Innovative Way of Improving S1 Production and Operation Using Real-Time Flowline Simulations of S1 Flowline Networks
Authors: Assavanives Boonyakorn , Nitayaphan Saranee , Watanakun Kantkanit , Kokanutranont Choosak , Srisuma Prakitr , Wanwilairat Sumbhath , Pechvijitra Pimpisa , Permpholtantana Tattanan , Rungfarmai Worawat , Thatan Naruedon 
Published: February 2023

It is always a challenge of legacy oil field to optimize the production with complex flowline network. Greater Sirikit (S1) oil field is operating with 400 wells, approximately, from 800 wells and over 100 flowlines. Therefore, flowline route adjustment is a crucial assignment to minimize pressure at wellheads for maximum production. Currently, the flowline simulations are used to model S1 flowline network, including crude production flowlines, gas lift flowlines, and water injection flowlines to check pressure drop and flowline size. All flowline management activities have been performed manually through numerous trial and errors and hand calculations to maintain production target. However, such method is time-consuming, and the results are not yet optimized.

A new innovative workflow is developed from the current flowline simulation models combined with online real-time data by Python programming. This innovative module imports data from the Production Data Management System (PDMS) to flowline simulation models and computes automatically through loops and conditioning algorithms commanded by Python toolkit. Methodology of an innovative module is to optimize production based on operating pressure from the wellheads to Flow Station (F/STN). The wellhead flowrate can be initially predicted from the well testing data while the operating conditions are from online data. The created module prioritizes the parameters that are bottlenecks for maximum production, e.g., high back pressure flowlines, or high water cut wells. Then, the module can selectively operate the wells and adjust the flowline routes with hill climbing optimization technique and conditioning algorithms to obtain the best operating scenario where the maximum production rate is achieved. Without human interference, an integration of an innovative module and flowline simulation models creates the seamless interoperability between BigQuery database and the simulation software. The accuracy and precision verification are a crucial process before module endorsement. Debugging and re-verification are repeated to ensure the validaty of the module. It is foreseen that the prospective pressure reduction will be at 10%, which potentially enhances more production. Economic evaluation has been carried out at expected production rate of S1 field. Flow line pressure management and allocation by program can reduce back pressure by 10 psi which increases production rate of 3840 Bbl/d from 400 active wells. The new work process also helps to reduce Full-time equivalent (FTE) and accelerates cycle time which makes work process agile, able to check anytime. The application intends to implement at S1 asset but not limited to. With the power and flexibility of Python ecosystem, it enables multistep workflows that can be shared, and the configuration can be extended to other assets or other operating oil and gas fields.