WOA (Whale Optimization Algorithm) Optimizes Elman Neural Network Model to Predict Porosity Value in Well Logging Curve

Youzhuang Sun, Junhua Zhang, Zhengjun Yu, Zhen Liu and Pengbo Yin
Additional contact information
Youzhuang Sun: College of Earth Science and Technology, China University of Petroleum, Qingdao 266555, China
Junhua Zhang: College of Earth Science and Technology, China University of Petroleum, Qingdao 266555, China
Zhengjun Yu: Sinopec Shengli Oilfield, Dongying 257000, China
Zhen Liu: College of Earth Science and Technology, China University of Petroleum, Qingdao 266555, China
Pengbo Yin: College of Earth Science and Technology, China University of Petroleum, Qingdao 266555, China

Energies, 2022, vol. 15, issue 12, 1-14

Abstract: Porosity is a vital parameter in reservoir research. In the process of oil exploration, reservoir research is very important for oil and gas exploration. Because it is necessary to take cores for indoor test in order to accurately obtain the porosity value of cores, this process consumes significant manpower and material resources. Therefore, this paper introduces the method of machine learning to predict the porosity by using logging curves. This paper creatively develops a WOA (whale optimization algorithm) optimized Elman neural network model to predict porosity through logging parameters PE, DEN, M2R1, AC, GR, R25, R4 and CNL. Porosity measurement is constructed by taking cores for indoor experiments. It contains a total of 328 sample points. The data is divided into training set and test set. The logging parameters are used as the input parameters of the prediction model, and the porosity measured in the laboratory are used as the output parameter. In order to evaluate the performance of the model, RMSE, R 2 , MAE and VAF evaluation indexes are introduced to evaluate. This paper also introduces the non-optimized Elman neural network and BP neural network to compare with this optimization model. The research shows that the WOA algorithm optimizes the super parameters of the Elman neural network, so that the performance of the WOA–Elman model is better than the Elman neural network model and the BP neural network model.

Keywords: WOA; meta heuristic algorithm; Elman neural network; reservoir prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/12/4456/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/12/4456/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:12:p:4456-:d:842317

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().