Intelligent oilfield - Cloud based big data service in upstream oil and gas

Intelligent oilfield - Cloud based big data service in upstream oil and gas

Machine learning

Petroleum industry

Gasoline

Environmental impact

Oil wells

Gas industry

Digital storage

Accident prevention

Information management

Data acquisition

Environmental technology

Large dataset

Real time systems

Information services

Metadata

Petroleum reservoir evaluation

Oil well completion

Data visualization

Data Analytics

Data transfer

Cloud analytics

Electronic data interchange

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. 2019, International Petroleum Technology Conference

Yang, Xudong

Bello, Oladele

Yang, Lei

Bale, Derek

Failla, Roberto

International Petroleum Technology Conference 2019, IPTC 2019, March 26, 2019 - March 28, 2019

2019

conferencePaper

10.2523/iptc-19418-ms

http://dx.doi.org/10.2523/iptc-19418-ms