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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 and the cascaded long short‐term memory network to leverage their complementary strengths. Two perturbation methods are applied to resolve the issues of over‐convergence and disturbance compensation. 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. In the case study, 12 well logs that cannot be measured while drilling are generated based on the logs available in the drilling process. The EnLSTM is capable of reducing cost and saving time in practice.