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Predicting formation lithology from log data by using a neural network 被引量:5

Predicting formation lithology from log data by using a neural network
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摘要 In order to increase drilling speed in deep complicated formations in Kela-2 gas field, Tarim Basin, Xinjiang, west China, it is important to predict the formation lithology for drilling bit optimization. Based on the conventional back propagation (BP) model, an improved BP model was proposed, with main modifications of back propagation of error, self-adapting algorithm, and activation function, also a prediction program was developed. The improved BP model was successfully applied to predicting the lithology of formations to be drilled in the Kela-2 gas field. In order to increase drilling speed in deep complicated formations in Kela-2 gas field, Tarim Basin, Xinjiang, west China, it is important to predict the formation lithology for drilling bit optimization. Based on the conventional back propagation (BP) model, an improved BP model was proposed, with main modifications of back propagation of error, self-adapting algorithm, and activation function, also a prediction program was developed. The improved BP model was successfully applied to predicting the lithology of formations to be drilled in the Kela-2 gas field.
出处 《Petroleum Science》 SCIE CAS CSCD 2008年第3期242-246,共5页 石油科学(英文版)
关键词 Kela-2 gas field neural network improved back-propagation (BP) model log data lithology prediction Kela-2 gas field, neural network, improved back-propagation (BP) model, log data, lithology prediction
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