Gamma ray log generation from drilling parameters using deep learning

Abstract

Lithology identification plays a vital role in defining the petroleum reservoir. Although well logging represents the traditional means of obtaining petrophysical data for lithology identification, there could be cases where logging while drilling instruments may fail during drilling. This paper presents an approach that utilizes drilling parameters obtained from mud logging and measurement while drilling (MWD) for real-time prediction of gamma ray log which is used as a lithology identifier. In this work, several machine learning methodologies, such as simple recurrent neural network (RNN), long short-term memory recurrent neural network (LSTM-RNN), temporal convolution network (TCN), gated recurrent unit (GRU) network, nonlinear autoregressive network with exogenous inputs (NARX) and simple artificial neural network (ANN) were tested for their ability to capture the relationship between hydro-mechanical specific energy (computed from drilling parameters) and gamma ray log. A recently drilled exploration gas well in the tertiary deltaic system of the Niger delta basin was used as the case study. Based on the field data, the results show the TCN and simple RNN performed best. The receptive field of the TCN played a significant role in its performance, and therefore, the LSTM-RNN can be made to perform comparable to that of TCN/simple RNN if the LSTM-RNN is manually made to work with an optimal window of input data points for each output data point. The size and nature of the data (volume, velocity, variety, and veracity) is likely a significant factor in the performance of the machine learning methodologies. Thus, it is recommended to focus on obtaining the best receptive field of the data during the machine learning development phase.