Method for identification and prediction of fore-land basin extremely-thick conglomerate body

Abstract

A method for identification and prediction of a fore-land basin extremely-thick conglomerate body belongs to the technical filed of petroleum and natural gas exploration and development. The method comprises the following steps: the step 1) determining electrical characteristics of a gravel layer; the step 2) determining the characteristics of gravel layer seismic horizon and seismic facies; the steps 3) carrying out electrical profile and earthquake depth profile superposition; and the step 4) carrying out gravel layer lithology and lithofacies space identification. The beneficial effects of the method are that gravel layer distribution range, lithology and lithofacies vertical and horizontal distribution rules and the like, which restrict fore-land basin oil and gas exploration and development at each geological time in the fore-land basin can be determined; speed traps caused by the gravel layers can be eliminated to the maximum degree; the earthquake processing imaging speed is allowed to be more accurate; seismic data quality reaches a new level; entrapment implement precision is improved greatly; and well exploration success rate can be further improved.

Description

technical field [0001] The invention relates to a method for identifying and predicting thick conglomerate bodies in foreland basins, and belongs to the technical field of oil and gas exploration and development. Background technique [0002] At present, the foreland basins in central and western my country are rich in oil and gas resources, but the Neogene to Quaternary gravel layers are generally developed, which brings great troubles to oil and gas exploration. First, the distribution of gravel layers is extremely uneven, with a thickness ranging from hundreds of meters to several thousand meters The vertical and horizontal changes of lithology and lithofacies are drastic, and the seismic velocity changes greatly, which is difficult to accurately grasp, resulting in inaccurate identification of structural traps below it, which restricts the success rate of oil and gas drilling; at the same time, gravel layers often have poor drillability and low ROP , the drilling cycle is ...

AI Technical Summary

Problems solved by technology

This method mainly relies on the velocity spectrum of seismic data. The surface and subsurface geological conditions in the central and western regions of my country are complex, and the seismic velocity spectrum data is distorted, so it is impossible to realize the prediction of gravel layer description.

[0005] The lack of necessary technical means for the prediction of shallow gravel layers in foreland basins is mainly reflected in three aspects: First, the signal-to-noise ratio of piedmont seismic data is generally low, and seismic data cannot reflect the changes in stratum lithology and lithofacies. Therefore, seismic data It is difficult to identify the shallow conglomerate; secondly, due to the strong tectonic action, the lithology and lithofacies of the shallow gravel layer change drastically, and the gravel layer often has time-diagnostic characteristics, which requires high identification accuracy; thirdly, the prediction accuracy of non-seismic data is low

Images