How Advanced Analytics improve Margins at Natural Gas Processing plants?

Digitalizing process operations with analytics and decision support tools is necessary to succeed in today’s volatile environment, enabling integrated process and economic modelling to predict the optimal target points accurately.

Posted: June 6, 2018

Oil and Gas

How Advanced Analytics improve Margins at Natural Gas Processing plants?

Improved fracking technology in oil and gas fields is creating an unprecedented boom in natural gas production, exerting tremendous pressure on natural gas prices globally. As recovery of Natural Gas Liquids (NGL) from the gas streams drives revenues for natural gas processing plants, any favourable market climate for encouraging production may adversely affect supply-demand equilibrium. Excess NGL may drive prices down in the short term. Any shifts in market values of NGL and natural gas may impact the fractionation spread, affecting the profitability of the gas processors.

To stay ahead of the game, optimizing recovery of Natural Gas Liquids is imperative for gas processors to sustain and profit in this competitive market. As the optimal processing conditions of NGL extraction –either ethane recovery or rejection mode- is highly dependent on the current pricings of Natural Gas and NGL components, incorporating economic data into process models and optimization objective functions enables processers to make informed operating choices that can significantly improve processing margins, resulting in higher ROI.

Natural Gas Liquids Spot Prices

Performing “What-if” scenario analysis to determine the optimal process conditions for maximizing profitability can be time consuming and inaccurate without the right technology to keep these models current. Digitalizing process operations with analytics and decision support tools is necessary to succeed in today’s volatile environment, enabling integrated process and economic modelling to predict the optimal target points accurately. With the right platform, this not only helps gas processors reduce processing costs, but also improves their organizational agility to respond to changes in feedstock quality, product pricing and specifications.

Steps to optimize NGL recovery

As extraction of NGL from the natural gas stream involves a few processing steps from compressing, to treating, to cryogenic recovery, to de-ethanizing or de-methanizing, a complete plant model is key to optimizing NGL recovery. Operating constraints, including NGL and sales gas product specifications, as well as processing economics, are dependent variables and must be modelled simultaneously with maximum profit as the final objective:

a) Reduce Process Variability

Reducing process variability allows the process to run closer to operating constraints, thereby improving yields and product quality. Advanced Process Control (APC) considers process dynamics, interactions, constraints and economics in real-time. The analytics identifies the relationships in a process from historical data and develops models to predict future process conditions, making necessary adjustments to increase yields while reducing energy consumption and maintaining product qualities.

Max limitMax limit

b) Real-time Process Optimization Advisory

While Advanced Process Control attempts to determine the optimal process conditions based on statistical and linear optimization algorithms, any non-linearities or model mismatch due to changes in equipment efficiency and availability, economic conditions and abnormal operations may lead to suboptimal set points. These changes often require more rigorous modelling of the new operating constraints and economic data and the optimal set points must be refreshed. Digitalizing the operations with Real-time Optimization (RTO) analytics effectively and efficiently updates the model automatically by recalibrating the process model, ensuring optimized operating conditions 24/7 based on the prevailing conditions while minimizing human intervention.

Frequency of Optimization

Automated Workflow to close the performance loop

Insight and optimization advice do not create value until action is taken to adopt and implement the new optimization set points. Accurate, reliable and timely information must be channelled not only to the engineers but also to executives who are empowered to make operating decisions that impact the bottom line. This is where enablers like workflow management are critical to ensuring that actionable data is executed upon to seize any profit improvement opportunities.

Automated workflow

Benefits of implementing Analytics and Decision Support Tools

Major natural gas processors have already embarked on the digital transformation journey, leveraging Predictive Control and First Principles models to accurately determine and control the optimal operating targets in real time based on the most current plant conditions and profitability, optimizing recovery of natural gas liquids. They have since reaped significant benefits from improved processing margins and asset utilization. They are now able to make better and more informed decisions through improved plant-wide operational visibility, and improve ROI through better asset utilization and an optimized process, enabling them to achieve the next level of operational efficiency–up to 5% improvement in processing margins.

Are you ready to enable Advanced Analytics in your enterprise to gain higher profits? Register for our Webinar now.

Watch our on-demand webinar to learn the importance of advanced analytics in process modelling of natural gas processing plants and how you can improve processing margins using real-time analytics, combined with decision support tools, to succeed in this volatile environment. Register now!

Written By
Eddy Lek

Eddy Lek is the Global Industry Marketing, Senior Manager for Energy. He is responsible for leading industry marketing campaigns in Oil and Gas. With over 15 years of experience in varying roles from sales to product management and marketing, he is a strong advocate for leveraging technology to improve efficiency and reduce waste. He has a MBA from National University of Singapore and an B.Eng from Nanyang Technological University.

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