Today, the oil and gas sector is facing an array of social, economic, environmental and political challenges. Novel technology, including Artificial Intelligence (AI), provides a means to address many of these challenges and help meet the demands of a transitional energy market. AI is set to become a major part of the energy sector, with its value in the petroleum industry alone predicted to be several billion dollars by 2022. In the longer-term, consulting firm McKinsey & Company estimates AI has the potential to create annual value of approximately $200 billion across the oil and gas sector, and $3.5-5.8 trillion of annual value in the global economy.
The term AI was first coined in 1956 by celebrated computer scientist John McCarthy and can be defined as a system capable of perceiving its environment and taking actions to maximise its chances of success. Machines with the capacity to learn in a way that can be compared to processes in the human brain. Thanks to developments in computational power, algorithms, data collection and digitalisation, AI that can mimic general human cognitive capabilities (rather than just make improvements within a single, narrow field) is now starting to become a reality. There is a current debate bridging ethics, philosophy and science around the possibility that mankind could soon create computers that are capable of learning independently at a speed that is infinitely faster than those who created them. For some, the possibilities of unheralded advancement are appealing. For others, the risks that a world totally beyond our control might emerge at an incomprehensible speed are a significant cause for concern.
Major issues facing the oil and gas sector include:
Additionally, the finite availability of hydrocarbons means that there are increasingly fewer unexploited accumulations left, whilst those remaining are often more technically challenging to produce or are in difficult locations, for example, the Arctic. “However, the biggest short-term challenge is cost reduction and AI will have a positive impact on it.” All in all, fossil fuels are set to play a major role over at least the next few decades as energy demand continues. To survive in this challenging climate, oil and gas companies must embrace new technology, including AI, to maximise efficiency and revenue.
What are the applications of AI?
AI has the potential to impact all areas of the industry, with applications ranging from drilling and data interpretation all the way to facility management and improving worker safety. Although AI is only in its infancy, it is already capable of adding value to the exploration workflow; production of new and existing oilfields; reducing risk of equipment malfunction and environmental disasters; among many other applications.
This is an extremely active area of research, “as more intelligent data management tools can be embedded onto new remote self-operated ‘entities’ (these being from other computers, to robots to simple sensors, valves, actuators and all sorts of devices). One major advantage of AI is that trained sophisticated models can be ported on low power CPUs to execute instructions among the complex environments and decisions they were trained for.” Major players in the sector are collaborating with both universities and companies to develop new AI technology.
Examples of this include: BP’s partnership with start-up Belmont Technology to develop a cloud-based geoscience platform using AI, with a target of 90% time reduction in data collection, interpretation and simulation; Chevron collaborating with Microsoft to automate data extraction and move it into back-end systems for analysis; and ExxonMobil joining up with MIT to develop self-learning, submersible robots for use in ocean exploration.
In order to survive in an increasingly competitive and dynamic market, oil and gas companies must invest in new technology to get ahead of their competition. AI will play a major role in this by providing new insights from data and improving performance in both project management, operations and the supply chain.
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