A New Scheme to Improve the Performance of Artificial Intelligence Techniques for Estimating Total Organic Carbon from Well Logs

Pan Wang and Suping Peng
Additional contact information
Pan Wang: State Key Laboratory of Coal Resources and Safe Mining, China University of Mining & Technology (Beijing), Beijing 100083, China
Suping Peng: State Key Laboratory of Coal Resources and Safe Mining, China University of Mining & Technology (Beijing), Beijing 100083, China

Energies, 2018, vol. 11, issue 4, 1-24

Abstract: Total organic carbon (TOC), a critical geochemical parameter of organic shale reservoirs, can be used to evaluate the hydrocarbon potential of source rocks. However, getting TOC through core analysis of geochemical experiments is costly and time-consuming. Therefore, in this paper, a TOC prediction model was built by combining the data from a case study in the Ordos Basin, China and core analysis with artificial intelligence techniques. In the study, the data of samples were optimized based on annealing algorithm (SA) and genetic algorithm (GA), named SAGA-FCM method. Then, back propagation algorithm (BPNN), least square support vector machine (LSSVM), and least square support vector machine based on particle swarm optimization algorithm (PSO-LSSVM) were built based on the data from optimization. The results show that the intelligence model constructed based on core samples data after optimization has much better performance in both training and validation accuracy than the model constructed based on original data. In addition, R 2 and MRSE in PSO-LSSVM are 0.9451 and 1.1883, respectively, which proves that models established with optimal dataset of core samples have higher accuracy. This study shows that the quality of sample data affects the prediction of the intelligence model dramatically and the PSO-LSSVM model can present the relationship between well log data and TOC; thus, PSO-LSSVM is a powerful tool to estimate TOC.

Keywords: organic shale; total organic carbon (TOC); least square support vector machine (LSSVM); particle swarm optimization (PSO); geophysical logs; artificial intelligence techniques (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: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1996-1073/11/4/747/pdf (application/pdf)
https://www.mdpi.com/1996-1073/11/4/747/ (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:11:y:2018:i:4:p:747-:d:138050

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 ().