Data Analytics
round this time last year, oil and gas executives were contemplating how Big Data analytics could become a source of significant competitive advantage for their companies. As the industry made slow, yet steady progress toward digital transformation, corporate discussions centered on how machine learning (ML), artificial intelligence (AI) and automation could save the upstream sector billions of dollars.
Fast forward to today, the industry is knee-deep in one of the worst downturns in history. The demand destruction has caused massive well shut-ins, historic layoffs and, consequently, remote operations have become the new normal. The executives are now discussing survival strategies using advanced digital solutions and are revisiting the use of historical data for resilience and recovery.
A recent study by McKinsey & Co. stated, “It is uncertain when the current perfect storm impacting oil and gas operators will pass…What is certain, however, is that only innovative operators with superior operating models will come out of this crisis prepared to cope with volatility and to sustain future growth.”
round this time last year, oil and gas executives were contemplating how Big Data analytics could become a source of significant competitive advantage for their companies. As the industry made slow, yet steady progress toward digital transformation, corporate discussions centered on how machine learning (ML), artificial intelligence (AI) and automation could save the upstream sector billions of dollars.
Fast forward to today, the industry is knee-deep in one of the worst downturns in history. The demand destruction has caused massive well shut-ins, historic layoffs and, consequently, remote operations have become the new normal. The executives are now discussing survival strategies using advanced digital solutions and are revisiting the use of historical data for resilience and recovery.
A recent study by McKinsey & Co. stated, “It is uncertain when the current perfect storm impacting oil and gas operators will pass…What is certain, however, is that only innovative operators with superior operating models will come out of this crisis prepared to cope with volatility and to sustain future growth.”
“Basically, the pandemic has triggered a shift from remote monitoring to increased remote operations,” Pandita said. “Data analytics has changed the view of how and where remote operations can be applied. With the advent of data analytics, process simulation and advanced process control, we are now able to build a digital twin to monitor upstream facilities. Data analytics has allowed implementing exception-based rules to detect anomalies and perform activities such as inspections. Similarly, field operations have become more intelligent with assistance for workers available remotely.”
Other experts expressed a similar sentiment.
Brennan added the industry is still in an early phase of witnessing what could be a “seismic shift” that the role of data analytics will play in the operations of the upstream sector.
“We are a highly innovative and resilient industry,” he said. “With the challenges brought on by the pandemic and the downturn, we are going to see about five years’ worth of transformative activity over the next 18 to 24 months.”
Brennan also pointed out that the pandemic and the subsequent challenges have created a significant opportunity for the upstream industry in the area of digital transformation.
“There is so much opportunity that remains in the upstream sector to apply AI, machine learning and these types of technologies to improve efficiencies and get more production out of the existing assets in the portfolio,” he said. “It’s also an opportunity for the sector to truly lean into changing trends in the labor market. Companies should upscale jobs like petrophysicists, petroleum engineers and really develop career paths like those of data scientists.
“The opportunity is there not just for operators, but for everyone who has evolved within the supply chain of the upstream sector to be more open in terms of how we are sharing data, to be more open to embracing new ways of working [and] to be more open to embracing technologies like ML and AI to improve decision-making.”
“Specifically over the past three to five months, we have been working hand in hand with our drilling services team to apply the BHC3 technology to improve the productivity and outcomes that they can deliver to their clients,” Brennan said.
“You need processes that are driven analytically,” he said. “In addition, you need disconnected business systems communicating and being driven analytically, so the systems themselves are responding to business dynamics. An example would be your ERP [enterprise resource planning] system communicating with production automation systems to check capacity for additional production to take an order and then executing the order. With ABB’s Genix platform, we can help customers do just these types of things. We are taking reactive businesses to outcome-driven businesses.”
Genix is a scalable, smart analytics and AI-driven platform and suite that makes data utilization easier by bringing together data with domain knowledge, technology and digital capability.
Hernandez said that in the current uncertain environment, it is human nature to immediately go to business instinct.
“Although instincts are important in business, data help you understand if what you are hearing and seeing lines up with your business,” he said. “The data bring everything into perspective. You must have good systems in place that help you visualize the data in a way that is meaningful to you.”
While the pandemic has brought a greater sense of urgency in accelerating digitalization efforts to unlock new operational gains, it has also given “a new mandate-cum-challenge to put people at the core and prioritize their health and safety,” the report stated.
Given that capital is the most constrained resource today, how should an oil and gas company plan its digital transformation in the current environment?
Schlumberger and IBM discuss recent collaborationn this exclusive video interview with Hart Energy’s Faiza Rizvi and Len Vermillion, executives from Schlumberger and IBM discussed a recent collaboration between both companies to accelerate digital transformation across the oil and gas industry. The joint initiative will increase global access to Schlumberger’s E&P cloud-based environment DELFI and cognitive applications by leveraging IBM’s hybrid cloud technology.
Even though DELFI offers access to several public clouds, almost half of oil and gas companies are unable to easily access these cloud platforms due to constraints around data sovereignty, reach of public clouds and architectural choices. The collaboration allows for workload portability, orchestration and management across multiple infrastructure environments allowing the DELFI environment to be available across a variety of infrastructure choices.
“We are working together to build a joint Open Subsurface Data Universe [OSDU] offering, which is an open-source collaboration to build a common data environment for the industry,” explained Trygve Randen, global director of digital subsurface solutions with Schlumberger. “Today there are a lot of efforts to bring OSDU to public cloud. With this collaboration, we are aiming to offer OSDU with a full set of hybrid deployment options…and a common data environment that every operator in any kind of jurisdiction will be able to implement.”
Manish Chawla, global managing director of energy and natural resources with IBM, said the joint offering is a great “entry point” for companies with large datasets looking for a flexible journey to the cloud that they can manage and control at their pace.
Randen added that in the process of gathering and processing data, it is important to liberate data from the silos and from the confines of different applications.
“We saw the opportunity to connect multiple data repositories into one platform. This will reduce the amount of duplication and have workflows that pan across multiple applications more easily. Also, one of the benefits of bringing the cloud concept is the availability of data all the time for all the workflows. With the cloud deployment, we want to ensure comprehensive access to data, which will give better insights and better decisions to avoid risk and optimize operations,” he said.
Even though remote projects were being implemented well before 2020, the pandemic has accelerated the urgency to mitigate HSE exposure for both off- and on-field employees without disrupting operations. Employing cloud platforms to help experts working from home analyze and visualize operations, leveraging edge analytics to analyze the data at the place of data creation itself, and providing augmented wearables for the onsite workforce have become the bare minimums now, and these will likely become permanent fixtures in the post-pandemic world.
“We approach this problem by building and utilizing out-of-the-box asset models based on an in-depth understanding of what brings critical equipment down, almost at a component level,” he said. “This allows running intelligent analytics despite having insufficient data. Adopting a hybrid approach that combines first principle models such as thermodynamic models with data-driven models allows us to overcome this challenge of not having enough data to perform complicated operations like measuring asset performance.”
Another challenge, Pandita noted, is that data from different parts of the value chain are trapped in different places, which need to be integrated to create value.
“Our approach involves awareness of end-to-end business processes, which now allow us to overcome the limitations of data silos,” he said.
Halliburton’s Mijares agreed that access to the right data quality is crucial.
“Availability of technology is broadening the area of data sources for our company,” he said. “The boundaries of what used to be well-defined silos for management, engineering, operations, maintenance, business planning and so on are starting to blur because of better access to data with new technologies.”
“Our customers are just struggling to know where to start,” he said. “They have purchased point solutions and don’t know how to bring it all together. At ABB, we like to help our customers take a step back and help them define where it is that they want to go and break the digital journey into steps driven by greatest business return. The first step in the journey is to leverage investments in the digital space. With ABB’s approach, we encourage investments with our consulting and domain expertise and then apply our technology to bridge the gaps in technology.”
“The challenge has always been there but is exacerbated due to the need for remote operations,” he said. “It’s easy to get confusing unmatched information, and it is time consuming to put it together. And when we are working with premise-only data, the challenge lies in accessing it remotely and working with it.”
Waldroop continued, “Oil and gas has become a much lower margin industry business. What that means is it is extremely crucial to have highly accurate data for businesses to make faster decisions in real time. Because depressed margins are going to be such a key point for us moving forward, with producers struggling to make $5 per barrel, they will need to have access to data to know what their costs are, where the production is coming from and where it is going.”
orbjørn Folgerø, chief data officer with Equinor, agrees that the role of data analytics has become more critical for the upstream sector in the current environment. Equinor is known to generate huge quantities of data. In total, more than 30 petabytes are stored in the operator’s data centers. Even before the downturn hit the industry, the company was in the process of leveraging its massive amount of data, which Folgerø believes is a good “starting point” to navigate the challenging market environment.
In this exclusive interview with E&P Plus, Folgerø shares his insights with Hart Energy’s Faiza Rizvi.
We are becoming a more data-driven company. At Equinor, we believe that the next step of our improvement journey will be mainly digital. … The majority of our improvement initiatives quite heavily center on digital. So it’s a big shift in our company, which is not just needed to drive well performance but also for the safety of workers and carbon efficiency.
The next layer on which we are working heavily is the data architecture. We are feeding data from our legacy systems into Omnia, and then we are creating a future-oriented data architecture by using machine learning and AI. … Once we have the cloud-based infrastructure and data in place, we can then visualize and analyze the data in any way we want.
In parallel, we have a road map of six digital programs, which are powered by business initiatives to leverage data. A big area is production optimization, which is a huge value driver. We are also moving all global offshore industrial data into Omnia, which helps fuel Equinor’s integrated operations center in Bergen, Norway, where almost 100 people are working to gather all these data as well as developing new machine learning applications for data analytics. We are seeing quite a significant impact on improving production using this approach.
We have also launched a subsurface data platform, making subsurface data available in Omnia. … This is used to explore new oil and gas reservoirs and to improve the recovery rates and lifetime of existing reservoirs. We are also using data analytics heavily to support our offshore wind sector.
Overall, we are seeing significant cash flow improvements due to digital and a promising impact that makes us confident and makes us want to accelerate even faster in the digital space.
The human side is probably the most important one. If we are investing heavily in technology, we should also invest heavily in the competence and development of employees. When Equinor set up a digital center, we also set up a digital academy where our employees have already completed over 150,000 training periods. Upscaling the workforce is important so they understand the technology and trust [digital] solutions.
On the technology side, like I said, it is all about data, and we have a lot of historical data since 1972 in varying levels of quality. So we realized that we need to lift the quality of the data, which is a big challenge and a lot of hard work.
Secondly, we need to move the data locked up in old software into a more structured and machine-readable dataset. So that’s one area that we are working heavily on, using natural language processing and other technologies to make our data more readable and easy to analyze.
Finally, we are using machine learning-generated data to try to fill in the gaps in the datasets. It’s quite novel, but we are still working in that area.