HOUSTON – July 17, 2020 – Halliburton (NYSE: HAL), Microsoft Corp. (Nasdaq: MSFT) and Accenture (NYSE: ACN) today announced they have entered into a five-year strategic agreement to advance Halliburton’s digital capabilities in Microsoft Azure.
Under the agreement, Halliburton will complete its move to cloud-based digital platforms and strengthen its customer offerings by:
Enhancing real-time platforms for expanded remote operations,
Improving analytics capability with the Halliburton Data Lake utilizing machine learning and artificial intelligence, and
Accelerating the deployment of new technology and applications, including SOC2 compliance for Halliburton’s overall system reliability and security.
“The strategic agreement with Microsoft and Accenture is an important step in our adoption of new technology and applications to enhance our digital capabilities, drive additional business agility and reduce capital expenditures,” said Jeff Miller, Halliburton chairman, president & CEO. “We are excited about the benefits our customers and employees will realize through this agreement, and the opportunity to further leverage our open architecture approach to software delivery.”
“Moving to the cloud allows companies to create market-shaping customer offerings and drive tangible business outcomes,” said Judson Althoff, executive vice president, Microsoft’s Worldwide Commercial Business. “Through this alliance with Halliburton and Accenture, we will apply the power of the cloud to unlock digital capabilities that deliver benefits for Halliburton and its customers.”
The agreement also enables the migration of all Halliburton physical data centers to Azure, which delivers enterprise-grade cloud services at global scale and offers sustainability benefits. Accenture will work closely with Microsoft, in conjunction with their Avanade joint venture, to help transition Halliburton’s digital capabilities and business-critical applications to Azure. Accenture will leverage its comprehensive cloud migration framework, which brings industrialized capabilities together with exclusive tools, methods, and automation to accelerate Halliburton’s data center migration and provide for additional transformation opportunities.
“Building a digital core and scaling it quickly across a business is only possible with a strong foundation in the cloud,” said Julie Sweet, chief executive officer, Accenture. “Halliburton recognizes that this essential foundation will provide the innovation, efficiency and talent advantages to do things differently and fast. We are proud to be part of driving this transformational change, which builds on our long history of working with Halliburton and Microsoft.”
The companies expect to complete the staged migration by 2022.
Microsoft (Nasdaq “MSFT” @microsoft) enables digital transformation for the era of an intelligent cloud and an intelligent edge. Its mission is to empower every person and every organization on the planet to achieve more.
Founded in 1919, Halliburton is one of the world’s largest providers of products and services to the energy industry. With approximately 50,000 employees, representing 140 nationalities in more than 80 countries, the company helps its customers maximize value throughout the lifecycle of the reservoir – from locating hydrocarbons and managing geological data, to drilling and formation evaluation, well construction and completion, and optimizing production throughout the life of the asset. Visit the company’s website at www.halliburton.com. Connect with Halliburton on Facebook, Twitter, LinkedIn, Instagram and YouTube.
Accenture is a leading global professional services company, providing a broad range of services in strategy and consulting, interactive, technology and operations, with digital capabilities across all of these services. We combine unmatched experience and specialized capabilities across more than 40 industries — powered by the world’s largest network of Advanced Technology and Intelligent Operations centers. With 513,000 people serving clients in more than 120 countries, Accenture brings continuous innovation to help clients improve their performance and create lasting value across their enterprises. Visit us at www.accenture.com.
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Here’s a look at how AI is transforming entire enterprises, particularly through the lens of marketing and IT, and why the two teams must work together.
The massive impact AI has already had in marketing, and what we expect to see of it in the near future, is a hot topic here at MarTech Today. In my previous columns, we’ve explored how AI will be woven into marketing organizations, where it belongs in your marketing stack, and where CMOs should focus today to get the best results from their investments in AI.
There’s no doubt it’s become widespread; in fact, global spend on artificial intelligence is expected to grow from an estimated $2 billion this year to $7.3 billion per year by 2022, according to a study from Juniper Research. Yet, as abundant as it is, artificial intelligence is still a mystery to many.
Case in point: Only 33 percent of consumers think they use AI-enabled technology, yet new research shows that 77 percent actually use an AI-powered service or device.
Marketers are perhaps savvier to the opportunities than most, so it was no surprise that when my company, BrightEdge, recently asked over 500 marketers to identify the next “big trend in marketing,” 75 percent pointed to some type of AI application.
CMOs are challenged now to not only identify the right AI applications to solve specific problems but to then sell those to the CEO, other company leaders and the teams that will use the technology. Today, we’re going to broaden the scope and take a look at just a few of the ways AI is transforming entire enterprises, particularly through the lens of marketing and IT integration.
The CIO, CMO and AI
We learned in recent Adobe research that 47 percent of digitally mature organizations, or those that have advanced digital practices, said they have a defined AI strategy.
We all know that Google has one. The search giant dropped a whopping $3.2 billion acquiring Nest Labs, the largest of its $3.9 billion in disclosed AI acquisitions since 2006. All told, Google has invested $3.9 billion in AI deals, more than any other company.
Microsoft, Apple, Intel and SalesForce behind Google round out the top five companies making acquisitions of AI. (Intel takes the crown for the highest number of unique investments in AI companies, at 81.)
Sixty-one percent of over 1,600 marketing professionals from companies of all sizes pointed to machine learning and AI as their company’s most significant data initiative for next year, a MemSQL survey found.
But where is all of this interest and investment headed?
Take a look at Amazon for a sneak preview. The e-commerce giant completely rebuilt itself around AI, with spectacular results, according to a feature published in Wired. In 2014, according to the article, Srikanth Thirumalai, computer scientist and head of Amazon’s recommendations team, brought CEO Jeff Bezos the idea that Amazon could use deep learning to revamp the way recommendations work.
Thirumalai was only one department leader who included AI in his visionary proposal to Bezos. The revolution came, he told Wired, when leaders in isolated pockets of AI came together to discuss the possibilities and ultimately begin collaborating across projects. As Thirumalai told Wired:
We would talk, we would have conversations, but we wouldn’t share a lot of artifacts with each other because the lessons were not easily or directly transferable.
What followed was a revolutionary AI-centric management strategy that has baked artificial intelligence into Alexa, Amazon Web Services and almost every other facet of the $1 trillion company. Amazon takes a “flywheel” approach to AI.
Modeled after the simple tool that stores rotational energy, Amazon’s AI flywheel enables teams to build off of AI applications developed elsewhere in the organization. It’s an entirely collaborative approach that has proven a revenue generator, as well, by offering select tools to third-party companies.
That collaboration — the shift from competing for the budget for AI to working across departments — has paid huge dividends for Amazon. What could it do for your brand?
Solving persistent challenges
In 2018, CMOs have had access to more third-party AI-powered tool options than they can shake a stick at. Our firm found in recent research that more than 50 percent of marketers simply expect marketing technology providers to have native AI capabilities and consider it important or a must-have.
CIOs have been slower on the draw. Gartner’s 2018 CIO Agenda Survey found that just 4 percent of CIOs have already implemented AI in the corporate realm. However, 46 percent plan to do so in the near future. This doesn’t mean IT is being left behind. After all, the best use of AI isn’t about providing tools; it’s the catalyst in massive organizational change and even creating a new type of organization.
In the Texas A&M University System, for example, Cyber Security Intelligence reports that AI has been put to work in IT enhancing cybersecurity via Artemis, an intelligent assistant from Endgame.
“We monitor the networks for 11 universities and 7 state agencies,” said Barbara Gallaway, a security analyst at Texas A&M University System, told the publication.
Using an AI application that enables her staff to ask simple questions has helped train them in their jobs as a side benefit, she reportedly said. Her team now includes eight part-time student workers who don’t need extensive experience in dealing with security incidents in addition to nine full-time IT staff.
AI-powered products and services are helping IT teams improve productivity and effectiveness through logs analysis, employee support, enhanced cybersecurity, deep learning, natural-language processing and more. CIOs have the opportunity to transform IT from cost center to organizational trailblazer with AI.
However, as we’ve seen with Amazon, the real magic happens when CMOs, CIOs and other company leaders work together to facilitate collaborative workflows and enhanced customer experiences through AI.
Analysis of data is already a key AI focus for businesses, with on-site personalization the second most commonly cited use case for AI. Working across departments and projects, teams are discovering new and unexpected use cases for AI in their organizations.
For example, Mike Orr, IT director of digital transformation at Murphy Oil, shared the following story with CIO.com. Murphy Oil turned to an AI-powered system from Turbonomic to make recommendations about how to optimize their infrastructure while moving it from traditional on-premises and colocation to cloud and SaaS models. Once the company grew comfortable with the system, they began to trust it to perform placement and sizing automatically. Prior to the move, Orr had 4 1/2 full-time equivalents working on nothing but tickets. “Now it’s one-tenth of an FTE [full-time employee],” he says.
This is something we’re going to see more and more; in fact, Gartner predicts that while 1.8 million jobs will be eliminated due to AI by 2020, 2.3 million more jobs will be created in their place. Rather than the robots “stealing our jobs,” the impact of AI technologies on business is projected to increase labor productivity by up to 40 percent and enable people to make more efficient use of their time.
So, how can CIOs and CMOs work together?
As the worldwide volume of data continues to grow at some 40 percent per year, the CIO and CMO need to work closely and collaborate early on new initiatives. IT is a critical strategic partner for marketing and should be involved and consulted from conception and through all stages of planning.
Constantly connected consumers are generating a wealth of data for marketing — so much that most teams struggle to uncover the actionable insights that drive smarter, more informed campaigns. Who better than IT to assist? In addition to their information architecture and analysis prowess, IT is also in a position to share relevant insights with other departments as well. CMOs and CIOs must each take steps to come closer together. For CMOs, this means mastering not only the art of creativity and strategy but also the science of analytics. CIOs need to shift from a mindset of control and prevention to that of a facilitator and enabler.
The CIO is in a position to execute massive organizational change, while the CMO can be critical in selling it internally, to the rest of the C-suite and right on down to individual team members.
The CMO must be able to articulate and clearly define business goals for the CIO to evaluate and cost out. This is a give-and-take relationship that may require some negotiation but is sure to result in more purposeful tracking, measurement, and analysis.
Each must demonstrate a willingness to communicate on the level; to adopt a common vernacular and clear set of expectations of one another.
Both the CIO and CMO must enable and support integrated teams. This means not only giving employees the time and space to work together, but also giving recognition and sharing results out to the company when these partnerships result in innovative, successful uses of AI within the organization.
It all sounds great in theory, doesn’t it? In reality, changing up the complexities of traditional organizational hierarchy and deep-seeded business practice has proven incredibly challenging. Industry recommendations suggest CIOs and CMOs ensure they have these five prerequisites in place (with the CEO’s explicit support) as the foundation on which to build this relationship:
Be clear on decision governance.
Build the right teams.
Hire IT and marketing translators.
Learn to drive before you fly.
In the age where the growth of big data brings complexity, with a universe of AI-powered possibility spread out before us, marketing and IT simply do better together.
ABOUT THE AUTHOR
Artificial Intelligence, Automation, COMPLIANCE & REGULATIONS, Data, Employment, Executive Brief, Featured 1
CANNONSBURG, Pa. — If there is mud on the floor, they say in the shale industry, that means cash is coming in the door. That is, when workers are out in the field and the boots are getting dirty, money is being made.
Thanks to an infusion of high technology driving the natural gas industry, it’s not just about dirty boots anymore – and it’s a good story. It’s a marriage of advanced technologies and dirt-under-your-nails hard work rarely told, because extracting shale is not a popular business politically.
Fracking, it turns out, is the one high-tech industry not embraced by politicians in Pittsburgh who are rushing to embrace the likes of Uber and Google. Why? Because local progressive Democrats, very vocal climate activists, and the burgeoning Democratic Socialists of America party demand a wholesale repudiation of the natural gas industry. Local Democratic officials thus have to oppose fracking or risk losing in a Democratic primary.
Today’s natural gas industry isn’t the same petroleum job your grandfather or your father would have applied for. It not only attracts computer scientists, software engineers, mathematicians, and geologists to relocate to Western Pennsylvania from around the country, but it also provides careers for locals who thought those good jobs left for good when the coal mines and steel mills closed a generation ago.
Plenty of locals, who perhaps were not cut out for college, just wanted an opportunity to work hard in an industry with a future. All the better if that industry utilized the resources of the land while conserving it — nobody wants to spoil the places for hunting, fishing, climbing, hiking, and camping. Even better, a local job would allow them to live near family.
Mike May is one such guy.
The 33-year-old grew up in Imperial, Pa., along the Lincoln Highway. After graduating from West Allegheny High School, May joined the Marines. When he left the service, he wanted to come back home to Western Pennsylvania and work his way up in the world, but he just didn’t know if he had the career skills.
“So, I started in the gas and oil fields literally working with my hands; I have worked in the industry from the bottom up,” he says as he stands in front of three monitors doing the same thing he did in the field.
No dirt under the nails. No weather dictating field conditions. No mud on the boots. Just precision automation that does the job a team of workers used to do in the field. Now, May does it inside the offices of CNX, a fracking company that broke off of energy giant CONSOL.
“Basically, I was a production operator,” explains May, “I ran all the physical operations, manual chokes, fixing anything that would break or go down; adjusting water dumps to increase the efficiency of the separators, water, and tank levels out there,” he says of the drilling sites.
Now, he does almost all of that remotely.
“See, this is the digital twin of the well site,” he says, pointing to one of several screens he is monitoring in a highly secure floor of the complex. “So, over here, we have all of our physical assets. This is the data surveillance side of the house. We’re also able to control and push parameters out to the field level. So, things I used have to do at the site and make physical changes I can do using technology,” he says.
Twenty miles north of this office, in Pittsburgh, several dozen young climate activists — about May’s age — protested last week in front of the mayor’s office. They pressed Democratic city and county leaders to stop the expansion of fracking in the county and to speak out against the Shell cracker plant under construction in the region.
Twenty miles in the opposite direction, public high schools are offering vocational training for their students that prepare them to walk off the high school football field on graduation day with their diplomas and into jobs that start at $129,000 a year.
Compared to the kids closer to Pittsburgh, these kids from rural high schools won’t have an inside track for jobs at the likes of Google, Uber, and others whom the Democratic mayor celebrates as part of the “new Pittsburgh.”
And the Shell cracker plant the climate activists were protesting? It doesn’t make really make crackers — cracking is the process that converts natural gas products into ethylene and then into plastics. The $6 billion dollar plant began construction last year, with construction employment expected to exceed 6,000 workers over the next ten years and provide 600 permanent positions once the plant is complete.
Since the 1920s, technology and automation have been disrupting the manufacturing world — eliminating jobs and growth opportunities throughout the different regions in the country. Here, technology is creating jobs. For May, automation and high technology didn’t take his job; it enriched it.
“Correct. I kinda evolved with the times. I am truly living the American Dream.”
First out is the Alvheim field, where Solution Seeker´s ProductionCompass AI solution will utilize all available and relevant data to perform real-time production data analytics and production optimization, including management of the challenging slugging problem at the field through advanced slug data analytics.
“With Alvheim, we embark on a very exciting journey with AkerBP and Cognite to deliver artificial intelligence to maximize oil and gas production based on pure data-driven models. We are honored and proud to be chosen as a strategic partner to AkerBP and Cognite, as AkerBP is clearly one of the most ambitious oil companies driving the digital oilfield agenda.” says Vidar Gunnerud, founder, and CEO of Solution Seeker.
The production data is streamed live from Cognite´s Data Platform, developed in close collaboration with AkerBP to make all data and models readily accessible for all users and systems. The platform facilitates an open ecosystem for advanced applications such as Solution Seeker´s AI.
“We believe Solution Seeker´s AI will enable us to fully leverage and make sense of all our production data, build robust, fast and precise prediction models, and maximize our production in real-time. Their solution plugs directly onto the Cognite Data Platform, accessing all relevant production data, and writing all relevant results from their artificial intelligence back to the platform so other systems and users, in turn, can utilize these new data. In addition to the value this project creates from production optimization, this is a real demonstration of how we want to work with partners through the Cognite platform. This is data liberalization in practice – creating tangible results at every step,” says Signy Vefring, Manager Digitalization Program Office at AkerBP.
Solution Seeker is developing the first artificial intelligence for oil and gas production optimization, leveraging big data and machine learning techniques to solve the continuous optimization problem. The AI is capable of analyzing thousands of historical and live production data streams, identifying field behavior and relations, and automatically and continuously providing the most up to date prediction model to make the optimal choice of production settings.
The AI is currently being developed and deployed in collaboration with ConocoPhillips, Neptune Energy, Wintershall, Lundin, and AkerBP, and will be launched and made commercially available to all operators in 2018. This will disrupt the way operators can maximize production and improve their operations.
Solution Seeker is a technology spin-off from the ICT research group at NTNU Engineering Cybernetics and NTNU’s Centre for Integrated Operations.
Solution Seeker AS
Today, AI is helping the oil and gas industry chart its future course. Since no previous sources have provided an in-depth look at the impact of AI among the leading oil and gas companies, we set out in this week’s research to help answer questions that oil and gas leaders are asking:
What types of AI are applications currently in use by leading oil and gas companies such as ExxonMobil and Shell?
What (if any) results have been reported on AI applications implemented by leading companies in the oil and gas industry?
Are there any common trends among their innovation efforts – and how could these trends affect the future of the oil and gas industry?
This article seeks to provide a comprehensive look at applications of AI by the five leading oil and gas companies. Our ranking of companies is based on the Forbes’ 2017 Global 2000 ranking of the world’s biggest public companies.
Through facts and figures we aim to provide pertinent insights for business leaders and professionals interested in how AI is impacting the petroleum industry.
Prior to exploring the applications, we’ll present the common patterns that emerged from our research in this industry.
Artificial Intelligence in Oil and Gas – Insights Up Front
The most popular AI applications from the top five industry leaders currently appear to be:
Intelligent robots – Robots designed with AI capabilities for hydrocarbon exploration and production, to improve productivity and cost-effectiveness while reducing worker risk (see ExxonMobil and Total below)
Virtual assistants – Online chat platform that helps customers navigate product databases and processes general inquiries using natural language (see Royal Dutch Shell below)
In the full article below, we’ll explore the AI applications of each company individually. We will begin with ExxonMobil, the #1 ranked company in this industry based on the Forbes’ 2017 Global 2000 ranking of the world’s biggest public companies.
Among its ongoing collaborative efforts with approximately 80 universities in the U.S. and abroad, In December 2016, ExxonMobil announced that it is working with MIT to design AI robots for ocean exploration. Brian Williams, an MIT professor and a designer of the AI software that helped create NASA’s Mars Curiosity Rover is a key member of the project team.
While the business advantage of using AI in deep-sea exploration may not be immediately apparent, the company aims to apply AI to boost its natural seep detection capabilities. Natural seeps occur when oil escapes from rock found in the ocean floor. An estimated 60 percent of oil underneath the earth’s surface in North America is due to natural seeps. Robots with the ability to navigate these oceanic regions and detect oil seeps can contribute to protecting the ecosystem and serve as indicators for robust energy resources. It is unclear specifically when ExxonMobil’s ocean exploring AI robots are expected to be deployed.
A visual depiction of natural oil seeps (source)
As a founding member of MIT’s Energy Initiative, ExxonMobil has committed a reported $25 million over 5 years to support energy research conducted by MIT faculty and staff. While the company has not published the total amount invested across its 80 university collaborations, we can gain some insight from the following published figures:
Princeton University: $5 million over 5 years
The University of Texas at Austin: $15 million
“Our goal is to have these submersibles embody the reasoning of the scientists that program them. You want the explorer to do the science without the scientist there. They need to be able to analyze data, keep themselves out of harm’s way and determine novel solutions in novel situations that go beyond basic mission programming. They need to have some common sense and the ability to learn from their mistakes.” – Professor Brian Williams, MIT
ExxonMobil’s MIT robotics collaboration (source)
Through its partnership with the MIT Energy Initiative and related efforts, ExxonMobil has made energy efficiency and the exploration of new energy sources a core focus of its business priorities. According to its 2016 annual report, the company has reportedly invested roughly $7 billion since the year 2000 on R&D and “deploying emissions-reducing technologies.” The company does not itemize allocations for these technologies and specifics on AI were not published.
Royal Dutch Shell
In August 2015, Shell announced that it would be the first company in the lubricants sector to launch an AI assistant for customers (an anomaly in terms of applications of artificial intelligence in oil and gas). Normally, customers searching for lubricants and related products must navigate a large database in order to find the ideal product(s) they are searching for. Shell aims to use its avatars, Emma and Ethan, to help customers discover products using natural language.
The Shell Virtual Assistant functions through an online chat platform through the company’s website. Examples of information that the system can provide include where lubricants are available for purchase, a range of available pack sizes and general information regarding the technical properties of specific products.
To provide context, the company claims that its Shell Virtual Assistant:
Handles over 100,000 data sheets for 3,000 products
Provides information on 18,000 different pack sizes
Understands 16,500 physical characteristics of lubricants
Matches Shell products to 10,000 competitive products
The Shell Virtual Assistant is only currently available in the U.S. and U.K. but also complements four other Shell services including Shell LubeMatch which reportedly provides “over two million product recommendations for Shell customers” annually and is accessible across 138 countries in 21 languages.
An infographic representing the claims made about Shell Virtual Assistant – from Shell’s website
We were able to access the virtual assistant on the company’s website. In a note to users posted above the platform, Shell states that the virtual assistant is still in pilot mode and that efforts are ongoing to increase the knowledge of the virtual assistants and to monitor their effectiveness.
At tech emergency, we have become increasingly wary of “chatbot” and “virtual assistant” efforts that present an innovative story without a substantive business application. It is difficult to assess the genuine business value of the Shell Virtual Assistant at this time, but we tend to air on the side of caution, and we encourage our business readers to do the same (we have collected a series of chatbots that do appear to be driving business value today, and we highlight them in our “7 chatbot use cases” article).
It behooves any company to present an exciting and innovative front in the press – and chatbots seem to be a “low hanging fruit” for supposed AI applications. This is by no means a warning that we specifically state for Shell – all press-facing technology initiatives serve the purpose of molding perceptions about the company (the goes for every industry). We do our best to dig for the genuine business ROI of AI applications, and we advise our readers to approach applications and press releases with skepticism, bearing in mind the motives of the companies behind them (which do not have to be malicious to be misleading and falsely optimistic).
Similar to Shell LubeMatch, the company is also looking to expand the service to other countries and languages.
Shell’s R&D expenditures in 2016 totalled $1.014 billion. While specifics on AI were not reported, according to its Investor’s Handbook, R&D priorities are focused on “improving the efficiency of its products, processes, and operations”, and there is a concentration on developing technologies which support low-carbon energy.
Shell’s innovation in collaboration with Subsea 7 has created an Autonomous Inspection Vehicle
that claims to provide safer and better inspections – at a significant cost savings
In the future, the company reportedly seeks to integrate AI and automation into its facilities. Shell envisions that automated robots will be able to take over routine observational tasks and data gathering currently conducted by human employees. The company reportedly integrated a virtual assistant called Amelia into its business model to more efficiently respond to inquiries from suppliers regarding invoicing.
Shell believes the future of AI in its industry will see a significant increase in unmanned and automated facilities.
China Petroleum and Chemical Corp. (Sinopec)
Sinopec has hinted at the role of AI in moving innovation forward in the oil and gas industry. The company boasts a long-term plan to roll out construction of 10 intelligent plants with a goal of a 20 percent reduction in operation costs.
On the manufacturing front, Huawei (Chinese telecom company) in April 2017 announced a collaborative effort involving Sinopec to design what is described as a “smart manufacturing platform.”
The platform description highlights AI as one of 8 core capabilities of the platform which aims to deliver a centralized method of data management and support integration of data across multiple applications used to manage factory operations.
AI would serve to establish rules and models that would inform how data is interpreted and offer opportunities for identifying valuable insights to improve factory operations. Huawei did not specify a timeline for when Sinopec is expected to fully implement the platform.
Hydrocarbon exploration, the ability to map and identify oil and natural gas deposits beneath the earth’s surface, is a growing area of focus in the oil and gas industry. However, more innovative and environmentally-friendly methods of achieving improved effectiveness and efficiency are needed in the field. Environmental conditions are increasingly challenging for workers conducting hydrocarbon exploration thus technology capable of handling the task while retaining optimal functionality is highly desirable.
In an effort to establish what is described as the “first autonomous surface robots able to operate on oil & gas sites,” Total launched an international competition in December 2013. Total’s ARGOS challenge (Autonomous Robot for Gas & Oil Sites) was narrowed down to five teams hailing from Europe, Asia and South America who were provided with a maximum of three years to finalize their prototypes. For each of the 5 teams, Total provided a maximum of €600,000 (approximately $707,376) to support research and design, and a single prize of €500, 000 (approximately $589,522.50) for the winning robot.
AI was a key component of how the robot would function. Total expected that competitors ensure that their robots were able to deliver reports encompassing real-time data collection related to inspection points (locations where exploration is taking place) and analyses around the effectiveness of the locations of interest.
Total established key goals for the ARGOS robot:
The ability to carry out inspections, during the day or night, which are currently performed by humans.
The ability to detect abnormal equipment activity and intervene in an emergency. Examples may include simple equipment malfunctions or more high-risk situations such as gas leaks.
In May 2017, Total selected ARGONAUTS designed by a team from Austria and Germany as its winner. Total retains exclusive intellectual rights to the technology behind the ARGONAUTS robot for a period of five years. No further announcements have been made as to when the company will begin implementing ARGONAUTS.
Within its Exploration and Production segment, Total reports that over half of R&D allocations are focused on improving exploration capabilities; hydrocarbons and robotics are specifically mentioned. Innovation and R&D expenditures for oil and gas activities totaled $689 million in 2016.
(Readers with a specific interest in robotics and vehicles for the heavy industry may want to listen to our heavy industry-focused interview with Dr. Sam Kherat on our AI in Industry podcast.)
In June 2017, Gazprom and Yandex (described as Russia’s leading internet company) entered into a cooperation agreement for the implementation of new projects in the oil and gas industry. The two companies plan to tap into AI and machine learning to roll out their prospective initiatives.
Specifically, the collaboration is expected to focus on:
Drilling and well completion
Modeling oil-refining strategies
Optimizing other technological processes