From the perspective of business, engaging employees is critical to developing and advancing a company’s sustainability goals. The feeling is mutual from the perspective of current, not to mention future employees: A company’s sustainability goals are important to the process of attracting and retaining the top talent.
But meaningful engagement across the entire spectrum of a company’s operations can be challenging. Many employees are often unsure how their job roles connect with a company’s sustainability programs and strategies, and many companies find it challenging to integrate — and inspire — leadership on sustainability in the day-to-day activities in their workforce. The net result: Employees often end up being an underused and undermotivated resource in a company’s sustainability journey.
Dow recognized these challenges early on and began to address them with its company-wide commitment to 2015, and now, 2025 Sustainability Goals, which have sought to redefine the role that business plays in society. A primary objective of the goals is to mobilize the human element — employees, suppliers, customers and the communities in which they live and work — to improve the well-being of people the world over.
To take the 2025 goals to the next level within the company, Dow collaborated with the Erb Institute of the University of Michigan in 2017 to design and launch the Dow Sustainability Academy. The Dow-Erb partnership has proven to be incredibly successful, productive, fun and, yes, sustainable. Dow brought to the table its decades of experience on making business sustainability real, and Erb brought its 20-year track record of being at the leading edge of research and teaching at the intersection of business, society and the environment.
The result of this partnership is a business-sustainability leadership and development program that provides Dow employees with the tools and insights they need to bring sustainability into their daily work. As part of the academy, Dow employees — selected as part of a competitive, application-based process — spend a week in training at the Erb Institute.
During this time, they learn from and interact with some of the world’s leading experts on a wide range of topics, from making the business case for sustainability and the policy backdrop against which business sustainability unfolds, to hands-on tools for implementing the elusive triple bottom line. When the in-class sessions come to a close, academy participants work on real-world projects related to one of the Dow sustainability goals and are given six months to use what they learned in Ann Arbor to complete them.
Recently, we had the pleasure of watching project teams from the second group of academy members present their project solutions to Dow leaders, as well as to the next contingent of employees chosen to be part of the academy. Each team passed along their advice to their successors in the academy, and it struck us while we listed to them that their learnings apply to not only academy participants but to anyone seeking to collaborate, stretch and grow at their company and in their career.
Here’s some of what we heard:
Avoid solutions that are attractive only because they are obvious or easy. One team was asked to determine the theoretical limits of how much emissions can be reduced from each Dow site, plant, equipment and technology. The aim was to help Dow achieve its 2025 Operations Sustainability Goal of growing the company globally over the next decade without allowing the company’s greenhouse gas emissions to exceed its 2006 baseline.
Team members had to reach outside their area of expertise and talk with dozens of people across Dow sites to understand and catalog the possible opportunities. By asking questions and — importantly — challenging assumptions about what previously were thought to be the performance range of various technologies and equipment, the group was able to identify additional, significant opportunities for reducing emissions.
When you face challenges, remember that your vision and passion are your North Star. All the projects carried out by academy participants require engaging in complex systems and with multiple stakeholders. In this kind of environment, sustainability objectives aren’t easy to define, and decisions must be made in an information-rich environment characterized by high levels of uncertainty.
One team, tasked with reducing food waste at a Dow site as part of the company’s goal to advance a circular economy, admitted that it was easy to get lost in rabbit holes or mired in red tape. However, by being true to their vision of what was possible, and by being persistent — “no” was not an acceptable answer — they were able to find both a workable solution for composting at a Dow site and identify local groups receptive and able to receive the compost.
Make “change agent” part of your job description. There’s a saying at Erb: When it comes to sustainability in business, be prepared to invent the job you want and then go do it. In other words, don’t wait to be anointed; being a change agent is a title you can bestow upon yourself.
The same goes for participants in the academy. One group was tasked with identifying a single project that aligned neatly with Dow’s valuing nature goal; the requirements were that the project had to be good for business but even better for the natural environment. Rather than identifying just one project, members took it upon themselves to identify one project each, for a total of three. From creating sustainable prairie habitat at company headquarter and planting native grasses to reduce erosion at a Seadrift, Texas, site to waste reduction at a plant in Freeport, Texas, these projects were heralded for their ability to cut emissions, rehabilitate the environment and bring business value to Dow.
As we get set to embark upon our fourth Dow Sustainability Academy, we could not be more delighted by what we have seen from those who have graduated from it. By thinking critically and creatively about sustainability’s role on the job, employees not only found answers to drive Dow’s sustainable practices but established critical leadership skills.
They learned to apply ingenuity and entrepreneurial spirit to address sustainability challenges and to respond to sustainability opportunities.
They began to see those sustainability decisions are real opportunities for setting and then achieving objectives and that business sustainability really is a journey that will require vision, leadership and course corrections along the way.
And they found that no matter their job titles, they actively could incorporate tools for sustainability into their jobs — and into their lives outside of work — in order to be champions for lasting, positive change.
That’s a win for employees, for Dow and Erb, and — most importantly — for society
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
Humans have five senses, yet none of them can understand unstructured information. Watson helps the oil & gas industry surpass human limits and enables the kind of decision making that keeps operations running at full speed. Find out more at https://www.ibm.com/industries/oil-ga…
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
Oildex, the leader in financial automation solutions for the oil & gas industry, today announced OpenTicket, the next generation of the company’s digital field ticket solution. OpenTicket is the industry’s only comprehensive, end-to-end cloud-based platform that provides both operators and service providers with all the software they need to generate, review and approve digital field tickets. New capabilities of OpenTicket include a dedicated mobile application that supports both online and offline generation of digital field tickets, support for Drilling & Completions (D&C) and Lease Operating Expense (LOE) organizations, and processing optimizations that speed payments, improving operator/supplier relationships.
“Highly inefficient paper field tickets are the last obstacle to overcome when it comes to automating and digitizing the oilfield,” said Craig Charlton, CEO of Oildex. “OpenTicket solves this problem and allows service providers to quickly and easily submit field tickets while allowing operators to quickly and easily approve those field tickets. Coupled with our OpenInvoice platform and recently announced Supply Chain Finance program, we are creating the most efficient source-to-settle ecosystem in the oil & gas industry.”
New Capabilities in OpenTicket
Complete solution for both operators and service providers: Through online portals for both operators and suppliers, OpenInvoice integration, a cloud-based collaborative workflow engine, integration APIs and a dedicated mobile application, OpenTicket is a complete solution for both service providers generating and submitting field tickets, as well as operators adjudicating and approving field tickets.
Offline mobile support for service providers: A native iOS and Android mobile app allows for the creation of digital field tickets as work is completed with Store and Forward functionality, so it works even when service providers are offline. The application features a user interface designed with the needs of service providers working in the field in mind.
Support for Drilling & Completions (D&C): New OpenTicket D&C functionality including rentals support, as well as integration with industry-leading morning reporting systems to provide accurate up-to-the-minute cost information from field tickets submitted via the mobile app.
“Virtual Company Man” capability: For Lease Operating Expense (LOE) production operations field supervisors, OpenTicket provides a ‘virtual company man’ capability whereby service provider personnel become members of a virtual team, allowing the field supervisor to be aware of all operations and costs across a broad geographic territory in near real time.
Optimized processing expedites approval enables ‘Pay on Ticket’: Several new processing improvements such as automated price book reconciliation allow OpenTicket to significantly decrease the time associated with the approval, invoicing, and payments, leading to improved operator/service provider relationships.
“With the introduction of OpenTicket, Oildex seems to have figured out a solution to a problem that has plagued the oilfield services industry since the very beginning,” said Bob Cohen, Research Director, Ardent Partners. “Oildex has streamlined the field ticket process by offering reconciliation with price books and purchase/work orders, added support for auto-approval scenarios that automatically ‘flip the ticket’ into an invoice, and applied AP workflow and approval capabilities to the field ticket automation process.”
By removing the use of traditional paper field tickets, OpenTicket improves safety by eliminating unnecessary travel, makes all field ticket information analyzable data to support analytics initiatives, gives field supervisors complete visibility into all activities and costs, and automates compliance and reconciliation processing to expedite approvals and payments. OpenTicket fully integrates with OpenInvoice to create a seamless and automated platform for submitting field tickets and creating digital invoices for review and approval.
Packaging and Availability
Formerly known as OpenInvoice Field Ticket, OpenTicket is available now. Operators can subscribe to OpenTicket and purchase OpenTicket Mobile seats they can distribute to their service providers. Existing Field Ticket subscribers will be able to obtain OpenTicket Mobile licenses from Oildex. As an agile development shop, Oildex updates OpenTicket every month. While most of the new capabilities in OpenTicket are already available, some including D&C functionality is planned to be available this summer.
Oildex is transforming the way the oil and gas industry connects, collaborates and automates. More than 1,100 operators, 67,000 service providers, dozens of financial institutions and millions of mineral rights owners use the Oildex Network to seamlessly and securely collaborate with their business partners, automate critical business processes, eliminate the high cost and errors associated with the handling of paper, and obtain access to key data to make more informed business decisions. Oildex is headquartered in Denver and has offices in Calgary; Houston; Austin; Fayetteville, Arkansas, and Tennessee. Learn more about Oildex at http://www.oildex.com.
What role can business play in achieving the Sustainable Development Goals (SDGs) The World Business Council for Sustainable Development (WBCSD) launched the CEO Guide to the SDGs – a new resource aimed at galvanising engagement from global business leaders in relation to the Sustainable Development Goals (SDGs). The CEO Guide to the SDGs sets out clear actions that CEOs can take to begin to align their organisations with the SDGs and plot a course towards unlocking the value they represent. Find out more: http://www.wbcsd.org/Overview/Resourc…
Published by Green TV April 3, 2017
Over the next decade, oil and gas companies have a huge challenge and major responsibility to significantly reduce their carbon footprint and address climate change.
Ernesto Santibanez Borda, a PhD researcher in the Earth Sciences & Engineering Department at Imperial College is looking to help these companies choose the best method for limiting emissions associated with using and transporting natural gas. We interviewed him about his work with Professor Anna Korre.
1. What problem you are trying to solve/address in your PhD?
We recognize that hydrocarbon companies are faced with an enormous task to figure out how to reduce their emissions dramatically in a cost-effective and efficient way while providing energy for increased world consumption. While predictions from the International Energy Agency (IEA) outline gas consumption growth as the fastest among all fossil fuels resulting in a possible gas-carbon demand parity by 2040, there are still significant emissions from natural gas.
My PhD research focuses on the natural gas supply chain including the stages of production, processing, and transport through pipelines or as Liquefied Natural Gas (LNG). It is about developing an intelligent approach to choosing which technologies could be adopted to reduce greenhouse gas emissions (GHG) in a cost efficient way.
The idea is to also consider market conditions and policy related uncertainties to help strategic decision-making.
2. That sounds like a big project. So, what steps are involved in your PhD?
My PhD can be divided into three main parts.
First, I will use the Life Cycle Analysis (LCA) methodology to understand the full extent of greenhouse gas emissions in the natural gas supply chain. This involves assessing all environmental impacts associated with all the stages of the natural gas supply chain from extraction through to distribution.
I am using models developed by the MERG (Minerals, Energy and Environmental Engineering Research Group) at Imperial College but I will also develop new ways. These models differ from the majority available in the market as there is a greater degree of accuracy in the estimation, most of the emissions are calculated based on material balance principles (which means accounting for material entering and leaving a system), and engineering calculations.
The second part is establishing the costs associated with each technological path using the Life Cycle Costing (LCC) methodology. When companies try to estimate their emissions through LCA, they can often see ways to reduce their emissions by adopting specific technologies. But in order to be able to implement those changes it is important to cost all those options.
The final step is to determine the best combination of technologies and practices that minimise environmental impacts and costs in order to aid industry decision-making. We are doing this through multi-objective optimisation which is a technique that models a problem mathematically and minimises or maximises mutually excluding objectives. In this case, we want to see how low the emissions can be, if we spend a specific amount of money.
3. Is your PhD part of a larger body of work at Imperial College? Who else are you working with?
Yes, I work within Department of Earth Science and Engineering in the MERG group underthe supervision of Professor Anna Korre and Dr. Zhenggang Nie.
The developed models are currently being tested in different case studies, some of them provided by the Oil and Gas Climate Initiative project in which we are involved in, and the results will be compared with the reported emissions/costs and benchmark values from literature to validate our results. We also want to analyse case studies from the Brazilian natural gas value chain.
I have also been working with the Sustainable Gas Institute, and using a lot of data from Dr Paul Balcombe’s paper (Methane & CO2 emissions from the natural gas supply chain).
4. Why are you concentrating on LNG?
Projections by IEA state that by 2040 inter-regional gas trade can expand by up to more than 40%, and LNG’s share of inter-regional gas trade can increase from 10 to 50%.
In addition, we believe that an optimisation assessment of the environmental impacts and costs of the LNG processes has still not been thoroughly addressed considering the impacts it has on other parts of the natural gas value chain.
5. What motivated you to work in energy research?
Energy is vital to the international economy, but there are still so many challenges; improving efficiency, and reducing our environmental impact as well as meeting increasing global demand.
I found the research around finding ways to meet global demand while making sure we keep to the environmental targets set by the latest international commitments quite fascinating. Seeing companies in the energy sector get involved also encouraged me to join this research area.
Finally, I like the work of integrating different knowledge disciplines such as hydrocarbon processing, operations research, and environmental assessment in order to produce a tool that could be used to make intelligent decisions that have a wide impact.
6. What attracted you or influenced you to becoming an engineer?
At secondary school, I realized I was interested in maths and science, so it was natural to start looking at careers that are related to those subjects, and engineering specifically caught my attention because it is practical and helps model, and find reasonable solutions for daily problems faced by individuals, companies or the society.
The fact that it does not just involve hard calculations, but can also integrate other disciplines into the decision-making also fascinated me because it opened a whole world of options on how to approach a specific problem.
Ernesto Santibanez Borda is a Brazilian and Chilean national. He holds a BSc Engineering from Pontificia Universidad Catolica de Chile, and MSc Petroleum Engineering from Imperial College London.
He also has 2 years of experience as production planning engineer in Escondida mine, operated by BHP Billiton (Chile)
By Zara Qadir
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