Maximize Oil and Gas Pipeline Efficiency with Integrated Digital Twins

Transform Pipeline Operations to Boost Performance and Safety.

Posted: November 15, 2019

Oil and Gas

Maximize Oil and Gas Pipeline Efficiency with Integrated Digital Twins

Moving crude and refined products over pipelines is still one of the most economical means of transportation from oil and gas fields to processing plants/terminal. According to Market Research Future, the global oil and gas pipeline market is expected to grow at a CAGR of 5% in the next few years due to increasing demand of natural gas and crude oil from developing countries. As pipeline operators earn revenues from transporting oil and gas on their pipelines either for a fee or through crude differentials, the strong volumes are driving unprecedented profits for their operations in recent years.  However, any changes in market dynamics such as favorable market climate for encouraging investment in new pipeline capacity or a sudden deterioration of global economy may adversely affect supply-demand equilibrium down the road.

To sustain and thrive in uncertainty, more companies are looking towards digital transformation to improve operational efficiency and reduce costs without compromising on safety performance.

Biggest opportunities in Digital Transformation

Advanced analytics and machine learning are often cited by executives as the next biggest opportunities in digital transformation. To explain in layman’s terms, analytics simply takes data from assets, analyses those data and builds a model using either statistic or machine learning algorithms to predict future operating behaviours of assets and processes, with the aim of reducing unplanned downtime and improving pipeline utilization.

To do this well, a digital transformation plan has to begin with quality and trusted data.  It is the foundation of every business value creation.  According to a report by McKinsey, companies often get so excited about Big Data, Analytics and Artificial Intelligence that they tend to focus and emphasize too much on the single technology elements of the ecosystem and end up neglecting other critical building blocks such as quality data, business value and people,  therefore seeing limited successes in their digital transformation initiatives.

Begins with an integrated Digital Twin – A single trusted view of assets

In order to achieve higher success, companies must first identify the series of components within the “insights value chain” that could lead to better capturing of the business value from big data along their digital journey. Building an integrated digital twin to digitalize assets and facilities is the first step of the journey.

At the initial phase, a 3D model is created that allows multi-disciplinary teams to interact with the data visually. This model is then tagged with all the necessary attributes and engineering documentation. Through a robust information gateway, information and data around the asset are extracted from disparate data sources and validated for accuracy against known standards to create view-able renditions of documents and drawings. This not only acts as a data validation layer to ensure that all data meet the correct standards throughout the asset lifecycle, but also breaks down information silos that had existed within the organization. The integrated digital twin allows users to have complete control of their asset information throughout the entire asset lifecycle. 


Reaping the rewards of an integrated digital twin

As the operational life continues, the digital twin is updated automatically, in real time, with current data, work records, and engineering information, to optimize maintenance and operational activities. Engineers and operators can now easily search the asset tags to access critical up-to-date engineering and work information, diagnose the health of a particular asset.  Previously, such tasks would take considerable time and effort, and would often lead to issues being missed, leading to failures or production outages. With the integrated digital twin, operational and asset issues are flagged and addressed early-on, and the workflow becomes preventative, instead of reactive.  Operational performance benchmarks such as pipeline throughput, energy consumption and others can be easily performed to uncover gaps and improve pipeline efficiencies, leading to significant improvement in not only business performance, but also organizational agility.


Turning to Analytics and Machine Learning

The real-time process data from the digital replica, in turn, can be fed into analytics and simulation with the ultimate goals of optimising overall pipeline throughput, process conditions and even predicting equipment failures ahead of time. With the advancement of analytics in capturing and preserving higher-fidelity data for more accurate models of real-world pipeline operations, this enables the expansion of more predictive applications such as modelling and predicting of liquids/gas pipeline flows to optimize production, improve leak detection and maximize assets reliability.

Are you ready to take the first steps to enable Digital Pipeline 4.0 in your company to gain competitive advantage today? 

Watch our recently concluded webinar - ”Transform Pipeline Operations with Integrated Digital Twin to Boost Performance and Safety” and experts outline the Digital Oil and Gas Pipeline strategies, highlight customer use cases and explain how you can maximize business performance from operations to maintenance leveraging an integrated Digital Twin, including predictive analytics and pipeline management tools.

Watch Now to start your Digital Transformation Journey today.

Watch the Webcast

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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