An Ensemble Kalman Filter Reservoir Dynamic History Fitting Method Based on Hypersphere Transformation

Abstract

The invention provides an ensemble Kalman filter dynamic reservoir history matching method based on hyper-sphere transform. The method comprises the following steps: 1, initializing a set of reservoir models, wherein the set comprises static parameters, dynamic parameters and oil well production data; 2, performing hyper-sphere transformation on permeability in the static parameters, and constructing a novel state vector set; 3, inputting each state vector in the novel state vector set into a reservoir simulator for predicting so as to obtain a state prediction value, wherein each state vector and the state prediction value thereof form a prediction set; 4, calculating a Kalman gain matrix according to the prediction set; 5, updating the prediction set according to the prediction set, the Kalman gain matrix and the measured oil well production data, thereby obtaining the updated static parameters, dynamic parameters and oil well production data. According to the method disclosed by the invention, the accuracy of reservoir history matching precision can be improved, and the blindness of manual history matching can be reduced.

Description

technical field [0001] The invention relates to the technical field of oilfield development, in particular to an ensemble Kalman filter reservoir dynamic history fitting method based on hypersphere transformation. Background technique [0002] Reservoir history matching is one of the indispensable links in the oilfield production process. In the petroleum industry, the concept of closed-loop reservoir management has received great attention from oilfield workers in terms of the concept of intelligent oilfields, and it is of great significance to the formulation of production systems and the maximum development of existing oil and gas resources. The traditional reservoir history matching method is that the reservoir engineer adjusts the model parameters according to his experience, and judges whether the set parameters are consistent with the production history data through the calculation results of the reservoir simulator, and finally uses the best fitting method A set of ...

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Problems solved by technology

[0003] In the process of reservoir history fitting, many parameters need to be adjusted, and the degree of freedom of the model is large. The traditional artificial history fitting method is very time-consuming and labor-intensive. The gradient-based history fitting method needs to calculate the sensitivity coefficient, which is difficult to adjust for uncertainties. assessment

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