Strata-Constrained GWLSTM Network for Logging Lithology Prediction

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Precise rock lithology identification from well logs is critical for reservoir characterization and field development. Traditional knowledge-based lithology interpretation is highly dependent on the interpreter’s experience and judgment, which could lead to erroneous decision making or biased prediction. To reduce human involvement and improve interpretation efficiency and consistency, a knowledge-constrained long short-term memory (LSTM) network solution is introduced. In this study, LSTM networks are applied with different constrains to obtain the mapping relations and validate the knowledge-constrained LSTM model accordingly. The entire workflow mainly includes input logging data preprocessing, different constrain validations during the LSTM model training, and validation processes. This study covers and compares the direct LSTM model without constrains, rectangular constrain LSTM (RCLSTM), and Gaussian window weighted constrain LSTM (GWLSTM). In particular, GWLSTM applies a sample cluster as input instead of single sample points. The weight of the sample point is controlled by a distance-correlated Gaussian window, which means the closer to the predicting point, the greater the impact on the prediction. LSTM, RCLSTM, and GWLSTM models are tested on a field data set of five wells in a typical sandstone gas reservoir. Two wells are used to train the network, while the other three wells are used for network assessment. The test results demonstrate that by applying LSTM networks to establish the mapping between the logging curves (e.g., CNL, DT, DEN, GR, and RD) and rock lithology, rock lithologies in target formation can be predicted from well logs. Moreover, the lithology predictions by the GWLSTM model are more accurate than those of conventional LSTM and RCLSTM models, especially for thin layers. In conclusion, GWLSTM networks improve lithology identification accuracy by taking stratigraphic sequences into consideration. And the Gaussian window constrains are more effective than rectangular window constrains for thin layer predictions. Lastly, GWLSTM doesn’t require a large training data set, which makes it advantageous for reservoirs with limited wells.