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Development of a 4D Information based Integrated Management System for Geothermal Power Plant Drilling Project

지열발전 시추프로젝트의 4D 정보화기반 통합관리 시스템 개발

  • Lee, Seung Soo (Korea Institute of Civil Engineering and Building Technology) ;
  • Kim, Kwang Yeom (Korea Institute of Civil Engineering and Building Technology) ;
  • Shin, Hyu-Soung (Korea Institute of Civil Engineering and Building Technology)
  • 이승수 (한국건설기술연구원 SOC성능연구소 Geo-인프라연구실) ;
  • 김광염 (한국건설기술연구원 SOC성능연구소 Geo-인프라연구실) ;
  • 신휴성 (한국건설기술연구원 SOC성능연구소 Geo-인프라연구실)
  • Received : 2014.06.19
  • Accepted : 2014.06.26
  • Published : 2014.06.30

Abstract

Deep drilling project should be managed systematically and efficiently because it is significantly influenced by various related factors having uncertainty and high risk in terms of economy and effective management. In particular, drilling project involves participants from various sectors including necessary service company and it also needs their collaboration by sharing related information occurring at drilling process in order to secure efficient performance management. We developed 4D (3D + time) information based visualization system for progress management by combining 3D design model and predicted optimized control parameters for each section in geothermal well design. We also applied PDM (precedence diagramming method) to the system in order to setup the effective process model and hooked it up to 3D information based on precedence relation and required time for informatized process network.

대심도 시추공사는 불확실성을 가진 공사영향요소가 많고, 시공리스크가 크며, 공사성능관리의 효율화여부에 따라 프로젝트의 경제성에 큰 영향을 미치기 때문에 체계적이고 효과적인 관리가 수행되어야 한다. 특히, 시추공사는 수많은 참여주체가 협업하여 진행되기 때문에, 실시간으로 발생되는 모든 시추정보는 원활하게 공유되어야 효율적인 성능관리가 수행될 수 있다. 본 연구에서는 지열정 설계정보를 활용하여 3차원 설계모델을 작성하였다. 또한, 지열정의 각 구간별로 최적화 설계된 성능제어변수 및 공기에 대한 성능정보의 예측결과를 3차원 모델과 연계하여 4D(3D+time)기반 진도 시각화시스템을 구축하였다. 공정간 선후행 관계 및 각 소요공기를 바탕으로 효과적인 공정 모델을 구축하기 위하여 선후행도형법을 활용하여 정보화 공정네트워크를 작성할 수 있는 모듈을 개발하여 3차원 정보와의 연계효율성을 고려하였다.

Keywords

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Cited by

  1. Modern drilling management system based on field data monitoring vol.11, pp.3, 2018, https://doi.org/10.1007/s12145-018-0336-8