JDR Vol.15 No.7 pp. 981-990
doi: 10.20965/jdr.2020.p0981


Case Reasoning-Based Emergency Decision Making for Oil and Gas Accidents

Ruifang La*,†, Zaixu Zhang*,**, and Pengfei Bai***

*School of Economics and Management, China University of Petroleum
No.66 West Changjiang Road, Huangdao District, Qingdao, Shandong 266580, China

Corresponding author

**School of Chinese Law and Economics Management, Shengli College China University of Petroleum, Shandong, China

***School of Management and Engineering, Capital University of Economics and Business, Beijing, China

May 18, 2020
October 8, 2020
December 1, 2020
oil–gas accident, emergency decision making, Bayesian network, case reasoning

Throughout the digitization of the petrochemical industry, the Beidou technology-based disaster monitoring, evaluation, and early warning network system has supported emergency decision making for oil and gas accidents. Many problems arise throughout the emergency decision-making process during oil–gas accidents, such as the limited time for decision making, high complexity, and inadequate emergency plans. Targeting these issues, we propose the construction of a case library using a Bayesian network. This way, when a new accident occurs, its similarity and deviation indexes could be matched against those of historical cases registered in the database. As such, the candidate cases are adjusted using a case combination and pruning method, yielding the final qualified case model. A case verification of the “11.22” Sinopec Oil pipeline leak and explosion in Qingdao reveals that the proposed method only requires an oil and gas accident database to be built in advance, eliminating the need for sampling data to make decisions, and reducing the search space. Using the proposed case-based reasoning, historical data and experience regarding oil and gas emergency decisions can be activated and reused, which would greatly improve the modeling efficiency of the Bayesian network.

Cite this article as:
R. La, Z. Zhang, and P. Bai, “Case Reasoning-Based Emergency Decision Making for Oil and Gas Accidents,” J. Disaster Res., Vol.15 No.7, pp. 981-990, 2020.
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