Application of Artificial Neural Networks for Well Logs

Neural Networks are a powerful tool - • For computation when the data available is less than adequate. • Can solve fundamental problems such as formation permeability prediction from the well log response with high accuracy. • Have great potential for computing results from historical data which would otherwise be irrelevant for analysis. Neural Networks find various applications in the Petroleum industry - Optimize the hydraulic fracture design, Permeability Predictions, Facies classification etc. This study deals with the development of a neural network for predicting well log response. This network is trained by input-output pairs of known well log data, using which an estimation model is created. This estimation model can then compute permeability output for input well log data of different offset wells. The neural network created uses a feed forward model with a Levenberg-Marquardt learning algorithm. Error is calculated using Mean Squared Error (MSE) technique. An optimum number of neurons in input, output and hidden layers were set. The network was trained with given pair of input response of well log dataset and its output permeability response was predicted. The study also considers two different cases to mark the importance of optimized training of a neural network before it can be used for predicting permeability values in offset wells. This technique is completely data driven and does not require priori assumptions regarding functional forms for correlating permeability and well logs (Sharma et al. 2011). The network developed in this study gives highly accurate results. Also it is very flexible as the training algorithm, number of layers and other parameters can be changed according to the data set available. It proves to be cost effective and saves time since it does not require core analysis. Neural Networks prove to be a useful emerging alternate tool to conventional methods.