Seismic facies control-based sedimentary microfacies recognition method

Abstract

The present invention relates to a seismic facies control-based sedimentary microfacies recognition method. The method includes the following steps that: 1) logging information is normalized; 2) fine horizon calibration is performed; 3) a model framework is determined; 4) a low frequency model is established; 5) a seismic data impedance value and a formation reflection coefficient are obtained, if synthetic trace data are in optimal matching with an actual seismic data trace, and drilling impedance is in optimal matching with seismic data impedance, and the method shifts to step 6), otherwise, the method shifts to the step 3); 6) well seismic constraint inversion is performed, a wave impedance inversion data body is obtained; 7) the top and bottom of a reservoir are traced and explained, with the top reflection layer and bottom reflection layer of the reservoir adopted as control surfaces, interlayer attributes are extracted from the inversion data body; and 8) seismic attributes are utilized to constrain the boundaries of sedimentary microfacies, and the spread of the sedimentary microfacies is described. According to the method, the logging information is adopted to constrain seismic inversion, and therefore, the resolution of an inversion result can be improved, the sedimentary microfacies of a target interval can be well depicted, the planar distribution of a thin sand layer can be predicted, and a basis can be provided for well location deployment.

Description

technical field [0001] The invention belongs to the technical field of special attribute processing and reservoir prediction in the technical field of geophysical exploration, and specifically relates to a sedimentary microfacies identification method under the control of seismic facies. Background technique [0002] With the deepening of oil and gas exploration and development, in recent years, oil reservoirs have been drilled in geological bodies such as turbidite sand bodies, sheet sands, and beach bar sands in negative structural belts. Target. Because the deep sag area and the inner slope are the confluence of multiple sand bodies, the reservoir is thin, and it is difficult to predict the thin sand layer. The traditional reservoir prediction technology cannot effectively describe the sedimentary microfacies distribution of the thin sand layer. [0003] Sedimentary microfacies refers to the smallest unit with unique rock structure, structure, thickness, rhythm and other...

AI Technical Summary

Problems solved by technology

This method solves the problem of beach bar microfacies prediction and identification for wells without drilling and coring data, but the Bayes discriminant function of bar sand and beach sand is established by using statistics, which makes this method have great limitations and is only suitable for beach Dam sedimentary microfacies identification

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