Integrating Data of Different Types and Different Supports into Reservoir Models

In this paper, we focus on the joint integration of production and 4-D inverted seismic data into reservoir models. These data correspond to different types and different scales. Therefore, we developed two-scale simulation workflows making it possible to incorporate data at the right scale. This issue also emphasized the need for adapting traditional history-matching methodologies. For instance, the formulation of the objective function and the development of customized parameterization techniques turned out to be two key factors controlling the efficiency of the matching process. Two application examples are presented. The first one is a small-size synthetic field case. It aims to build a set of reservoir models respecting either production data only or both production and 4-D seismic-related data. It is shown that the incorporation of 4-D seismic-related data in addition to production data into reservoir models contributes to reduce the uncertainty in production forecasts. The second example is a field in the North Sea offshore Norway operated by Statoil . It stresses difficulties in conditioning reservoir models to both real production and 4-D inverted seismic data among the very large number of uncertain parameters to handle and the comparison of real noisy data with numerical responses.

[1]  Jean-Paul Chilès,et al.  Combining geostatistics and flow simulators to identify transmissivity , 1998 .

[2]  G. Matheron.  Les variables régionalisées et leur estimation : une application de la théorie de fonctions aléatoires aux sciences de la nature , 1965 .

[3]  Olivier Gosselin,et al.  Effect of Scale Dependent Data Correlations in an Integrated History Matching Loop Combining Production Data and 4D Seismic Data , 2003 .

[4]  Mathieu Feraille,et al.  Joint History Matching of Production and 4D-Seismic Related Data for a North Sea Field Case , 2010 .

[5]  Patrick Jenny,et al.  Adaptive fully implicit multi-scale finite-volume method for multi-phase flow and transport in heterogeneous porous media , 2006, J. Comput. Phys..

[6]  M. Marietta,et al.  Pilot Point Methodology for Automated Calibration of an Ensemble of conditionally Simulated Transmissivity Fields: 1. Theory and Computational Experiments , 1995 .

[7]  Didier Yu Ding,et al.  History Matching of Production and 4D Seismic Data: Application to the Girassol Field, Offshore Angola , 2012 .

[8]  Andres Alcolea,et al.  Inverse problem in hydrogeology , 2005 .

[9]  K. Aziz,et al.  Petroleum Reservoir Simulation , 1979 .

[10]  T. Mukerji,et al.  The Rock Physics Handbook: Contents , 2009 .

[11]  J. Kiefer,et al.  Stochastic Estimation of the Maximum of a Regression Function , 1952 .

[12]  Dmitry Eydinov,et al.  A multiscale method for distributed parameter estimation with application to reservoir history matching , 2006 .

[13]  Jef Caers,et al.  Regional probability perturbations for history matching , 2005 .

[14]  David E. Goldberg,et al.  Genetic Algorithms in Pipeline Optimization , 1987 .

[15]  Sébastien Da Veiga,et al.  Cosimulation as a perturbation method for calibrating porosity and permeability fields to dynamic data , 2011, Comput. Geosci..

[16]  Elodie Tillier,et al.  Simultaneous Inversion of Production Data and Seismic Attributes: Application to a Synthetic SAGD Produced Field Case , 2012 .

[17]  Veronique Gervais,et al.  Local adaptive parameterization for the history matching of 3D seismic data , 2012, Computational Geosciences.

[18]  Graham E. Fogg,et al.  Multi-scale alluvial fan heterogeneity modeled with transition probability geostatistics in a sequence stratigraphic framework , 1999 .

[19]  Jef Caers,et al.  Modeling Spatial Uncertainty , 2011 .

[20]  G. Chavent,et al.  History Matching by Use of Optimal Theory , 1975 .

[21]  Jef Caers,et al.  Geostatistical History Matching Under Training-Image Based Geological Model Constraints , 2002 .

[22]  J. Chilès,et al.  Geostatistics: Modeling Spatial Uncertainty , 1999 .

[23]  Frederic Roggero,et al.  History Matching Using Local Gradual Deformation , 2007 .

[24]  Andre G. Journel,et al.  Stochastic modelling of a fluvial reservoir: a comparative review of algorithms , 1998 .

[25]  Mickaele Le Ravalec-Dupin,et al.  Pilot Block Method Methodology to Calibrate Stochastic Permeability Fields to Dynamic Data , 2010 .

[26]  S. P. Neuman.  Calibration of distributed parameter groundwater flow models viewed as a multiple‐objective decision process under uncertainty , 1973 .

[27]  D. Oliver,et al.  Recent progress on reservoir history matching: a review , 2011 .

[28]  G. Matheron.  Principles of geostatistics , 1963 .

[29]  J. Rehling,et al.  Draugen Field – Successful Reservoir Management Using 4D Seismic , 2003 .

[30]  Andrew Dilay,et al.  Seismic monitoring of steam‐based recovery of bitumen , 1994 .

[31]  L. Hu.  Gradual Deformation and Iterative Calibration of Gaussian-Related Stochastic Models , 2000 .

[32]  F. Roggero,et al.  Constraining Reservoir Models to Production And 4D Seismic Data - Application to the Girassol Field, Offshore Angola , 2008 .

[33]  Ronald A. Behrens,et al.  4D Seismic Monitoring of Water Influx at Bay Marchand: The Practical Use of 4D in an Imperfect World , 2002 .

[34]  Veronique Gervais,et al.  Integration of saturation data in a history matching process based on adaptive local parameterization , 2010 .

[35]  J. Delhomme.  Kriging in the hydrosciences , 1978 .

[36]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[37]  Benoît N∄tinger,et al.  The effective permeability of a heterogeneous porous medium , 1994 .

[38]  L. V. D. Meer,et al.  Monitoring of CO2 injected at Sleipner using time-lapse seismic data , 2004 .

[39]  Véronique Gervais,et al.  Advanced Integrated Workflows for Incorporating Both Production and 4D Seismic-Related Data into Reservoir Models , 2012 .

[40]  Thomas L. Davis,et al.  Time-Lapse Seismic Monitoring and Dynamic Reservoir Characterization, Central Vacuum Unit, Lea County, New Mexico , 2000 .

[41]  F. Gaßmann.  Uber die Elastizitat poroser Medien. , 1961 .

[42]  Yalchin Efendiev,et al.  Accurate multiscale finite element methods for two-phase flow simulations , 2006, J. Comput. Phys..

[43]  A. Tarantola.  Inverse problem theory : methods for data fitting and model parameter estimation , 1987 .

[44]  Xian-Huan Wen,et al.  Efficient Conditioning of 3D Fine-Scale Reservoir Model To Multiphase Production Data Using Streamline-Based Coarse-Scale Inversion and Geostatistical Downscaling , 1999 .

[45]  Darryl Fenwick,et al.  A Combined Geostatistical and Streamline-Based History Matching Procedure , 2002 .

[46]  D. Denney.  4D Seismic Monitoring of Water Influx at Bay Marchand , 2002 .

[47]  Martin J. Blunt,et al.  Nested gridding and streamline-based simulation for fast reservoir performance prediction , 1999 .

[48]  A. R. Syversveen,et al.  Methods for quantifying the uncertainty of production forecasts: a comparative study , 2001, Petroleum Geoscience.

[49]  R. Holt.  Conceptual model for transport processes in the Culebra Dolomite Member, Rustler Formation , 1997 .

[50]  Gillian Elizabeth Pickup,et al.  The Development of Appropriate Upscaling Procedures , 2000 .

[51]  N. Sun.  Inverse problems in groundwater modeling , 1994 .

[52]  Raymond D. Mindlin,et al.  Compliance of elastic bodies in contact , 1949 .