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Unleashing the Power of Big Data Analytics and AI to Modernize the Energy Industry

Energy industry overview

The O&G industry is not new to experiencing uncertainties. After the dramatic reduction in demands due to the pandemic-induced lockdowns in 2020, the industry is now expected to increase production and open its strategic reserves to meet the demand surge owing to several factors including sanctions on Russia, the largest global oil exporter. As a result, prices are sharply inflating, reaching an all-time high of USD 110 per barrel. With conditions far from ideal, O&G companies are under pressure to stay resilient and ready for every unprecedented situation.

In the past two years, the COVID-19-affected supply chain disruption and government-mandated emission reduction targets pushed companies to adopt strategies for greater operational automation and improved remote asset monitoring and control. Additionally, companies accelerated renewable energy capacity installations to strengthen their position in the future energy ecosystem. With digital technologies, energy companies are aiming to streamline and optimize their resource portfolios, develop smart approaches for energy transition, and meet their environmental requirements. The new developments and initiatives also boosted employment rates, as companies continue to seek new talents and train existing employees to work in the new operational environment.

At this transformational stage, energy leaders are focused on integrating their digital strategies to maximize returns from energy diversification and achieve net zero operations by 2050. The imperative now is to build critical capabilities by leveraging advanced technologies and develop concrete action plans to ensure seamless implementations for achieving operational excellence and adding value for their customers, while staying cost-competitive.

Toward achieving smart energy ambitions

The energy industry is riding the digital tailwinds and taking advantage of the rapidly evolving IIoT and cloud technologies, reduced equipment costs, and government incentives. But, companies are still facing bottlenecks in achieving their desired returns and operational efficiency. Following are key challenges that require their immediate attention:

  • Oil exploration - The search for new O&G reservoirs involves time and cost and error-prone processes of digging deeper into ocean beds and other difficult to reach places
  • Predictive maintenance - Equipment components operate in extreme environmental conditions, putting them at constant risk of failure
  • Smart integration - The protocols used to synchronize the current equipment with the old equipment fail to support inter-device communication, thereby hindering engineers to take predictive/proactive actions
  • Cybersecurity - The complexities of data flow between the different assets have put the infrastructure’s cybersecurity at risk
  • Production forecasting - The complex multi-stage O&G supply chain – upstream, midstream, and downstream – often cause unpredictable situations, such as overproduction or the inability to meet demands, leading to loss of revenue

The combination of all the challenges and growing innovative use cases have driven companies to seek their solutions in advanced technologies such as big data analytics, machine learning (ML), and artificial intelligence (AI).

The following sections have key use cases leveraging the three technologies for improving operational efficiencies across the different stages of the energy value chain.

O&G exploration

Upstream O&G companies are leveraging AI and ML in exploration, drilling, and extraction. In exploration, for instance, engineers are using AI-based models to determine reservoir rock properties, seismic attributes, and wire-line data to accelerate the evaluation of hydrocarbon recovery effectiveness.

A German O&G leader is using an exploratory AI-based tool to reduce the time spent in manual data search and improve decision making. AI/ML are also optimizing drilling precision, ensuring offshore worker safety, and remotely monitoring oil quality in offshore facilities.

Another leading US-based O&G company partnered with MIT to develop deep-sea AI robots. The robots are deployed into the ocean sub-surface to identify natural oil-seep areas on the ocean floor. The self-learning bot uses its intelligence to investigate and adapt to the abnormalities.

Maintenance operations

Heavy-asset industries such as O&G have long been seeking solutions to improve asset reliability. The earlier process of repair-when-broken only raised downtimes and caused heavy revenue losses. The diesel spill in Russia in May 2020 that polluted the adjacent rivers with 20 thousand tons of oil highlight the urgency for on-time maintenance. In the past few decades, companies are realizing the importance of implementing IIoT and reading sensor data to detect potential failures in the near future (see Figure 1).

ML algorithms have become a widely adopted technology for predictive and prescriptive maintenance. Engineers are gaining insights from ML platforms to significantly reduce downtime and maintain operational uptimes

A leading multinational O&G company applied predictive maintenance technology for their compressors, turbines, and pumps and saved several hundred million dollars.

An oil exploration and development company equipped their multi-phase pump with an AI-powered predictive maintenance system. In six months, technology helped the company prevent failures that earlier would cause a  production loss of USD 10 million.


Evolution of maintenance stategies in the oil and gas industry

0003.jpg1234Figure 1: Timeline showing the evolution of maintenance strategies in the O&G industry

The renewable energy generation sector has also deployed the predictive maintenance approach to enhance the overall efficiency of their power plants.

A company, which generated energy for nearly 20 million households, used ML-enabled predictive maintenance to bring down energy consumption by one percent by avoiding asset failures. While the number may seem small, the savings is high owing to the number of consumers involved.

Production optimization

Achieving operational efficiencies necessitates O&G businesses to improve controls over their oilfields and enable fast decision-making. Leveraging advanced analytics, ML, and AI can help them remove productivity bottlenecks, automate routine processes, and predict oil resource availability and demand trends.

An offshore O&G major leveraged ML algorithms to identify performance gaps in nine locations in Latin America and Africa. Using the insights, the company deployed a predictive maintenance platform across the locations, thereby reducing unplanned downtime by 20 percent and increasing oil production by over 500,000 barrels per year.

Taking it a step further, future-forward O&G companies are further enhancing productivity by leveraging digital twins for real-time operational monitoring, control, and simulations.

A leading O&G major in Saudi Arabia has implemented digital twin technology across their power plants using big data analytics, AI and other complementary technologies such as IIoT, robotics, cloud, additive manufacturing, and AR/VR. Along with a dedicated Fourth Industrial Revolution (4IR) Center, the company continuously develops advanced capabilities to improve power generation, ensure human safety, reduce time-consuming manual tasks, and stay resilient during emergency or unpredictable situations.

Virtual power plants

With renewables increasingly replacing O&G in energy generation, companies are investing in distributed energy models, which will allow them to generate power from both non-renewable and renewable sources. But the operation and management of the distributed energy resources (DER) are becoming more complex with increasing DER capacity and growing requirement for charging infrastructure for EVs. The global DER capacity is expected to grow to 3.2 TWh by 2030. As a result, companies in the UK, US, Australia, South Korea, Japan, and Germany are leveraging virtual power plants (VPP) to improve grid reliability and stability, which is expected to expand the VPP market from USD 565 million in 2021 to USD 3.3 billion by 2030. As a decentralized power generation model, VPPs make energy distribution flexible, affordable, and reliable

With AI, VPPs can improve

  • Load and generation forecasting (especially from the uncertain photovoltaic cells)
  • Network planning
  • Grid optimization
  • Enhanced power quality management across all the nodes – including energy producers, distributors, consumers, and electric vehicle charging points

Smart meters

The increased use of smart meters along with DERs is enabling the centralization of energy distribution and consumption operations.

A US-based utility company found that retrofitting homes with the advanced metering infrastructure enabled them to save 3.5 times more energy than earlier. The end-to-end monitoring not only ensures transparent supplier-household communication, but by leveraging big data analytics and machine learning the data from smart meters allows detection of anomalies in the meter. Companies are also able to predict the upcoming energy consumption levels.

Smart meters have the potential to expand customer engagement further. AI-powered tools are enabling personalization through consumption analysis.

One such tool, developed by Carnegie Mellon University, compares the offers of different energy providers and recommends the best deals to the customer at that moment. In time, the tool self-learns the customer’s preferences and takes switching decisions on its own. The tool also reports on the demand requirements to the producer for supply adjustment.

AI has already become an integral part of energy companies with its capabilities of extracting measurable value from all domains of the energy ecosystem. Be it transitioning into renewables for reducing carbon footprint, optimizing grid operations, managing the demand-side, discovering materials, or coordinating between the disparate and distributed assets. AI’s impact has the potential to further boost value and RoI in the future with autonomous level innovations. But while new applications continue to show tremendous opportunities, adoption remains limited.

Address AI adoption challenges with the right approach

While energy companies have successfully implemented short-term solutions, they remain unclear about long term priorities. A McKinsey article highlighted five factors that could hinder or boost AI adoption – data, partners, people, deployment, and governance. The following considerations will be necessary to expand the impact of AI in modernizing the energy industry:

  • Companies will have to democratize their data so that it can be used at the front line
  • Alliance with the right technology partners can help accelerate innovations and give the right future direction
  • Recruiting or retaining the right people will go a long way in sustainably improving operations
  • Developing a robust deployment strategy and building on them will drive successful use cases
  • Governing remains the crux of any technological adoption, where senior management must have a clear vision and roadmap for the transformation

While AI may be portrayed as a magical solution for every energy industry challenge, it’s not. But, the growing complexity, scale, and exigency of energy transition worldwide demand leaders to quit ignoring the advantages of AI and ML. If correctly deployed, AI can become the key lever for creating value, expanding consumer reach, reimagining new services, building resilience, and promoting environmentally clean and affordable energy systems.

Transform with Xebia

Xebia has a robust ecosystem of products, services, competence builders, accelerators, alliances, and solution partners to address the current challenges of O&G and renewable energy companies. Be it in integration, data engineering, Data Lake, RPA, data science, ML, or AI, we continuously strive to explore new technology frontiers and build solutions for delivering sustainable value. Our data intelligence experts are already engaged in solving problems in the energy industry. And we are witnessing first-hand the evolution and successes of AI/ ML every day. It is time energy companies take the plunge with AI/ML and capitalize on the opportunities to ensure sustainable transformation.

For all your data, AI, and analytics requirement, get in touch with us today!

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