Application of particle swarm optimization for ...

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States. AU: García Gonzalo, E. EM: [email protected]. AF: Mathematics, Oviedo University, Oviedo, Spain. AB: A recent global optimization method, Particle ...
28/02/2011

Application of particle swarm optimiza…

2009 Fall Meeting Search Results

Cite abstra cts a s A uthor(s) (2009), Title, Eos Trans. AGU, 90(52), Fall Meet. Suppl., A bstract xxxxx-xx

Your query was: fernández HR: 0830h AN: N S41A- 03 TI: Application of particle swarm optimization for enhanced

reservoir characterization and inversion of production and seismic data. (Invited ) AU: Fernández Martïnez, J EM: [email protected] AF: Civil and Environmental, University of Berkeley, Berkeley, CA,

United States AU: Muk erji , T EM: [email protected] AF: Energy Resources, Stanford University, Palo Alto, CA, United

States AU: Echeverrí a Ciaurri , D EM: [email protected] AF: Energy Resources, Stanford University, Palo Alto, CA, United

States AU: García Gonzalo, E EM: [email protected] AF: Mathematics, Oviedo University, Oviedo, Spain AB: A recent global optimization method, Particle Swarm Optimization (PSO, 1995), has been successfully applied in many engineering fields, although its use in geosciences is still very limited. Like all stochastic methods, PSO requires reasonably fast forward modeling parameterized by a small number of model parameters. The basic idea behind PSO is that each model searches the model space according to its misfit history and the misfit of the other particles of the swarm. We apply different PSO versions to the reservoir optimization problem of seismic history matching. In seismic history matching we update the subsurface reservoir facies model to match production data and seismic time-lapse data. The updated reservoir model is then used to make predictions of reservoir performance. The forward model relating facies to observed data consists of multiphase porous media flow simulation (for production data), and wave propagation (for seismic data). The saturations and pressures obtained from the flow simulation are linked to the elastic moduli and wave velocities through appropriate rock physics models (Gassmann's equations). The ill-conditioned character of the optimal search is attenuated in two ways: 1)by spatial principal component analysis (PCA) the optimization search space is projected to a subspace of much smaller dimension, while keeping consistency with prior spatial geological features already known for the reservoir; 2)the solutions are constrained further by increasing the diversity of the data observed. In this research we combine flow production measurements (localized around wells and of high temporal periodicity) with seismic data (spatially distributed and of lower temporal periodicity). We formulate the workflow for joint inversion using PSO methods, and test it on a synthetic reservoir model. The results show that PSO performs much better than other global methods and as fast (in terms of convergence rate) as other local

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optimization techniques tested. Furthermore, PSO allows estimating uncertainty for the posterior model parameters. DE: [1906] INFORMATICS / Computational models, algorithms DE: [1980] INFORMATICS / Spatial analysis and representation DE: [3260] MATHEMATICAL GEOPHYSICS / Inverse theory DE: [3275] MATHEMATICAL GEOPHYSICS / Uncertainty quantification SC: Near Surface Geophysics (NS) MN: 2009 Fall Meeting N ew Search AGU Home

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