The distributed nature of fiber-optic measurements such as distributed temperature sensing (DTS), distributed acoustic sensing (DAS), and distributed strain sensing (DSS) enables nearly continuous monitoring of the downhole environment in both space and time. Though continuous monitoring opens the door to a rich new set of asset management applications, it comes with its own set of challenges in terms of data transmission, management, and security. Recently, cloud-based fiber-optic data management services have been successfully introduced to the oil and gas industry as an effective way to collect, transfer, store and display distributed measurement data from the downhole environment. To maximize the value of such cloud-based data management systems, and further improve the return on investment for asset managers, the large volume of distributed sensing data collected must be converted to value in a simple and easy-to-use form, depending on different applications. Traditionally, interpretation of distributed sensing data is a manual process conducted by engineers in a post-job workflow. This paper presents the successful integration of an analytics library into the cloud-based fiber-optic data management system. This integration enables real-time, and in some cases near real-time, asset decision making. The design of the analytics architecture is open to meet the wide range of application requirements by asset managers. A few application examples of the analytics integration will be presented using real-time data streamed directly from the field.
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.
In recent years, the upstream oil and gas industry has witnessed significant breakthroughs in developing and deploying permanent, on-demand, and distributed temperature (DTS) and acoustic (DAS) fiber-optic monitoring systems to optimize well completions and enhance production. Beyond steady advances in hardware, challenges associated with the analysis of distributed optical data are being addressed to enable delivery of value-driven answer solutions and services. Such solutions are often nontrivial and must be driven by scientific workflows in which data-driven models, advanced analytics and most importantly, physics-based models are applied at the right scale to correlate the data to downhole events. In this study, we present a methodology for integrating state-of-the-art intelligent completion and production tools together with a robust modeling and analytics framework for the efficient development of data interpretation services for complex downhole environments. The answer product platform discussed is built on fast computational models, robust data-driven analytics, cloud-based data streaming and management services, and real-time reporting delivery systems. Several case-studies demonstrate how our fiber-optics solutions and analysis are leveraged for applications such as leak detection, fluid injection profiling, acid placement, sand detection, and water ingress. For each case-history, we discuss the operational workflow for the downhole intelligent assembly, procedures for acquiring high-resolution DTS/DAS data, and the use of advanced fast physics or data driven models used to deliver the solution. Some of the application examples (e.g. water ingress and sand detection) also demonstrate how simultaneous measurements of DTS and DAS data are often critical to detecting and devising a strategy for mitigating well-specific issues. The case-studies presented in this study demonstrate how vast amounts of data can be acquired by fiber-optic interrogation systems and subsequently interpreted in real-time, or near real-time. Such a process is enabling oil and gas operators to make data-driven decisions that drive optimized reservoir performance and proactively mitigate operational risk such as NPT due to equipment failure.
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