Intelligent Oilfield - Cloud Based Big Data Service in Upstream Oil and Gas
- Xudong Yang (Baker Hughes, a GE company) | Oladele Bello (Baker Hughes, a GE company) | Lei Yang (Baker Hughes, a GE company) | Derek Bale (Baker Hughes, a GE company) | Roberto Failla (Baker Hughes, a GE company)
- Document ID
- International Petroleum Technology Conference
- International Petroleum Technology Conference, 26-28 March, Beijing, China
- Publication Date
- Document Type
- Conference Paper
- 2019. International Petroleum Technology Conference
- 2 Well completion, 6.4.3 Data and Communications Security, 7.6.4 Data Mining, 7.6.6 Artificial Intelligence, 5.6.1 Open hole/cased hole log analysis, 6.3.3 Operational Safety, 7.6 Information Management and Systems, 7 Management and Information, 5.6.11 Reservoir monitoring with permanent sensors, 6.4.3 Data and Communication Security, 5 Reservoir Desciption & Dynamics, 5.6 Formation Evaluation & Management
- Intelligent Oilfield, Big Data, Cloud Based Service, Distributed Fiber Sensing, Real-time data service
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|SPE Non-Member Price:||USD 23.00|
The Oil and Gas (O&G) industry is embracing modern and intelligent digital technologies such as big data analytics, cloud services, machine learning etc. to increase productivity, enhance operations safety, reduce operation cost and mitigate adverse environmental impact. Challenges that come with such an oil field digital transformation include, but are certainly not limited to: information explosion; isolated and incompatible data repositories; logistics for data exchange and communication; obsoleted processes; cost of support; and the lack of data security. In this paper, we introduce an elastically scalable cloud-based platform to provide big data service for the upstream oil and gas industry, with high reliability and high performance on real-time or near real-time services based on industry standards. First, we review the nature of big data within O&G, paying special attention to distributed fiber optic sensing technologies. We highlight the challenges and necessary system requirements to build effective and scalable downhole big data management and analytics. Secondly, we propose a cloud-based platform architecture for data management and analytics services. Finally, we will present multiple case studies and examples with our system as it is applied in the field. We demonstrate that a standardized data communication and security model enables high efficiency for data transmission, storage, management, sharing and processing in a highly secure environment. Using a standard big data framework and tools (e.g., Apache Hadoop, Spark and Kafka) together with machine learning techniques towards autonomous analysis of such data sources, we are able to process extremely large and complex datasets in an efficient way to provide real-time or near real-time data analytical service, including prescriptive and predictive analytics. The proposed integrated service comprises multiple main systems, such as a downhole data acquisition system; data exchange and management system; data processing and analytics system; as well as data visualization, event alerting and reporting system. With emerging fiber optic technologies, this system not only provides services using legacy O&G data such as static reservoir information, fluid characteristics, well log, well completion information, downhole sensing and surface monitoring data, but also incorporates distributed sensing data (DxS) such as distributed temperature sensing (DTS), distributed strain sensing (DSS) and distributed acoustic sensing (DAS) for continuous downhole measurements along the wellbore with very high spatial resolution. It is the addition of the fiber optic distributed sensing technology that has increased exponentially the volume of downhole data needed to be transmitted and securely managed.
|File Size||1 MB||Number of Pages||15|
Williams, T., Lee, E., Chen, J., Wang, X., Lerohl, D., Armstrong, G., Hilts, Y. 2015. Fluid Ingress Location Determination Using Distributed Temperature and Acoustic Sensing. SPE Paper # 173446, Paper Presented at the SPE Digital Energy Conference & Exhibition. The Woodlands, Texas, USA, 3-5 March.
Bello, O., Ji, M., Denney, T., Lazarus, S., Vettical, C. 2016. A Dynamic Data-Driven Inversion Based Method for Multi-Layer Flow and Formation Properties Estimation. SPE Paper # 181025, Paper Presented at the SPE Intelligent Energy Conference & Exhibition, Aberdeen, Scotland, United Kingdom, 6-8 September.