A fine modeling method for multi-layer and thick-layer reservoirs in fluvial facies

Abstract

The invention provides a fluvial facies multi-layer and thick-layer oil deposit fine modeling method. The method includes the steps of firstly, fine comparing division results according to well point reservoirs to form a well point rhythm layer layered database, and establishing a fine framework model of a reservoir rhythm layer through a reservoir interface control method; secondly, forming a well point micro-facies database according to a logging micro-facies mode, and establishing a sedimentary facies model through a plane micro-facies distribution mode control method; thirdly, forming a logging fine explanation well point parameter database according to a logging fine explanation model, and establishing a property model of the reservoirs through a facies model control method. By establishing the reservoir fine model, the plane and longitudinal anisotropism characteristics of the oil deposit can be accurately depicted, and therefore the aim of guiding the plane and longitudinal remaining oil distribution rule research is achieved.

Description

technical field [0001] The invention relates to the field of fine reservoir description in petroleum development, in particular to a fine modeling method for fluvial multi-layer and thick-bed reservoirs. Background technique [0002] Fluvial facies multi-layer and thick-bed reservoirs have many vertical oil layers. After the reservoir development enters ultra-high water cut, the heterogeneity of the reservoir becomes more serious, the distribution of remaining oil and its influencing factors become more complicated, and the remaining oil in the sand body taps its potential gradually. As the main target of oilfield development, the establishment of fine model of fluvial multilayer and thick reservoir determines the heterogeneity inside the sand body, and then controls the distribution of remaining oil. Therefore, it is very important to carry out research on fine modeling methods of fluvial facies multi-layer and thick-bed reservoirs to lay the foundation for the study of rem...

AI Technical Summary

Problems solved by technology

The establishment of reservoir framework model and attribute model is difficult, mainly because of incomplete known data, insufficient precision of known data, insufficient understanding of reservoirs, and incomplete modeling algorithms. Interlayer well prediction results often contain many uncertainties, which bring certain risks to the study of well location, well trajectory, measures, adjustment scheme design, and remaining oil distribution law.

The stochastic simulation algorithm can be used to characterize the uncertainty of inter-well prediction of reservoirs, which has important reference value for optimizing various scheme designs and reducing risks. Severe heterogeneity, scattered distribution of plane sand bodies, and poor connectivity of plane sand bodies; vertically thick layers are developed, with obvious rhythm in thick layers, and both thick and thin layers are developed

In this case, although the stochastic simulation algorithm can characterize the uncertainty of inter-well prediction of reservoirs, for the purpose of establishing a fine geological model of multi-layer and thick-layer reservoirs - to obtain the deterministic approach to the actual underground to the greatest extent. The 3D geological models are far from each other, and it is difficult for people to select the optimal modeling results from multiple geological models of equal probability given by stochastic modeling

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