An Overview of Evolutionary Computing for Interpretation in the Oil and Gas Industry

• Type: Article in International Conference Proceedings Book
• Date: 2016

Authors

Lopes, RL (Author) Other; Jahromi, HN (Author) Other; Jorge, AM (Author) FCUP

Conference proceedings International

Title: ACM International Conference Proceeding Series

Pages: 110-114

9th International C* Conference on Computer Science and Software Engineering, C3S2E 2016

20 July 2016 through 22 July 2016

Indexing

Scopus - 1 Citation

Other information

Authenticus ID: P-00K-S91

DOI: 10.1145/2948992.2949006

Abstract (EN): The Oil and Gas Exploration & Production (E&P) field deals with high-dimensional heterogeneous data, collected at different stages of the E&P activities from various sources. Over the years different soft-computing algorithms have been proposed for data-driven oil and gas applications. The most popular by far are Artificial Neural Networks, but there are applications of Fuzzy Logic systems, Support Vector Machines, and Evolutionary Algorithms (EAs) as well. This article provides an overview of the applications of EAs in the oil and gas E&P industry. The relevant literature is reviewed and categorised, showing an increasing interest amongst the geoscience community. © 2016 ACM.

Language: English

Type (Professor's evaluation): Scientific