석유가스 개발사업의 인공지능기술 활용 현황 및 전망
Current Status and Prospects of Artificial Intelligence in the Oil and Gas Exploration and Production Business
1
조회
0
다운로드
4
인용
0
참고문헌
키워드
초록
이 연구는 디지털 전환의 시대를 맞이하여 석유가스 개발사업의 인공지능기술 활용 현황과 전망을 살펴본다. 인공지능, 머신러닝, 딥러닝에 대한 간략한 소개를 시작으로 석유가스 개발사업에서 인공지능이 어떻게 활용되는지, 다양한 인공지능기술 중 최근 석유가스 개발사업에서 각광받는 딥러닝 기술의 종류에 대한 소개, 국내외 석유가스개발사업의 딥러닝 기술 활용 현황과 전망, 고찰, 그리고 맺음말로 구성하였다. 이를 토대로 디지털 전환의 시대에서석유가스 개발사업이 대처하고 나아가야 할 방안에 대하여 논하고자 한다.
Abstract
This study reviewed the current status and prospects of using artificial intelligence (AI) technology in oil and gas exploration and production (E&P) in facing the era of digital transformation. Beginning with a brief introduction to artificial intelligence, machine learning (ML), and deep learning (DL), this manuscript discusses the following: the use artificial intelligence in E&P projects, an introduction to the state-of-the-art deep learning techniques highlighted in recent E&P projects, an analysis of the trends in global and domestic E&P business, relevant considerations, and concluding remarks. Thus, how the oil and gas E&P business is encouraged to cope with and move forward in the era of digital transformation is examined in detail.
참고문헌 (23)
- Abbas, A. K., "Intelligent Prediction of Stuck Pipe Remediation Using Machine Learning Algorithms", SPE Annual Technical Conference and Exhibition (2019)
- Baker Hughes, "a GE company (BHGE)", Minds + Machines Conference, pp.25~26 (2017)
- Carpenter, "Artificial Intelligence Improves Seismic-Image Reconstruction", J. Pet. Technol, 71(10), pp.65~66 (2019)
- Chen, Y., "Forecasting Crude Oil Prices: a Deep Learning based Model", Procedia Comput. Sci, 122(2017), pp.300~307 (2017)
- Chu, M. -G., "Determination of an Infill Well Placement using a Data-driven Multi-modal Convolutional Neural Network", J. Pet. Sci. Eng., pp.106805~ (2020)
- "CompTIA" (2020)[URL]
- Crnkovic-Friis, L., "Geology Driven EUR Prediction Using Deep Learning", SPE Annual Technical Conference and Exhibition (2015)
- Goodfellow, I., "Generative adversarial nets", Advances in Neural Information Processing Systems, pp.2672~2680 (2014)
- "Innovationmap" (2020)[URL]
- JPT, "University Group Lists “Most Promising” Oil & Gas Startups of the Year", ~ (2018)
- JPT, "Culture Change Needed To Realize Full Value of Digital", ~ (2018)
- JPT, "Wood Mackenzie: Peak Oil Demand Is Coming, Will Transform Exploration Sector", ~ (2019)
- JPT, "Google Says No More AI or Machine Learning for Upstream Industry", ~ (2020)
- Kim, J., "Robust Optimization of the Locations and Types of Multiple Wells using CNN based Proxy Models", J. Pet. Sci. Eng, 193, pp.107424~ (2020)
- Kim, S., "Development of Ensemble Smoother–Neural Network and its Application to History Matching of Channelized Reservoirs", J. Pet. Sci. Eng, 191, pp.107159~ (2020)
- Krizhevsky, A., "ImageNet Classification with Deep Convolutional Neural Networks", Advances in Neural Information Processing Systems, pp.1097~1105 (2012)
- "mc.ai" (2020)[URL]
- Min, B., "Current Status and Prospects of Artificial Intelligence in E&P Business", Petronet Weekly Review, ~ (2020)
- Basic Plan for Resources Development (Proposal) - 6th Basic Plan for Energy and Mineral Resources Development (2020∼2029), "MOTIE", ~ (2020)
- Rice University News and Media Relations, "‘Most promising’ companies win Rice Alliance Startup Roundup at OTC", ~ (2019)
- "Society of Petroleum Engineers" (2020)[URL]
- Tractica, "Artificial Intelligence Market Forecasts", Omdia Tractica, 48~ (2019)
- AI In Oil and Gas Market by Type, by Function, and by Application: Global Industry Perspective, Comprehensive Analysis, and Forecast, 2018–2025, "Zion Market Research", Zion Market Research, ~ (2019)