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A Machine Learning Approach Towards Predicting Formation Permeability Using Real-Time Data
Published in Institute of Physics
Volume: 107
Issue: 1
Pages: 6587 - 6598
Reservoirs are hydrocarbons (oil and gas) bearing subsurface structures (formation) in which wells are drilled to produce the fluids to the surface. Well testing is a method of studying pressures and their corresponding rates from an individual well, to analyze the various characteristics of a reservoir, which helps in optimum management of the production operations. In this paper, we have applied one of the most popular supervised learning algorithms known as Artificial Neural Networks (ANN) for predicting the permeability (conductivity) of the formation. The well testing data consisting of well head pressure, down hole gauge pressure, flow rates for oil and water, P*, P-1hour etc. were used as input features for training the model. A 4-layer dense ANN architecture consisting of one input and output layer each and two hidden layers was built for training and testing the model. © The Electrochemical Society
About the journal
JournalECS Transactions
PublisherInstitute of Physics
Open AccessNo