A multi-level stacking logging identification method for deep coal measures and thin coal seams

Abstract

The invention discloses a multistage superposition logging recognition method for a deep coal series and a thin coal layer. Through multiple times of superposition of multiple kinds of logging curves and data, the method disclosed by the invention can obtain quantitative data of coal layer characteristics under burial conditions in a deep part (with depth of over 3000 m). Through processing and the superposition of the multiple kinds of logging data about the deep thin coal layer, the method disclosed by the invention can obtain depth, elevation and thickness values of the coal layer at a top boundary point and a bottom boundary point in the deep part. In addition, the method disclosed by the invention can also obtain standard values of boundary and thickness data by comparing the coal layer in the deep part and upper and lower rock layers in order to realize spatial linkage of points, lines and faces. The method disclosed by the invention makes for overcoming the problems such as wrong judgment, false judgment and large errors in an existing deep coal layer recognition technique, and can realize quantitative accurate recognition of top and bottom boundaries, the thickness, the depth and the like of the deep coal layer.

Description

technical field [0001] The invention relates to a multi-level stacked well logging identification method for deep coal measures and thin coal seams. Background technique [0002] The use of logging data for subsurface strata identification, stratum identification and sequence analysis is an effective supplement to core strata observation and analysis, making the final identification, determination and division of subsurface strata and ore beds more scientific and real sex. For example, the response values ​​of natural gamma ray and resistivity logging curves are mainly affected by changes in sediment shale content, sorting properties, and grain size. Therefore, changes in logging values ​​can provide information on the hydrodynamic conditions of the depositional environment and source supply. Conditions, sedimentation methods (progradation, aggradation, retrogradation), profile structure and other important information. When using logging curves to identify sequence bounda...

AI Technical Summary

Problems solved by technology

The main problem is: in the past, most of the applications in coal measures are shallow, that is, the depth is less than 2000m. In the field of petroleum geology, drilling and logging can reach up to tens of thousands of meters. Using conventional methods to identify coal seams, even if the thickness of the coal seam is very large, it cannot be accurately identified and determined

Shallow logging formation identification methods, such as general logic discrimination methods, cannot meet the identification requirements of deep formations, and technical progress and comprehensive improvement of logging data interpretation are necessary

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