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IJSTR >> Volume 11 - Issue 01, January 2022 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



A Review Of Artificial Neural Network ¬Applications In Petroleum Exploration, Production And Distribution Operations

[Full Text]

 

AUTHOR(S)

Angella Nwachukwu, Henry O. Omoregbee, Modestus O. Okwu, Lagouge K. Tartibu, Dolor Roy Enarevba, Adedoyin Adesuji

 

KEYWORDS

Artificial intelligence, Artificial neural networks, Drilling operations, Petroleum Exploration, Reservoir characterization.

 

ABSTRACT

The energy business thrives with in-depth knowledge and awareness of the subsurface in the oil and gas operations. Strategists have attempted to solve the problem of uncertainties which exist as a result of the complex nature of the subsurface in a variety of ways; nevertheless, the traditional approaches used have failed to provide a reliable guide to apposite decisions on the exploitation of these reservoirs. Artificial intelligence techniques, specifically artificial neural networks (ANN) have been discovered as a possible tool for unravelling the uncertainties experienced during exploration and production (E&P) operations. This research is an exposition and demonstration of the ANN capabilities in E&P operations. Firstly, the nitty-gritties of ANN were discussed. Secondly, information on AI applications in the oil and gas operations was divulged. Then the application of ANN in reservoir characterization, drilling operation, exploration and production were detailed. Finally, a case study was presented focusing on ANN application in drilling operations by considering simple speed in rpm, feed rate in mm/rev, drill size in mm and depth of cut in mm as input variables. The output target is the surface roughness obtained via experiment. The system was trained by using nineteen (19) samples representing 70%of the dataset for training and 30% for testing and validation. The close values of the experimental and predicted results demonstrate the capability of ANN to effectively fine-tune the values of input and output variables and parameters to obtain a good solution.

 

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