Porosity distribution prediction using artificial neural networks

Reservoir characterization plays a very important role in the petroleum industry, especially to the economic success of the reservoir development. Heterogeneity can complicate the evaluation of reservoir properties. Porosity is the primary key to a reliable reservoir model.;Several studies in the literature indicated that accurate evaluation of reservoir properties can be made by the analysis of electric logs. Stringtown oil field in Tyler and Wetzel counties in the northwestern part of West Virginia was selected to conduct this study.;Artificial Neural Networks (ANN) is one of the latest technologies available to the petroleum industry. The objective of this study was to predict reliable porosity values from geophysical log data. In this study, porosity predictions were compared against core measurements and were found to be reliable with R2 of 0.97. The results confirmed the capability of using ANN. The results were utilized to map the Porosity distribution.

Name thesis:degree_name

MS

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Petroleum and Natural Gas Engineering

Year dc:date.available

2000

Author dc:creator

• Al-Qahtani, Fahad Abdullah

Contributors dc:contributor

• Khashayar Aminian.

• Petroleum engineering
• Artificial intelligence

Identifier

https://researchrepository.wvu.edu/etd/1010

OAI identifier oai:identifier

oai:researchrepository.wvu.edu:etd-2013