As Oil remains one of the most highly valued commodities in the energy sector and as concerns over the environmental impact of energy production and consumption persist, oil and gas companies are actively seeking innovative approaches to achieving their business goals while reducing environmental impact.
Another issue in oil and gas industries is 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.
As a result of that during the last two decades, the petroleum industry across the world has experienced a sharp increase in the number of artificial intelligence (AI) applications. This upsurge in the number of applications of AI is due to the greater availability of human experts.
Artificial Intelligence has been defined as “the ability of the machine to perform at the level of human expert”. Rooted in science and engineering, the domain includes machine learning, natural language processing, pattern recognition, search, inference, and planning. It is devoted to designing ways to make computers perform tasks that were previously thought to require human intelligence.
AI studies are broadly classified into two main categories;
1. Studies that try to mimic the operation of human brains known as Artificial Neural Network (ANN)
2. Studies that understand and apply thinking methodologies is known as classical Artificial Intelligence.
Artificial neural networks, fuzzy logic, and evolutionary algorithms are common among AI techniques being applied today in oil and gas reservoir simulation, production and drilling optimization, drilling automation and process control, and data mining. This offers new opportunities for the oil and gas industry. “In the near future, there will be sensors with connectivity everywhere, recording data as often as you want them to and constantly creating new datasets. It can improve the efficiency and safety of well-drilling, and boost productivity. It can also “help us to improve equipment reliability and predict maintenance requirements of our facilities.1 Using AI processes, known as algorithms, we’ll be able to combine datasets about areas such as flow rates and pressures and equipment vibration with data from the natural environment, such as seismic information and ocean wave height, to transform the way we run and optimize our operations. According to economic research firm MKM Partners, Artificial Intelligence is “a major segment, with a potential $15 billion opportunity by 2025.” This estimate largely takes into account the boost of the applications of AI for the oil and gas industry.
Having said that, we can able to answer questions that oil and gas experts looking for by considering top five leading oil and gas companies according to Forbes 2017 Global 2000 ranking of the world’s biggest public companies:
What types of AI applications are 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?
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 (like ExxonMobil and Total did)
· Virtual assistants – Online chat platform that helps customers navigate product databases and processes general inquiries using natural language (like Royal Dutch Shell did)
· In December 2016, ExxonMobil announced that it is currently working with MIT to design AI robots for ocean exploration.
· 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.
2. Royal Dutch Cell3
· In August 2015, Shell announced that it would be the first company in the lubricants sector to launch an AI assistant for customers.
· Normally, customers searching for lubricants and related products must navigate a large database in order to find the ideal product 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, the range of available pack sizes and general information regarding the technical properties of specific products.
· In the future, the company reportedly seeks to integrate AI and automation into its facilities. The company reportedly integrated a virtual assistant called Amelia into its business model to more efficiently respond to inquiries from suppliers regarding invoicing.
3. China Petroleum and Chemical Corp. (Sinopec)
· The company boasts a long-term plan to roll out the construction of 10 intelligent plants with a goal of a 20 percent reduction in operating costs with the help of AI.
· 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.” It 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.
4. Total 4
· To tackle the hydrocarbon Exploration issue mentioned earlier Total launched an International competition in 2013. Total’s ARGOS Challenge was narrowed down to five teams across the world who were provided with a timeline of three years to finalize their prototypes.
· Total specify characteristics they want in their ARGOS robot:
o The ability to carry out inspections, during the day or night, which are currently performed by humans.
o 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.
· And as a result of Challenge Total finally selected ARGONAUTS robot.
5. Gazprom 5
· In June 2017, Gazprom and Yandex (Russia’s leading internet company) entered into a cooperation agreement to introduce AI and Machine learning in their prospective initiatives.
· Specifically, the collaboration is expected to focus on:
o Drilling and well completion
o Modelling oil-refining strategies
o Optimizing other technological processes
· As specific to implementation have not yet been reported, Time will tell specifically how Gazprom and Yandex will leverage AI and machine learning throughout their various initiatives.
Ø How Artificial Intelligence can be leveraged
· Planning and Forecasting: On a macro scale, machine learning can help to increase the awareness of macroeconomic trends to drive investment decisions in exploration and production (E&P). Economic conditions and even weather patterns can be taken into consideration to determine where investments should take place, as well as the intensity of production.
· Eliminate Costly risks in drilling: As drilling is a very expensive and risky investment, applying AI in the operational planning and execution stages can significantly improve the success rate across the various stages of drilling including: well planning, real-time drilling optimization, estimating fictional drag, and well-cleaning prediction. Additionally, geoscientists can better assess variables such as the rate of penetration (ROP) improvement, well integrity, operational troubleshooting, drilling equipment condition recognition, real-time drilling risk recognition, and operational decision-making.
· When drilling a hole, the machine-learning software takes into consideration a plethora of different factors such as seismic vibrations, thermal gradients, and strata permeability, along with more traditional data such as pressure differentials. AI can help to drive drilling decisions such as direction and speed in real time to optimize the drilling operation and can predict failure of equipment such as semi-submersible pumps (ESPs) to lower unplanned downtime and equipment costs.6
· Well Reservoir Facility Management: It includes integration of multiple disciplines: reservoir engineering, geology, production technology, petrophysics, operations, and seismic interpretation. AI can help to create tools, which will allow asset teams to build a professional understanding of their asset and identify opportunities to improve operational performance.
· Predictive Maintenance: Today with the help of AI, It is possible to look at very large amounts of data and give real-time responses on the best future course of action and avoid future equipment failure or remediate security breach.
· Oil & Gas Well surveying and Inspections: In June 2014, the Federal Aviation Administration (FAA) issued the first commercial permit for drone use over United States soil and the permit was for the oil and gas industry (BP), so as to allow survey of pipelines, roads, and equipment in Prudhoe Bay, Alaska. Drones can be applied to almost every aspect of the industry, including land surveying and mapping, well and pipeline inspections, and for security purposes. Technology is being developed to enable the use of drones to detect early methane leaks.
· Remote Logistics: As logistics to offshore location is always a challenge, Drones, based on AI technology can be used to deliver different materials to remote offshore locations.7
Ø Way Forward:
· ?Today’s O industry has been transformed by two downturns in the industry in one decade. Although the adoption of new hard technology such as directional drilling and hydraulic fracturing brought on fracking, the O industry needs to continue this trend in today’s low-price market to survive. AI has the potential to differentiate between those that continue to thrive and those that are left behind through complacency.
· The promise of AI is already starting to be realized in the O&G industry. Early adopters are taking advantage of their position on the technology adoption curve to get a head start on the competition and keep their assets safe. The industry has always leveraged technology to adapt to change, and the early adopters have always benefited the most. As the oil and gas industry continues to be more competitive, companies cannot afford to be left behind. However, if companies can understand the opportunities inherent in adopting cognitive technologies, their future surely looks bright.
· 1 Source: https://www.jimmyvallee.com/blog.php?article=artificial-intelligence-ai-revolution-in-oil-gas_17
· 2 Source: https://energyfactor.exxonmobil.com/news/mit-collaboration/
3 Source: https://www.shell.com/business-customers/lubricants-for-business/news-and-media-releases/2015/artificial-intelligence-powered-service-for-lubricant-customers.html
4 Source: http://www.telegraph.co.uk/business/2017/09/19/french-oil-major-talks-google-energy-sector-turns-ai/
5 Source: http://www.gazprom-neft.com/press-center/news/1119799/
6 Source: http://iot.ng/artificial-intelligence-the-future-of-oil-and-gas/
7 Source: https://www.linkedin.com/pulse/lower-oil-prices-can-artificial-intelligence-upstream-srivastava