qht 1~, SmieW d Petroleum Engin-s, lnc This paw was~epared fcf presenlaticm al lhs 19SS SPE Annual Ttiniml Conferenm and ExhbiUon held in Naw Orleans, Louisiana, 27-3o Septemk leS8, This papar was salecfad for pre=lation by an SPE Prcgrsm Committas followi~raview of matim con-h an abstract submitted by the authofls) Cmtents d the ps~r, as Prasentd, have not kn reviawed by the Sociaty of Petroleum Engineers md are eubjed tõ n by~6-auWTsJ me malarial, as prsaented, doss not mssarily reflect my pitl~cd ths Society of Petroleum Er,ginaers, tis o~~s, or membrs. Papars presented at SPE maafings ara subjact to~blimtion review by Editwisl Committals of the Society of Petroleum Engineers. Electronic reprcdtion, distribution, or storage ci any part d this paper fcf annmercial Furposas wihut the writtm mnserd of the Stiety of Petroleum Engineem is prohbited,Permission to r~cduce in print is restricted to an abstract of not more than 300 wnrds illustrafms may not be mpiecf The abstr~must czmtain mspiwous edgmenl of hre and by Mm Ihs papar was~sented Wtte LiMarian, SPE, P.O. Box B33B3B, Rtisrdsors, TX 750s3-383s, U.S.A, fax 01-972-952-9435. AbstractOne of the outstandingchallengesin reservoir characterization is to build high resolution reservoir models that satisfjr static as well as dynamic data. However, integration of dynamic data typically requires the solution of an inverse problem that can be computationally intensive and becomes practically infeasible for free-scale reservoir models. A critical issue here is computation of sensitivity coefficients, the derivatives of dynamic production history with respect to model parameters such as permeability and porosity.We propose a new analytic technique that has several advantages over existing approaches. First, the method utilizes an extremely efficient three-dimensional multiphase streamline simulator as a forward model. Second, the parameter sensitivities are formulated in terms of onedimensional integrals of analytic functions along the streamlines.~us, the computation of sensitivities for all model parameters requires only a single simulation run to construct the velocity field and generate the streamlines. The integration of dynamic data is then performed using a two-step iterative inversion that involves (i) 'lining-up' the breakthrough times at the producing wells and then (ii) matching the production history. Our approach follows from an anaIogy between streamlhes and ray tracing in seismology. The inverse method is analogous to seismic waveform inversion and thus, allows us to utilize efficient methods from geophysical imaging. me feasibi~OTour proposed approach for large-s~e field applications has been demonstrated by integrating production response directly into three dimensional reservoir models consisting of 31500 grid blocks in less than 3 hours in a Silicon Graphics without any artificial reduction of parameter space, for example, through the use of 'pilot points'. Use of 'pilot points' will allow us to substantially increase the model size without any signifi...

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractWe propose a multiscale approach to data integration that accounts for the varying resolving power of different data types from the very outset. Starting with a very coarse description, we match the production response at the wells by recursively refining the reservoir grid. A multiphase streamline simulator is utilized for modeling fluid flow in the reservoir. The well data is then integrated using conventional geostatistics, for example sequential simulation methods. There are several advantages to our proposed approach. First, we explicitly account for the resolution of the production response by refining the grid only up to a level sufficient to match the data, avoiding over-parameterization and incorporation of artificial regularization constraints. Second, production data is integrated at a coarse-scale with fewer parameters, which makes the method significantly faster compared to direct fine-scale inversion of the production data. Third, decomposition of the inverse problem by scale greatly facilitates the convergence of iterative descent techniques to the global solution, particularly in the presence of multiple local minima. Finally, the streamline approach allows for parameter sensitivities to be computed analytically using a single simulation run and thus, further enhancing the computational speed.The proposed approach has been applied to synthetic as well as field examples. The synthetic examples illustrate the validity of the approach and also address several key issues such as convergence of the algorithm, computational efficiency, and advantages of the multiscale approach compared to conventional methods. The field example is from the Goldsmith San Andres Unit (GSAU) in West Texas and includes multiple patterns consisting of 11 injectors and 31 producers. Using well log data and water-cut history from producing wells, we characterize the permeability distribution, thus demonstrating the feasibility of the proposed approach for large-scale field applications.