This article, written by Assistant Technology Editor Karen Bybee, contains highlights of paper SPE 112517, "Transforming E&P Data Into Knowledge: Applications of an Integration Strategy," by Jean-Paul Couput, Alain Louis, SPE, and Jacques Danquigny, SPE, Total S.A., originally prepared for the 2008 SPE Intelligent Energy Conference and Exhibition, Amsterdam, 25-27 February. The paper has not been peer reviewed. Data validation and reconciliation (DVR) applied to flow-measurement systems improves the reliability and accuracy of production estimates. The potential of DVR to analyze, quantify, and interpret large amounts of data and provide more information from existing sensors is of particular value when key measurement devices fail or are not available. Introduction The main objective of the digital-oilfield concept is to maximize production and recovery by integrating all production operations, from the reservoir to export, by use of suitable models and optimization techniques. The combination of both existing and leading-edge technologies ensures that field optimal efficiency is achieved. Today, most production systems require accurate raw production data. All measured data contain various types of errors (e.g., intrinsic measurement errors, gross errors, and noise). Moreover, when real-time measuring systems, such as multiphase flowmeters (MPFMs), are used, errors may originate from the inaccuracy of the model used to compute the flow rates of the individual phases. Operating decisions based on such data can affect reserves calculations. The traditional approach to overcome these issues by combining best practices and measurement devices is not sufficient any longer. In the upstream area, new sensor technologies including three-phase metering and virtual metering have emerged. They still remain a challenge, especially in deepwater subsea applications, where equipment repair or replacement involves large costs. One solution is to combine data from physical sensors with virtual metering and DVR models. This is an innovative approach that has been proved in the downstream area and that effectively helps increase oil production and recovery. Data-Quality Issues Building strong and robust field-monitoring systems assumes that the right and correct measurement and data are made available for further processing at the right time. This constitutes a real challenge for existing and future upstream installations, where both instrumentation and associated measurements are deficient and inconsistent. Another issue is the lack of measurement in case of a failure or in the case of measurement systems designed with minimum equipment. Such questions are not new, but now they are critical and more difficult to address, mainly because requirements are more stringent and measurement accessibility and quality control (QC) are more problematic, especially in deepwater-field developments and in unmanned situations.
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