Edge Computing: Continuous Surveillance and Management of Production Operations in a Cost Effective Manner
- Abhishek Sharma (Agora Schlumberger) | Prince Samuel (Agora Schlumberger) | Gian-Marcio Gey (Agora Schlumberger) | Sujit Kumar (Schlumberger)
- Document ID
- Society of Petroleum Engineers
- SPE Annual Technical Conference and Exhibition, 26-29 October, Virtual
- Publication Date
- Document Type
- Conference Paper
- 2020. Society of Petroleum Engineers
- 6.3 Safety, 7 Management and Information, 3.1 Artificial Lift Systems, 7.2.1 Risk, Uncertainty and Risk Assessment, 7.2 Risk Management and Decision-Making, 3 Production and Well Operations
- Production Operations, Internet of Things, Artificial Lift, Digital OilField, Machine Learning
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E&P operators are aggressively looking to increase production with operational efficiency gains. 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 various case studies where operators improved operational efficiency and optimize 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 such as artificial lift pumps, ultimately improving the 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.
|File Size||760 KB||Number of Pages||9|
Gartner IT Glossary, https://www.gartner.com/it-glossary, Accessed 3 March 2018.