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Ensemble Kalman filter dynamic reservoir history matching method based on hyper-sphere transformation

A Kalman filter and history matching technology, applied in the field of oil field development, can solve problems such as difficulty and uncertainty, large model freedom, time-consuming and labor-intensive problems, and achieve the effects of rapid absorption, improved accuracy, and shortened calculation cycle

Active Publication Date: 2017-03-15
重庆炬心智能科技研究院有限公司
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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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  • Ensemble Kalman filter dynamic reservoir history matching method based on hyper-sphere transformation
  • Ensemble Kalman filter dynamic reservoir history matching method based on hyper-sphere transformation
  • Ensemble Kalman filter dynamic reservoir history matching method based on hyper-sphere transformation

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Embodiment Construction

[0013] The ensemble Kalman filter reservoir dynamic history fitting method based on hypersphere transformation of the present invention includes:

[0014] Step S1: Initialize the set of reservoir models.

[0015] The set of reservoir models formed by initialization is:

[0016]

[0017] where x n,j means at time t n The jth set element of the state vector of . m s and m d are static parameters and dynamic parameters respectively; among them, the static parameters include the permeability and porosity of each grid of the reservoir model, and the dynamic parameters include the water saturation and pressure of each grid of the reservoir model; d is the production data of the oil well, Including bottom hole pressure, well oil production and oil well water production.

[0018] Static parameters, dynamic parameters and oil well production data constitute state variables, the vectors composed of state variables are state vectors, and the matrix composed of all state vectors ...

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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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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00
CPCG16Z99/00
Inventor 梁晓东李太福周伟易军张元涛刘媛媛
Owner 重庆炬心智能科技研究院有限公司
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