is imperative to apply data-driven techniques to gain more insights that can be utilized to better manage production. A data-driven approach can provide huge benefits for organisations holding vast amount of reservoir, production, and facilities data. It could provide insights into non-linear multidimensional relationships between parameters so that the field development is better understood and optimized. It could allow companies using a proactive approach towards field operations and equipment maintenance resulting in additional cost savings. This paper presents case studies in which operators optimized production utilizing edge-driven Industrial Internet of Things (IIoT) solutions. These edge IIoT solutions enable fast-loop control through a combination of physics and data-driven workflows, which empowers the operator to proactively manage their assets and focus attention on potentially problematic wells. The solution’s architectural setup and ability to deliver fast-loop control workflows at the edge enables operators to successfully detect and manage potential issues and ultimately improve well performance. Additionally, this approach reduces the dependency upon domain experts to frequently analyze data. The high-frequency data capturing resulted in predicting equipment performance with confidence and allowing remote well management to reduce health, safety, and environment (HSE) risks while decreasing logistics and maintenance costs.