پیش‎ بینی همگرایی تکنولوژی های هوش مصنوعی و حفاری با استفاده از روش پیش بینی پیوند

نوع مقاله : مقاله پژوهشی

نویسندگان

1 عضو هیئت علمی دانشکده مدیریت صنعتی و فناوری دانشگاه تهران

2 دانشجوی دکتری دانشکده مدیریت صنعتی و فناوری دانشگاه تهران

3 دانشکده مهندسی شیمی، نفت و گاز، دانشگاه علم و صنعت ایران، تهران، ایران

4 دانشیار- دانشکده مدیریت - دانشگاه تهران

چکیده

با ظهور فناوری های دیجیتال و تاثیرات چشمگیر آنها در صنایع مختلف همچون صنعت نفت، همواره چگونگی همگرایی آنها در این صنایع و ارائه پیش‎بینی از این همگرایی مورد سوال بوده است. بر همین اساس در این مقاله تلاش شد با انتخاب هوش مصنوعی به عنوان یک فناوری دیجیتال و همینطور حفاری به عنوان یک فناوری نفتی، چگونگی همگرایی این دو فناوری پیش‏بینی شود. برای بررسی این موضوع، داده‏های پتنت این دو حوزه فناوری از پایگاه داده پتنت معتبر جمع‎آوری شد و شبکه هم‏رخدادی این دو فناوری تشکیل گردید. سپس با استفاده از روش پیش‏بینی پیوند، پیش‏بینی از همگرایی میان زیرفناوری های این دو فناوری انجام شد. با بررسی نتایج، مشخص شد که یادگیری ماشین، بینایی ماشین و رباتیک به عنوان زیرفناوری های هوش مصنوعی کاربرد گسترده‏تری در بخش‏های مختلف عملیات حفاری داشته و پیش‏بینی می‎شود در این حوزه‎ها شاهد رشد و همگرایی بیشتری باشیم.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Forecasting Convergence of Artificial Intelligence and Drilling Technologies Using Link Prediction Method

نویسندگان [English]

  • Mehdi Mohammadi 1
  • Masoud Mahanifar 2
  • Rohaldin Miri 3
  • mohammad reza sadeghi moghadam 4
1 Faculty Member, Faculty of Industrial and Technology Management, University of Tehran
2 PhD Candidate, Faculty of Industrial and Technology Management, University of Tehran
3 School of Chemical, Petroleum and Gas Engineering, Iran University of Science and Technology, Tehran, Iran
4 Associate Professor - Faculty of Management - University of Tehran
چکیده [English]

With the emergence of digital technologies and their significant impacts on various industries such as the petroleum industry, their convergence in these industries and forecasting this convergence have always been questioned. This article attempts to forecast the convergence of artificial intelligence and drilling as digital and petroleum technologies. To address this topic, the patent data of these two technological areas were collected from a valid patent database and the co-occurrence network of these two technologies was created. The convergence of the sub-technologies of these two technologies was forecasted by using the link prediction method. Findings indicate that machine learning, computer vision, and robotics, as sub-technologies of artificial intelligence, have a broader application in different parts of drilling operations, and their growth and convergence are anticipated.

کلیدواژه‌ها [English]

  • Technology Forecasting
  • Technology Convergence
  • Link Prediction
  • Artificial Intelligence (AI)
  • Drilling
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