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Production Test data is a critical parameter for several production operations and reservoir management workflow. High quality Production test data is vital for better understanding of the flow behavior and rates to ensure optimized production and maximize the asset value. Current Production Testing practice in oil industry includes separator testing or MPFM at well heads or at degassing station. However, the frequency of testing varies between one to three months for each well which may not sufficient to realize the full potential of Digital Oil Field (DOF) workflows. Virtual Flow Metering (VFM) technique along with well model results shall provide continuous well rates that would significantly improve the quality of decisions made through the production workflow. This brings in a varied Production testing environment for different well categories and types. In order to continuously provide the real time production parameters to the DOF workflows, it is essential to integrate the different Production testing techniques through an Integrated Production Testing Framework. In this paper, the authors discuss an Integrated Production Testing Framework that comprise of validated real time and historical data, integrated workflows and the enabling technologies that includes calibrated well models, trained neural network models and visualization tools. Production test data obtained using traditional methods (PTS) and MPFM will be at low to medium frequency. VFM using neural network model estimates the flow rates continuously between the actual tests at a high frequency. This framework is suitable for production, injection wells that are installed with MPFM, PTS, water cut meters and covers different production testing scenarios Like PLT, ESP testing, Long-term MRT and step rate injectivity testing. The framework enables implementation of continuous estimated rates, exception based alarms, automatic well test validation, track well test operations, guidance for reservoir monitoring program, KPI monitoring, precise back allocation leading to better production optimization and reservoir management across oil and gas producing assets. This paper discusses an integrated approach to manage different Production testing methodologies to streamline the usage of the data for different workflows..
Production Test data is a critical parameter for several production operations and reservoir management workflow. High quality Production test data is vital for better understanding of the flow behavior and rates to ensure optimized production and maximize the asset value. Current Production Testing practice in oil industry includes separator testing or MPFM at well heads or at degassing station. However, the frequency of testing varies between one to three months for each well which may not sufficient to realize the full potential of Digital Oil Field (DOF) workflows. Virtual Flow Metering (VFM) technique along with well model results shall provide continuous well rates that would significantly improve the quality of decisions made through the production workflow. This brings in a varied Production testing environment for different well categories and types. In order to continuously provide the real time production parameters to the DOF workflows, it is essential to integrate the different Production testing techniques through an Integrated Production Testing Framework. In this paper, the authors discuss an Integrated Production Testing Framework that comprise of validated real time and historical data, integrated workflows and the enabling technologies that includes calibrated well models, trained neural network models and visualization tools. Production test data obtained using traditional methods (PTS) and MPFM will be at low to medium frequency. VFM using neural network model estimates the flow rates continuously between the actual tests at a high frequency. This framework is suitable for production, injection wells that are installed with MPFM, PTS, water cut meters and covers different production testing scenarios Like PLT, ESP testing, Long-term MRT and step rate injectivity testing. The framework enables implementation of continuous estimated rates, exception based alarms, automatic well test validation, track well test operations, guidance for reservoir monitoring program, KPI monitoring, precise back allocation leading to better production optimization and reservoir management across oil and gas producing assets. This paper discusses an integrated approach to manage different Production testing methodologies to streamline the usage of the data for different workflows..
Exploration and Production companies are increasingly instrumenting their fields with the objective to proactively monitor and surveillance of its wells, reservoir, and facilities for safe and better operations. With this scenario, there is ever increase in the volume and variety of data being generated to support workflows such as real time drilling operations, production surveillance and reservoir monitoring. Data Analytics enables to get most value out of the vast volume of data being generated. In order to extend the present limits of digital oil field envelope from operations monitoring, companies need to harness data analytics to identify patterns, trends, correlation, forecasting out of its vast petroleum data. This includes exception based surveillance, case based reasoning, and condition based monitoring in order to facilitate advanced monitoring, and to support tactical and strategic decision-making process. To establish data analytics environment it requires systematic way of capturing, storing and managing data, integrating and embedding data analytics into the mainstream sub-surface, drilling, production and operations workflows which includes integrated reservoir monitoring, drilling and production optimization. Data analytics technology blends traditional data analysis with sophisticated algorithms and business rules for processing large volumes of diverse types through an Integrated Data Analytics Environment (IDE). Data governance is a vital part of IDE to ensure there is clear ownership and responsibilities as the accuracy of the results are as good as the quality of the data. IDE environment enables systematic harvesting of operational event, lessons learnt and best practices to enable knowledge based operations decisions. The integrated data analytics platform enables integrating E&P workflows and data analytics to enhance operations monitoring, predict well productivity, identify performance patterns and KPI, improve reservoir recovery factor, and predict equipment failure, towards achieving the objective of continuous optimization of oilfield performance.
Gas condensate fields present unique challenges regarding data acquisition, data quality, exception-based surveillance, flow modeling, nodal analysis, well testing, allocation, and visualization. Although existing tools and methods address many of these aspects, it is possible to streamline processes and explore increased production efficiency methods. This paper addresses these challenges; it presents a case study of an intelligent control system implementation for a gas-condensate field based on a unified data model, integrated modeling, and cross-domain workflows. This paper presents a transformative, intelligent, and automated work process, referred to here as "smart workflows." As part of these workflows, virtual gauges are used that are based on inflow models and lifts, adjustable valves, and modular networks. The workflows are implemented on a truly open end-to-end platform that enables the coupling of multiple databases, streamlining of data for an integrated analysis of the measurements and model calculations, and ascertaining the mismatch between the two. The workflows also initialize adaptive self-tuning procedures. The smart workflows enable engineers to achieve various improvements, including an integrated structure of process data model to enable quick access to validated data, monitoring and control functions to a gas-condensate field in real time, and reduced downtime and operational costs. The smart workflow also supports functions that include collection and verification of measurement data, configuration of the integrated solution component models, evaluation of the action of root causes, and planning of operation scenarios. As part of the implemented system, an integrated information system data structure sets the degree of relatedness of tasks, each of which can be initialized depending on work situations and/or operator commands. Such comprehensive analysis of the data provides reliable integrated system configuration parameters of the model, which increases the accuracy of the calculations used in the optimal planning of the operational scenarios.
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