Global AI in Oil and Gas Market - 2019-2026
|石油・ガス部門におけるAIの世界市場：2019年-2026年 Global AI in Oil and Gas Market - 2019-2026|
|出版日: 2020年07月18日||ページ情報: 英文||
人工知能（AI）は、今後の石油・ガス部門の成長に多大な影響を及ぼすと予測されています。石油・ガス業界でも、自動化によって安全性と生産性を迅速に改善することができるため、AIの導入が加速しています。この業界では、探鉱開発にまつわる様々な課題に対処し、従業員へのリスクを軽減するため、重要なプロセスを半自動化したり、一部を完全に自動化する取り組みが進められています。Motorola Solutionsによると、 世界の石油セクターにおけるAIの需要は、2035年までに約33％増加するといいます。
Artificial intelligence is projected to be a key influencing factor for the growth of the oil and gas sector in the upcoming years. The technology revolution in oil and gas industry is taking off because of improvising safety and productivity that can be achieved by automation. With increasing challenges faced by the oil and gas industry in the past for exploration and exploitation of hydrocarbons, a cross-disciplinary approach is being rendered which requires some critical processes to be semi-automated and some to be fully automated. It can also reduce the risk to human workers. According to Motorola Solutions, the demand for the AI in the global oil sector is expected to increase by about 33%, by 2035. The AI in Oil and Gas market valued USD XX million in 2018 and it is expected to grow at a CAGR of XX% to reach USD XX million by 2026.
Increasing safety concerns among the workforce, especially the maintenance of aging pipeline infrastructure is the major driving agent in the growth of the AI in Oil and Gas market. Additionally, the surging incidents of oil & gas leakage from storage tanks and pipelines at production facilities are expected to fuel the growth of the market. Organizations across the world are trying to make the exploration and the production processes more efficient and optimized. The operations in this field are the major factors that are driving the usage of AI in oil and gas companies. The AI tools can help oil and gas companies in digitizing records and can automate the analysis of the gathered geological data and charts, which can lead to potential identification of issues, such as pipeline corrosion or increased equipment usage. For instance, For instance, in Jan 2019, BP invested in a technology start-up: Belmont Technology to strengthen the company's AI capabilities by developing a cloud-based geoscience platform. The investment will be used to support BP's ongoing work in exploring the application of cognitive computing and machine learning in its global oil and gas business.
In the exploration and production space, it can be challenging making sense of extensive amounts of data for valve positions, pump speeds, pressures at different places in the system, temperatures and flow rates, etc. Decision-makers reviewing the data might be using it in a simplified manner or not at all. AI allows companies to review the data in a shorter amount of time, discover patterns that likely were not previously observable and determine the best course of action. For instance, the Oil and Gas Authority (OGA) is making use of AI in parallel ways, owing to the United Kingdom's first oil and gas National Data Repository (NDR), launched in March 2019, using AI to interpret data, which, according to the OGA anticipations, is likely to assist to discover new oil and gas forecast and permit more production from existing infrastructures. However, high capital investments for the integration of AI technologies, along with the lack of skilled AI professionals, could hinder the growth of the market. A recent poll validated that 56% of senior AI professionals considered that a lack of additional and qualified AI workers was the only biggest hurdle to be overcome, in terms of obtaining the necessary level of AI implementation across business operations.
The AI in Oil and Gas market is segmented based on the basis of type, function and application. By type segment the AI in oil and gas market is classified into hardware, software and services. For instance, Calgary-based Ambyint has developed intelligent High-Resolution Adaptive Controllers (HRACs) which integrate with the hardware and instrumentation, such as the motor, controller, variable frequency drive, and other moving parts of lift systems. The adaptive controllers can deliver real-time control and optimization capabilities at the well, leveraging edge computing capabilities to deliver both physics-based analytics and modern data science in real time. By function, the AI in oil and gas market is classified into predictive maintenance and machinery inspection, field services, material movement, quality control, reclamation, and production planning. Predictive maintenance and machinery inspection is the fastest growing segment in the AI in oil and gas market globally. By application, the market is classified into upstream, midstream, and downstream. The upstream segment is set to dominate the AI in oil and gas market globally.
The growth in the shale oil and gas production in the US is creating the need for an expanded midstream network of pipelines, rail, tankers, and terminals. AI is widely used in the midstream sector to gather data during the transportation process through pipelines and provide the same to the human-machine interface in order to control the process. In the Oil & Gas industry these tools have been used to solve problems such as pressure transient analysis, well log interpretation, reservoir characterization, and well selection for stimulation, among others.
By region, the AI in Oil and Gas market is segmented into North America, South America, Europe, Asia-Pacific, Middle-East, and Africa. Among all of the regions, North America dominated the AI in Oil and Gas market due to the increasing adoption of AI technologies across the oilfield operators and service providers and the robust presence of prominent AI software and system suppliers, especially in the United States and Canada, the North American segment is anticipated to account for the largest share of the AI in the oil and gas market, over the forecast period. Moreover, Factors, such as the strong economy, the high adoption rate of AI technologies across the oilfield operators and service providers, robust presence of prominent AI software and system suppliers, and combined investment by government and private organizations for the development and growth of R&D activities are poised to drive the demand for AI in oil and gas sector, in the region.
For instance, ExxonMobil, one of the leading oil producers in the country, announced its plans to increase the production activity in the Permian Basin of West Texas, by producing more than 1 million barrels per day (BPD) of oil-equivalent by as early as 2024. This is equivalent to an increase of nearly 80 percent compared to the present production capacity.
Key players operating in the market are IBM, Intel, Google, Microsoft, Oracle, Sentient technologies, Inbenta, General Visio and Cisco (United States) are some of the key players profiled in the study. Additionally, the Players which are also part of the research are FuGenX Technologies, Infosys, Hortonworks and Royal Dutch Shell. Leading multinational players dominate the market and hold substantial market share, thereby, presenting tough competition to new entrants. Key players focus on various strategic initiatives such as merger & acquisition, geographical expansion, new product launch and increasing R&D expenditure to stay competitive in the market. For instance, in February 2020, Royal Dutch Shell PLC has been expanding an online program that teaches its employees artificial intelligence skills, part of an effort to cut costs, improve business processes, and generates revenue. Artificial intelligence enables the company to process the vast quantity of data across the businesses to generate new insights, which can keep the ahead of the competition.
In October 2019, Microsoft announced the collaboration with energy industry tech company Baker Hughes and AI developer C3.ai to bring enterprise AI technology to the energy industry via its Azure cloud computing platform. It would allow customers to streamline the adoption of AI designed to address issues like inventory, energy management, predictive maintenance and equipment reliability.