Oil and Gas Production Prediction Based on SVM and Improved Particle Swarm Optimization-Reference-Cited by-同舟云学术

Oil and Gas Production Prediction Based on SVM and Improved Particle Swarm Optimization

Published:2020 Issue: Volume: Page:259-275
ISSN:2327-0411
Container-title:Advances in Computational Intelligence and Robotics
language:
Short-container-title:

Author:

Peng Jun¹^ORCID,Qiao Yudeng²,Jin Shangzhu¹^ORCID,Tang Dedong³,Ge Lan⁴,Xia Qinfeng⁴,Pang Shaoning⁵

Affiliation:

1. School of Electronic Information Engineering, Chongqing University of Science and Technology, China

2. Chongqing Chuanyi Analyzer Co., Ltd, China

3. Chongqing University of Science and Technology, China

4. Sinopec Chongqing Fuling Shale Gas Exploration and Development Co., Ltd., China

5. Federation University Australia, Australia

Abstract

Cognitive information is widely used in the field of oil and gas, where production forecasts are of great importance to companies. In this chapter, combining support vector machine and improved particle swarm optimization algorithm, a gas field production prediction model is established, and the model is validated by the actual production data of an enterprise over the years. The results show that the model has good convergence, high prediction accuracy, and training speed and can predict its output more accurately. The method adopted in this chapter is the development of cognitive information technology. The authors have reason to believe that with the continuous development of cognitive information technology, it will have a far-reaching impact on social progress.

Publisher

IGI Global

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