Kick is a downhole phenomenon which can lead to blowout, and so early detection is important. In addition to early detection, the need to prevent false alarm is also useful in order to minimize wastage of operation time. A major challenge in ensuring early detection is that it increases the chances of false alarm. While several data-driven approaches have been used in the past, there is also ongoing research on the use of derived indicators such as D-exponent for kick detection. This article presents a data-driven approach which uses D-exponent and standpipe pressure for kick detection. The data-driven approach presented in this article serves as a complementary methodology to other stand-alone kick detection equations, and uses o-exponent and standpipe pressure as inputs. This paper proposes a methodology which uses the long short-term memory recurrent neural network (LSTM-RNN) to capture temporal relationships between time series data comprising of D-exponent data and standpipe pressure data with the aim of increasing the chances of achieving early kick detection without false alarm. The methodology involves obtaining the slope trend of the D-exponent data and the peak reduction in the standpipe pressure data for training the LSTM-RNN for kick detection. Field data is used for training and testing. Early detection was achieved without false alarm. (C) 2020 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.