Recent years have witnessed the development of the applications of machine learning technologies to well logging-based lithology identification. Most of the existing work assumes that the well loggings gathered from different wells share the same probability distribution; however, the variations in sedimentary environment and well-logging technique might cause the data drift problem; i.e., data of different wells have different probability distributions. Therefore, the model trained on old wells does not perform well in predicting the lithologies in newly-coming wells, which motivates us to propose a transfer learning method named the data drift joint adaptation extreme learning machine (DDJA-ELM) to increase the accuracy of the old model applying to new wells. In such a method, three key points, i.e., the project mean maximum mean discrepancy, joint distribution domain adaptation, and manifold regularization, are incorporated into extreme learning machine. As found experimentally in multiple wells in Jiyang Depression, Bohai Bay Basin, DDJA-ELM could significantly increase the accuracy of an old model when identifying the lithologies in new wells.

中文翻译：

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数据漂移下基于测井的岩性识别模型的建立：一种转移学习方法。

近年来，目睹了机器学习技术在基于测井的岩性识别中的应用发展。现有的大多数工作都假设从不同的井收集的测井数据具有相同的概率分布。但是，沉积环境和测井技术的变化可能会导致数据漂移问题。即不同井的数据具有不同的概率分布。因此，在旧井上训练的模型在预测新井的岩性方面表现不佳，这促使我们提出一种名为数据漂移联合适应极限学习机（DDJA-ELM）的转移学习方法，以提高精度。适用于新井的旧模型。在这种方法中，三个关键点，即项目平均最大差异，联合分布域自适应和流形正则化被集成到极限学习机中。正如在渤海湾盆地济阳De陷多口井的实验中发现的那样，DDJA-ELM在识别新井的岩性时可以大大提高旧模型的准确性。