Prediction of Oilfield-Increased Production Using Adaptive Neurofuzzy Inference System with Smoothing Treatment

Joint Authors

Chen, Lin
Ma, Nannan
Wang, Yi
Liu, Zhi-bin

Source

Mathematical Problems in Engineering

Issue

Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-11, 11 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2019-12-19

Country of Publication

Egypt

No. of Pages

11

Main Subjects

Civil Engineering

Abstract EN

A novel modified adaptive neurofuzzy inference system with smoothing treatment (MANFIS) is proposed.

The MANFIS model considered the smoothing treatment of initial data basing on the adaptive neurofuzzy inference system, and we used it to predict oilfield-increased production under the well stimulation.

Numerical experiments show the prediction result of the novel considering smoothing treatment is better than that without smoothing treatment.

This study provides a novel and feasible method for prediction of oilfield-increased production under well stimulation, and it can be helpful in the further study of oilfield development measure planning.

American Psychological Association (APA)

Chen, Lin& Liu, Zhi-bin& Ma, Nannan& Wang, Yi. 2019. Prediction of Oilfield-Increased Production Using Adaptive Neurofuzzy Inference System with Smoothing Treatment. Mathematical Problems in Engineering،Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1195812

Modern Language Association (MLA)

Chen, Lin…[et al.]. Prediction of Oilfield-Increased Production Using Adaptive Neurofuzzy Inference System with Smoothing Treatment. Mathematical Problems in Engineering No. 2019 (2019), pp.1-11.
https://search.emarefa.net/detail/BIM-1195812

American Medical Association (AMA)

Chen, Lin& Liu, Zhi-bin& Ma, Nannan& Wang, Yi. Prediction of Oilfield-Increased Production Using Adaptive Neurofuzzy Inference System with Smoothing Treatment. Mathematical Problems in Engineering. 2019. Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1195812

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1195812