Petroleum exploration and production processes typically generate enormous amounts of petro-technical data using sub-surface and surface sensors. The acquisition, transferring, managing, and interpreting of these huge sensor data, as well as the decision making based on it has led to the advent of the digital oilfield phenomenon in the petroleum industry. To achieve improved efficiency, accuracy, and performance, many E & P operators are aiming to apply fiber optics distributed temperature sensing data management technologies to add volume. Currently, high volume distributed sensing data transfer, storage, processing, archiving, retrieval and exchange system in the petroleum industry still face big challenges such as high cost of hardware and software, complicated implementation and deployment framework that is difficult to sustain such as scale and upgrade, as well as compatibility for data provided by different vendors. An efficient online real-time elastically scalable system that enables fast retrieval from big data infrastructures is therefore essential.

This paper describes a scalable web based enterprisefiber optic infrastructure for data exchange, management and visualization. This platform applies multi-tier client-server architecture, scalable distributed databases, PRODML (Production Markup Language), and web services technologies to provide a reliable mechanism to bring fiber optic data from the field site to the corporate network in real-time and enable user to visualize the data anywhere, any time. The support of PRODML industry standard make it vendor neutral and allow data exchanging from different systems and sharing data among users and different applications. The distributed Cassandra database enables the scalability to handle the fiber optic big data in a high performance and efficient way. Finally, the global inventory management system allows keeping track of changes to the asset and the instrumentation configuration over the life of the distributed sensor systems, as well as the ability to correlate the measurement data to the proper asset configuration. A case study is presented that demonstrates successful field testing to verify the functionalities of the newly developed system for high data volume distributed sensors. Specific attention is given to many advantages being offered by this new framework over existing ones.

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