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To face new hydrocarbon production metering systems and back allocation challenges, Total E&P Qatar and its Research Center (TRC-Q), located in the Qatar Science and Technology Park (QSTP), test novel approaches to improve current production metering system in the Al-Khalij field. This field is operated under a Production Sharing Agreement (PSA) with Qatar Petroleum (QP). A field pilot, which involves three platforms offshore, is currently conducted to test a Data Validation and Reconciliation (DVR) software. This tool is used for production accounting, monitoring and as an alarm system related to equipments’ failure. The main outcome to be expected from this pilot will be an automatic validation of on-line production data before their final recording in the database. The key advantage of the DVR approach is to take into account all the information redundancy and data uncertainties. In the DVR approach, a realistic uncertainty is associated with the information (measurement / model parameter) and a statistical method is applied to re-estimate and improve simultaneously the information and its subsequent uncertainty. The current status of this study is presented along with improvements and challenges. A few examples of on line monitoring of Al-Khalij Wells’ production are presented through a comparison between the DVR on-line virtual meter outputs and the on-site production tests. The results, obtained from sensitivity analysis are also discussed to assess the improvements achieved by applying the DVR approach.

Multiphase flow occurs in almost all oil and gas wells and production installations. The past three decades have seen intense efforts to develop empirical correlations and modelling tools to improve the ability to predict multiphase-flow behaviour with greater accuracy. The empirical approach typically involves flowing fluids at carefully measured flow rates through a pipe, observing the flow pattern, and measuring liquid holdup and pressure drop. Using the measured data, empirical correlations are developed for predicting flow patterns, liquid holdup, and friction factor, and a pressure gradient equation is developed that uses these empirical correlations. In its operations on Al-Khalij field, which is operated under a Production Sharing Agreement (PSA) with Qatar Petroleum (QP), Total E&P Qatar experience multiphase flow in main production lines, with a resulting pressure loss which has to be accurately predicted. As part of the Qatar Science & Technology Park (QSTP), Total Research Centre - Qatar (TRC-Q) has research collaborations with different partners. In this work, we present the results of a joint research project between the Petroleum Engineering program at Texas A&M University in Qatar and the Smart Metering Project team from TRC-Q. A literature review of available multiphase flow correlations has been performed first. Secondly, limitations and range of applicability of these correlations were evaluated. We then identified the most applicable correlation to specific conditions of a pipeline in Qatar's Al-Khalij field. This included gas-oil-water flow in a large-diameter multi-kilometre-long pipeline which passes through a hilly terrain (slightly deviated from horizontal). Our Analyses show that Beggs and Brill set of equations estimates pressure gradient with the least error among other correlations for these conditions. To improve the correlation's accuracy, we divided the pipeline into several segments with an optimum length to minimize the error and ensure computation efficiency. We developed a code to implement this correlation to simulate the multiphase flow inside the pipe. We could obtain pressure and phase flow rates along the pipeline. Also, a sensitivity analysis of some key parameters was performed. The model provided good agreement with measured pressure at the end of the pipeline.

Total E&P Qatar seeks to constantly improve its operations performances. These performances are associated with such indicators as health, safety & environment (HSE) performance, oil production, water management, production costs and energy efficiency. A key tool for reaching this goal is the enhancement of data monitoring and management technology. The smart metering pilot project is run at Total Research Centre - Qatar (TRC-Q) at Qatar Science and Technology Park in close cooperation with Total E&P Qatar Operations and TOTAL Research in France. At TRC-Q, we are testing a game-changing technology based on data validation and reconciliation (DVR). We integrate real-time measurements and virtual metering in an online monitoring system on our production platforms. The originality compared to equivalent systems is the use of the DVR approach, which consists of using a statistical model to manage uncertainties associated with each measured parameter and of quantifying error propagation. The DVR process allows an automatic real-time correction of both measurements and model parameters, on the basis of their allocated uncertainty and thanks to the information redundancy. Consequently, the resulting output data is more consistent with the available data and associated with a reduced uncertainty. So, for the DVR to be effective we seek to increase the data redundancy and provide high-quality modelling of physical and thermodynamic phenomena occurring over the whole production process. The achievement so far is a pilot hourly metering system based on online measurements. These measurements and modelling parameters are automatically linked on the basis of their redundancy and the Operations Department is provided with a customised report containing the corrected parameters and their associated uncertainties. We also keep looking for new relevant information to incorporate into the system and for improvement in the quality of the mathematical model associated with each equipment (pumps, flow meters, etc.) model in order to improve the reliability of the output. The results that will be presented cover the initial promising results of the pilot for the virtual metering of oil, water and gas production and the associated uncertainty quantification.