Application research on intelligent logging interpretation based on E&P Dream Cloud

doi:10.3969/j.issn.1672-7703.2020.05.013

Abstract

Abstract: Geophysical logging is still the most important technical means used to identify the physical properties of subsurface strata and to decide whether they are hydrocarbon-bearing. Using the technologies of big data and artificial intelligence, based on the E&P Dream Cloud intelligent data analysis platform, this paper presents a new approach to intelligent well logging interpretation. Features of log data are extracted on the basis of data collection and splicing. Expert knowledge and experience are combined to build model. Machine learning and model training are carried out, and model testing and iteration are completed. For this study, intelligent logging interpretation technology was verified by confirming sandstone and mudstone differentiation (with a cut-off value of 0.4 based on 60 wells) and oil and water layer differentiation (with a cut-off value of 0.5 based on 47 wells). The interpretation results show that the intelligent system takes about 2-4 hours to distinguish between sandstone and mudstone. The accuracy rate of mudstone and sandstone identification is 94% and 90% respectively with an overall accuracy rate of 92%. It takes about 4-6 hours to identify oil and water layers. The accuracy rate of oil and water layer identification is 93% and 70% respectively with an overall accuracy rate of 86%. This technology can effectively increase the automation degree and the coincidence rate of multi-well logging interpretation. The application of big data analysis technology provides a new motive force and good prospects for the development of well logging interpretation technology.

Key words: E&, P Dream Cloud, well logging, big data, artificial intelligence, well log interpretation

CLC Number:

Zhao Lisha, Shi Yongbin, Jin Wei, Li Hua, Ta Siken. Application research on intelligent logging interpretation based on E&P Dream Cloud[J]. China Petroleum Exploration, 2020, 25(5): 97-103.

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