Abstract
Well logging data plays an important role in reservoir description and O&G resource evaluation. In the era of artificial intelligence (AI), intelligent well logs interpretation driven by AI can greatly improve the efficiency of well logs interpretation, save time and effort of domain experts, and reduce costs. To achieve intelligent well logs interpretation, the training of AI models requires a large amount of labeled data. However, the current situation is small amounts of labelled data, high cost and strong subjectivity of expert annotation. On the basis of the physical models of well logs, this paper proposes a domain knowledge-based approach for synthetic well logs generation. The generated well logs are employed to help train AI models. The shortcomings of existing methods are considered comprehensively, and a joint deep learning model driven by domain knowledge is established for geological parameter inversion and well logs generation. Various types of well logs can be generated by using the proposed approach. Experimental results show that the proposed method can generate high-quality labeled well logs. Moreover, the generated well logs are useful for improving the performance of AI models of intelligent well logs interpretation.
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Hou, Sl. et al. (2022). Domain Knowledge-Based Well Logs Generation and Its Application in AI Modeling. In: Lin, J. (eds) Proceedings of the International Field Exploration and Development Conference 2021. IFEDC 2021. Springer Series in Geomechanics and Geoengineering. Springer, Singapore. https://doi.org/10.1007/978-981-19-2149-0_130
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DOI: https://doi.org/10.1007/978-981-19-2149-0_130
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