Next Generation Downhole Big Data Platform for Dynamic Data-Driven Well and Reservoir Management

Abstract

Abstract The instrumentation of reservoirs and wells using distributed downhole sensor and information- communication systems has enabled significant advances in their management. Examples include monitoring of well integrity and reservoir compaction; production monitoring of artificial lift wells; data integration for short-term history matching, reservoir characterization and geologic model updating; flow rate allocation, inflow profiling, probabilistic production forecasting and downhole set point optimization in intelligent well completions; matrix acidizing and hydraulic fracturing characterization, dynamic estimation of petrophysical properties; dynamic geomechanical properties estimation; joint inversion of distributed downhole fiber sensing and time-lapsed seismic data for anisotropy permeabilities estimation; skin analysis; reservoir and well performance diagnosis; reservoir analysis and parameter estimation, multiphase flow assurance and many more. Expanding the benefits of the distributed downhole sensors is currently driving the need for big data infrastructures and associated dynamic data-driven application systems for reservoir characterization, simulation and management. However, the significant costs of setting up and managing the infrastructure to manage distributed downhole sensing data such as distributed temperature sensors (DTS), discrete distributed temperature sensors (DDTS), discrete distributed strain sensors (DDSS) and distributed acoustic sensors (DAS) is a major challenge. These distributed downhole data sources are characterized with high volume, variety, velocity, veracity, variability and visualization. Currently, the distributed downhole sensing data transfer, storage, processing, archiving, retrieving and interpreting system in the petroleum industry still faces substantial challenges. Some examples are a high cost of hardware and software, ongoing system support and maintenance, a complicated implementation and deployment framework that is difficult to sustain, scale and upgrade, as well as the need for data compatibility provided by different vendors. The objective of this paper is to present a platform which offers an automated one-stop shop for distributed downhole sensing data transmission, management and interpretation. This platform employs a big data infrastructure and allows for joint inversion of production and distributed downhole sensing data in a wide range of online real-time reservoir and well monitoring applications. This paper describes a vendor-neutral, scalable web-based enterprise distributed downhole sensing infrastructure for data exchange, management and visualization. This system also allows for calibration of DTS interrogators and integration with PI systems. This platform applies multi-tier client-server architecture, scalable distributed databases, Production Markup Language (PRODML), and web services technologies to provide a reliable mechanism to bring distributed downhole sensing data from the field site to the corporate network in real-time and enable users to visualize the data anywhere, any time. A framework for cleaning distributed downhole sensing data streams in real-time is developed to render the data produced by sensors usable for analysis (remove problems due to noise, outliers, measurement drifts, incorrect calibration and other issues). Using the distributed downhole sensing data management platform, we combine information from physics-based models with cleaned distributed downhole sensing live data to analyze anisotropy in permeability and skin in multilayer formations, estimate inflow profiles, determine multiplayer formation or petrophysical properties and estimate geomechanical and reservoir compaction properties. This paper demonstrates the capability of the distributed downhole sensor data infrastructure and information integration platform through the use of different sets of distributed downhole sensing data in various applications.