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

Document Type : Research Paper

Authors

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

10.22104/jtdm.2024.7080.3349

Abstract

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.

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History

  • Receive Date: 31 August 2024
  • Revise Date: 23 September 2024
  • Accept Date: 12 October 2024

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