Petroleum Science >2022,??lssue 2:??- DOI: https://doi.org/10.1016/j.petsci.2021.12.012
Identification of low-resistivity-low-contrast pay zones in the feature space with a multi-layer perceptron based on conventional well log d Open?Access
文章信息
作者：Lun Gao, Ran-Hong Xie, Li-Zhi Xiao, Shuai Wang, Chen-Yu Xu,
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引用方式：Lun Gao, Ran-Hong Xie, Li-Zhi Xiao, Shuai Wang, Chen-Yu Xu, Identification of low-resistivity-low-contrast pay zones in the feature space with a multi-layer perceptron based on conventional well log data, Petroleum Science, Volume 19, Issue 2, 2022, Pages 570-580,
文章摘要
Abstract
In the early exploration of many oilfields, low-resistivity-low-contrast (LRLC) pay zones are easily overlooked due to the resistivity similarity to the water zones. Existing identification methods are model-driven and cannot yield satisfactory results when the causes of LRLC pay zones are complicated. In this study, after analyzing a large number of core samples, main causes of LRLC pay zones in the study area are discerned, which include complex distribution of formation water salinity, high irreducible water saturation due to micropores, and high shale volume. Moreover, different oil testing layers may have different causes of LRLC pay zones. As a result, in addition to the well log data of oil testing layers, well log data of adjacent shale layers are also added to the original dataset as reference data. The density-based spatial clustering algorithm with noise (DBSCAN) is used to cluster the original dataset into 49 clusters. A new dataset is ultimately projected into a feature space with 49 dimensions. The new dataset and oil testing results are respectively treated as input and output to train the multi-layer perceptron (MLP). A total of 3192 samples are used for stratified 8-fold cross-validation, and the accuracy of the MLP is found to be 85.53%.
In the early exploration of many oilfields, low-resistivity-low-contrast (LRLC) pay zones are easily overlooked due to the resistivity similarity to the water zones. Existing identification methods are model-driven and cannot yield satisfactory results when the causes of LRLC pay zones are complicated. In this study, after analyzing a large number of core samples, main causes of LRLC pay zones in the study area are discerned, which include complex distribution of formation water salinity, high irreducible water saturation due to micropores, and high shale volume. Moreover, different oil testing layers may have different causes of LRLC pay zones. As a result, in addition to the well log data of oil testing layers, well log data of adjacent shale layers are also added to the original dataset as reference data. The density-based spatial clustering algorithm with noise (DBSCAN) is used to cluster the original dataset into 49 clusters. A new dataset is ultimately projected into a feature space with 49 dimensions. The new dataset and oil testing results are respectively treated as input and output to train the multi-layer perceptron (MLP). A total of 3192 samples are used for stratified 8-fold cross-validation, and the accuracy of the MLP is found to be 85.53%.
關鍵詞
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Low-resistivity-low-contrast (LRLC) pay zones; Conventional well logging; Machine learning; DBSCAN algorithm; Multi-layer perceptron