Field and Installation Monitoring With Online Data Validation and Reconciliation - Middle East and West Africa Offshore Fields
Authors: Denney Dennis 
Published: February 2015

This article, written by Senior Technology Editor Dennis Denney, contains highlights of paper SPE 128717, ’Field and Installation Monitoring Using Online Data Validation and Reconciliation - Application to Offshore Fields in the Middle East and West Africa,’ by Jean- Paul Couput and Renaud Caulier, Total S.A., and Ulrika Wising, Belsim, prepared for the 2010 SPE Intelligent Energy Conference and Exhibition, Utrecht, Netherlands, 23-25 March. The paper has not been peer reviewed.
Optimization opportunities for field-production efficiency and performance can be improved by combining monitoring and control elements in “value loops.” Such an approach is possible when reliable and consistent data and information from the complete production system are provided to the right users at the right time. Existing field applications of advanced-data-validation and -reconciliation (DVR) techniques use all available information and measurements to provide reliable and consistent data, from reservoir to delivery.
Introduction
The main objective of field-production optimization by use of “digital-oilfield” concepts is maximizing production and recovery, with a specific focus on safety and environment preservation. All measured and calculated data have an amount of uncertainty and could contain various types of errors including intrinsic measurement errors, calculation errors, poor representation, incorrect fluid density, drift, and noise.
When complex measuring systems such as multiphase-flow meters (MPFMs) or wet-gas meters must be used, errors may originate from inaccuracies in fluid parameters, thermodynamic and hydrodynamic models used to compute the flow rates, and properties of the individual phases. Erroneous production data may affect economics and revenues, but also may affect reserves calculations made from an incorrect material-balance calculation. A cost-effective solution is to use and process all information and measurements simultaneously with an advanced-DVR approach.
Data-Quality Issues
Typically, complex-measurement situations are found in unmanned developments (e.g., marginal fields with minimum facilities) or in deepwater subsea applications where equipment repair or replacement involves high costs. Another example is production from mature fields where measurements become more uncertain with data instability, high water cut, high gas content, and other factors.
Advanced-DVR Techniques
Data-validation and data-reconciliation sciences were developed in the late 1950s. Data validation and data reconciliation sometimes are used together and sometimes separately. There are different approaches in the two technologies that define the rigorousness of the technologies. To ensure data quality, a combined approach that uses the most-rigorous approaches is recommended, referred to in this paper as advanced DVR.
Data validation ensures that a program or a system operates on clean correct data. It uses routines that check the correctness and meaningfulness of data. The most common method is the range check (i.e., it checks that the data lie within a specified range of values). More-advanced approaches use different algorithms to identify gross errors and then eliminate them. Validation, per definition, does not close mass, energy, or component balances, and the range check is based on experience, which is itself based on observation of raw measurements.