Ensemble long short-term memory (EnLSTM) network

• URL: http://arxiv.org/abs/2004.13562v2
• Date: Sun, 1 Nov 2020 02:17:49 GMT
• Title: Ensemble long short-term memory (EnLSTM) network
• Authors: Yuntian Chen and Dongxiao Zhang
• Abstract summary: 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 proven to be the state-of-the-art model in generating well logs with a mean-square-error (MSE) reduction of 34%.
• Score: 0.456877715768796
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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