  In this study, we propose an ensemble long short-term memory (EnLSTM)
network, which can be trained on a small dataset and process sequential data.
The EnLSTM is built by combining the ensemble neural network (ENN) and the
cascaded long short-term memory (C-LSTM) network to leverage their
complementary strengths. In order to resolve the issues of over-convergence and
disturbance compensation associated with training failure owing to the nature
of small-data problems, model parameter perturbation and high-fidelity
observation perturbation methods are introduced. The EnLSTM is compared with
commonly-used models on a published dataset, and proven to be the
state-of-the-art model in generating well logs with a mean-square-error (MSE)
reduction of 34%. In the case study, 12 well logs that cannot be measured while
drilling are generated based on logging-while-drilling (LWD) data. The EnLSTM
is capable to reduce cost and save time in practice.
