Using Real-Time Data and Integrated Models to Diagnose Scale Problems and Improve Pump Performance
Authors
Baraa Sayyar Al-shammari (Kuwait Oil Company) | Nitin Rane (Kuwait Oil Company) | Shareefa Mulla Ali (Kuwait Oil Company) | Aala Ahmad Sultan (Kuwait Oil Company) | Salem Hamad Al Sabea (Kuwait Oil Company) | Meqdad Al-naqi (Kuwait Oil Company) | Mukul Pandey (Weatherford) | Fernando Ledesma Solaeche (Weatherford)
Publisher
SPE - Society of Petroleum Engineers
Publication Date
March 18, 2019
Source
SPE Middle East Oil and Gas Show and Conference, 18-21 March, Manama, Bahrain
Paper ID
SPE-194847-MS
Abstract
The Kuwait Integrated Digital Field project for Gathering-Center 01 (KwIDF GC-01) at Burgan Field acquires real-time data from wells and processing facilities as input for its production-surveillance program. Live data from the field is fed into an integrated production model for analyzing and optimizing pump performance. An automated workflow process generates alarms for critical well and facility parameters to identify wells with potential scaling issues. KwIDF workflows are integrated with updated well models to visualize the effect of scale build up on the wellhead performance and thereby assist in quantifying the associated production losses caused by scale deposition. A sensitivity analysis is also performed to identify current and optimal pump operating conditions and prioritize scale cleaning jobs.
The exception-based surveillance of key real-time parameters for wells utilizing electrical submersible pumps (ESPs) in Burgan field has significantly improved diagnostics of scale deposition at wellhead chokes and flowlines. Automated workflows calibrate an integrated production model in real-time, which enables engineers to run a quick analysis of current pump operating conditions and make a proactive plan of action. The application of real-time data and automated models has aided the operator's production team in making informed and timely decisions that enable them to run pumps at optimal operating conditions, with the result that they are able to sustain well production at target levels.
This paper describes an innovative approach to applying real-time data and integrated models in an automated workflow process for enhancing capabilities to diagnose scale deposition in the surface flow network. Examples are presented to demonstrate the application of integrated technology for identifying scaling at wellhead chokes and flowlines and prioritizing a scale removal program for optimizing pump performance.