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Wells in Tengiz and Korolev oil fields are equipped with data transmitting devices, which provide real-time process data used by Production engineers for continuous production monitoring and identification of unusual process conditions. Monitoring and analysis of each well performance becomes a tedious process with growing well inventory. Up until recently, real-time data from wellsite transmitters was not used to its full potential to simplify and automate well performance analysis. To improve the quality of daily well performance monitoring and detection of abnormal process conditions, sets of data rules have been developed to create alerts and screens with real-time process data managed by exception. These alerts and screens help to identify malfunctioning equipment and changes in operating conditions. Timely evaluation of critical conditions helps to proactively prepare a mitigation plan and prevent unscheduled well shutdowns. Data management by exception allows automatic filtering of big data sets and draws attention only to wells with deviations from the stable operating regime. Detailed review of highlighted well conditions helps to differentiate between malfunctioning equipment and actual changes in operating conditions. Fast identification of the issues allows taking preventative actions to maintain process stability of each producing well. Implementation of these tools significantly reduced number of unscheduled well shutdowns due to leaks in Surface Controlled Subsurface Safety Valve (SCSSV) hydraulic system and pneumatic valves control system. The screens also help to identify malfunctioning equipment including pressure and temperature gauges, pressure downhole gauges (PDHGs) and multiphase flow meters (MPFMs), as well as flow assurance issues such as hydrate formation. Developed data rules can be useful for any field equipped with data transmitting devices. This paper aims to share the best practices of using real-time operational data analytics to identify malfunctioning equipment, changing operating conditions and other process related issues to maintain stable production process.
Wells in Tengiz and Korolev oil fields are equipped with data transmitting devices, which provide real-time process data used by Production engineers for continuous production monitoring and identification of unusual process conditions. Monitoring and analysis of each well performance becomes a tedious process with growing well inventory. Up until recently, real-time data from wellsite transmitters was not used to its full potential to simplify and automate well performance analysis. To improve the quality of daily well performance monitoring and detection of abnormal process conditions, sets of data rules have been developed to create alerts and screens with real-time process data managed by exception. These alerts and screens help to identify malfunctioning equipment and changes in operating conditions. Timely evaluation of critical conditions helps to proactively prepare a mitigation plan and prevent unscheduled well shutdowns. Data management by exception allows automatic filtering of big data sets and draws attention only to wells with deviations from the stable operating regime. Detailed review of highlighted well conditions helps to differentiate between malfunctioning equipment and actual changes in operating conditions. Fast identification of the issues allows taking preventative actions to maintain process stability of each producing well. Implementation of these tools significantly reduced number of unscheduled well shutdowns due to leaks in Surface Controlled Subsurface Safety Valve (SCSSV) hydraulic system and pneumatic valves control system. The screens also help to identify malfunctioning equipment including pressure and temperature gauges, pressure downhole gauges (PDHGs) and multiphase flow meters (MPFMs), as well as flow assurance issues such as hydrate formation. Developed data rules can be useful for any field equipped with data transmitting devices. This paper aims to share the best practices of using real-time operational data analytics to identify malfunctioning equipment, changing operating conditions and other process related issues to maintain stable production process.
There are often situations when readings of a large number of sensors installed at the field are used as needed, rather than on a systematic basis. The paper is devoted to the experience of building and using a Digital twin (DT) of the oilfield, history matched using instrument readings at the main nodal points from the well bottom to the tank farm. Digital twin is used to solve practical problems of selecting measures to increase production, specifying the maintenance program and chemical injections, overall allocating production by wells without direct measurements of production flow and forecasting in different time horizons the entire field production.
Monitoring real time well performance is critical to not only maintain safe and reliable operations, but it is also crucial in meeting the yearly production targets. To track the current well rates Tengizchevroil (TCO) utilizes multiphase flowmeters (MPFM) which are installed on a group of wells (meter stations), usually consisting of 8-12 wells. However, there are some minor constraints in the current MPFM well testing process – wells can only be tested one-by-one with minimum 2-hour test durations, meaning that real time rates are only represented for one well only in the time of testing. When wells are not on test, TCO uses the theoretical rate (FTP rates or so called "flowing tubing pressure rates") determination process. This paper describes the application of digitalization tools in daily operations at Tengiz for production tracking and allocation process. Prosper models are created and maintained for each well in TCO's operating well stock which are stored in Model Catalogue (DOF's model repository). Each month these Prosper models are updated and calibrated based on updated and reviewed well test data – engineers usually set new reservoir pressures or productivity index estimations to match the recent well test results. Revised models are later updated into the GAP integrated network model in Model Catalogue, and an automated workflow in DOF generates updated VLP/IPR tables with well rate trends as a function of varying wellhead pressure. The VLP/IPR tables are later uploaded into SCADA for integration with the real time monitoring tool, where based on current wellhead pressure the FTP rates are computed on a real-time basis for Operations to monitor. The FTP update process also serves as a foundation for Field Production Allocation – a relationship of the cumulative daily production sourced from Plant intake gauges (actual Field production) to cumulative theoretical (FTP) Field rates. On average, TCO strives to keep the allocation factor in the range of 0.90-1.10 (+/- 10% error). This allocation process is done in Energy Components (EC). The FTP and Allocation processes are foundational for short-term and long-term forecasting as the Well performance is used for day-to-day operations planning, maintaining, and ensuring enough production is feeding the Plants. The end-to-end workflow is currently being streamlined to further automate where it makes sense; for example, leveraging DOF's capabilities to automate generating well test records from live real-time data, implementing a management by exception plug-in for the well test QA/QC, and semi-automating the Prosper model calibration process.
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