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Abstract

Description: Quantitative integration of 4D seismic data with production data into reservoir models is a challenging task. This paper tackles two key issues of the complex joint inversion workflow to improve its efficiency and accuracy. We applied two derivative free optimization (DFO) methods, namely particle swarm optimization (PSO) and Simultaneous Perturbation and Multivariate Interpolation (SPMI), and compared their performances. We tested different strategies of effectively mining information in both 4D seismic and production data. We proposed a method of choosing the different weights in data domain by utilizing sensitivity of inversion parameters to different types of data. We also tested the strategy of combining the inversion results from separate inversion runs using 4D seismic data or production data only. Application: We tested the workflow in a 3D synthetic model. Uncertain parameters for this model include relationship between porosity and permeability, and the ratios of kv to kh for different reservoir zones. The performance of PSO and SPMI are compared in terms of the evolution of objective function and estimation of uncertain parameters. We also provide recommendations about when to use which method. Different strategies of optimal use of 4D seismic and production data are also applied and compared using this model. The learning is also applied to a deepwater turbidite field. Results, Observations, Conclusions: Both PSO and SPMI are effective DFO methods and deliver good results for 4D seismic history matching problems. The complementary features of these two methods can ensure both applicability and efficiency of this joint inversion workflow. Choosing proper weights in either data or model domain can improve the accuracy of this workflow. Significance of Subject Matter: By solving the two key issues of jointly assimilating 4D seismic and production data, we deliver reliable workflow for reservoir model characterization and management.

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/content/papers/10.3997/2214-4609-pdb.293.G007
2012-06-04
2021-10-15
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http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609-pdb.293.G007
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