Method for partitioning glutenite sedimentary period based on Fischer diagram

Abstract

The invention relates to a method for partitioning glutenite sedimentary period based on a Fischer diagram. The method comprises the following steps: selecting logging curve combination, discriminating a lithological change surface, discriminating a high-frequency base-level cycle, determining the preferred period of the high-frequency base-level cycle, determining the accuracy of partitioning the high-frequency base-level cycle, making Fischer diagrams, and partitioning a long-term base-level cycle. The method can be used for constructing the corresponding relationship between lithofacies and the logging curves by analyzing the lithofacies, can be used for selecting the logging curve combinations which can well reflect the lithologic characters to carry out clustering analysis, main composition analysis and polynomial regression analysis so as to determine the lithological change, and can be used for partitioning a high-frequency cycle base level by using the logging curve combination, lithological change level and discrete wavelet high-frequency component, and adopting time-frequency analysis technique to acquire the high-frequency preferred cycle period of the logging curve to inspect the partitioned high-frequency cycle base level. On this basis, the Fischer diagrams are made to obtain curves reflecting the change of the base level so as to partition the sedimentary period.

Description

Technical field: [0001] The invention relates to a method for dividing sandy conglomerate body deposition stages. Background technique: [0002] Sandy conglomerate reservoirs have strong heterogeneity in the horizontal direction, large depositional thickness in the vertical direction, few mudstone interlayers, and lack of paleontological markers and marker layers. , the logging response is not obvious, the geological logging is rough, and the seismic resolution is low, which makes it difficult to divide single wells and compare them in the whole area. one. [0003] In wavelet transform, for non-stationary signals (logging signals are non-stationary signals), the time-frequency window needs to be adjustable, that is, it is required to have better time resolution characteristics in the high-frequency part, while in the low-frequency part It has good frequency resolution characteristics. Wavelet analysis of well logging curves can identify curve cycles of different frequenci...

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