ANN-based prediction of cementation factor in carbonate reservoir

• Main Authors: Kadhim, Fadhil Sarhan, Samsuri, Ariffin, Al-Dunainawi, Yousif
• Format: Conference or Workshop Item
• Published: 2015
• Subjects: TP Chemical technology
• Online Access: http://eprints.utm.my/id/eprint/61837/; http://saiconference.com/Conferences/IntelliSys2015

Since carbonate reservoirs are a heterogeneous in nature, therefore the behaviour of petrophysical properties of these reservoirs is a highly nonlinear. There is no close conventional statistical model can describe the behaviour of the relation between cementation factor and rock properties. Artificial Neural Network technique is used in many applications to predict variable that usually cannot be measured in linear modelling. Depending on well logs data, the Interactive Petrophysics software had been used to calculate the petrophysical properties of studied oilfield. In this study, the data sets used for training and testing neural network are provided from well number three of Nasiriya oilfield in the south of Iraq. The neural network model was trained using two different training algorithms; Gradient Descent with Momentum and Levenberg - Marquardt. Porosity, permeability and resistivity formation factor relationships to cementation factor are proposed using artificial neural network model. An efficient performance of excellent prediction of cementation factor has been obtained with less than (1*10-4) mean square error (MSE).