Improving the simulation of a waterflooding recovery process using artificial neural networks

The waterflood performance of the dual five-spot pilot project in the Stringtown oil field, situated in West Virginia, has been studied. A numerical simulator, called BOAST98, was used for the simulation purposes, after developing a reservoir description.;The producing horizon in the field is the Upper Devonian Gordon sandstone, which is characterized by severe heterogeneity due to the depositional environment. Using available core and log data and geological analysis, a reservoir characterization study was done. A preliminary reservoir description based on log porosity-core permeability correlation was improved by developing Artificial Neural Networks (A.N.N.), which incorporates geophysical well log information. These A.N.N.'s were utilized to predict porosity and permeability for five wells in the pilot area.;A reservoir model for simulation purposes was constructed after identifying the principal flow units within the formation. Results from the simulation were compared with five years of actual field data. A close history matching for the cumulative oil and water production in the pilot project was achieved after scheduling 10--15% of the total water injection volume into the pilot area.

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

• Gil, Edison

Contributors dc:contributor

• Kashayar Aminian.

• Petroleum engineering
• Artificial intelligence

Identifier

https://researchrepository.wvu.edu/etd/1071

OAI identifier oai:identifier

oai:researchrepository.wvu.edu:etd-2074