Data assimilation of a legacy 4D seismic in a brown field

The quantitative use of four-dimensional (4D) seismic data in an assisted history matching process is still a major challenge in the industry. The problem becomes even more challenging when the quality of the seismic is deficient. Legacy 4D seismic data, i.e. 4D data from seismic acquisitions that were not originally parameterized for time-lapse processing, are typically regarded as not suitable for quantitative approaches. On the other hand, this kind of data are very common in the petroleum industry. Therefore, there is a practical interest in determining whether these data can bring useful quantitative information to improve reservoir characterization through assisted history matching. In this work, we investigate the use of low-quality 4D seismic data (legacy surveys with low repeatability) in an ensemble data assimilation process. We use data from a carbonate reservoir in Campos Basis with a long production history (38 years). The seismic data are the acoustic impedance from a joint 4D inversion. To overcome the overall low quality of the data, we propose to assimilate the seismic data only within the interpreted anomalies regions. We compare the data assimilation results considering only production data and different configurations combining production and seismic data. The results show that we were able to simultaneously assimilate production and seismic data. In our best case, we obtained a production data match with the same quality of a case using only production data, while significantly improving the seismic data match.

[1]  Geir Evensen,et al.  History Matching Using the Ensemble Kalman Filter on a North Sea Field Case , 2008 .

[2]  Albert C. Reynolds,et al.  History-Matching Production and Seismic Data in a Real Field Case Using the Ensemble Smoother With Multiple Data Assimilation , 2013, ANSS 2013.

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

[4]  Michael Andrew Christie,et al.  Multiple model seismic and production history matching: a case study , 2004 .

[5]  Dean S. Oliver,et al.  History Matching Using the Ensemble Kalman Filter With Multiscale Parameterization: A Field Case Study , 2011 .

[6]  Albert C. Reynolds,et al.  Ensemble smoother with multiple data assimilation , 2013, Comput. Geosci..

[7]  Dean S. Oliver,et al.  Ensemble-Based Closed-Loop Optimization Applied to Brugge Field , 2010 .

[8]  Zhijing Wang,et al.  Seismic properties of pore fluids , 1992 .

[9]  Alexandre A. Emerick,et al.  Deterministic ensemble smoother with multiple data assimilation as an alternative for history-matching seismic data , 2018, Computational Geosciences.

[10]  Geir Nævdal,et al.  HISTORY MATCHING AND PRODUCTION FORECAST UNCERTAINTY BY MEANS OF THE ENSEMBLE KALMAN FILTER: A REAL FIELD APPLICATION , 2007 .

[11]  David H. Johnston,et al.  Practical Applications of Time-lapse Seismic Data , 2013 .

[12]  Dean S. Oliver,et al.  THE ENSEMBLE KALMAN FILTER IN RESERVOIR ENGINEERING-A REVIEW , 2009 .

[13]  Gioia Falcone,et al.  Petroelastic Modelling as Key Element of 4D History Matching: A Field Example , 2004 .

[14]  Sigurd Ivar Aanonsen,et al.  Ensemble Based 4D Seismic History Matching: Integration of Different Levels and Types of Seismic Data , 2010 .

[15]  T. Mukerji,et al.  The Rock Physics Handbook , 1998 .

[16]  Dean S. Oliver,et al.  History Matching of the Norne Full-Field Model With an Iterative Ensemble Smoother , 2014 .

[17]  Xiaodong Luo,et al.  Estimating observation error covariance matrix of seismic data from a perspective of image denoising , 2016, Computational Geosciences.

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

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

[20]  Hari Hara Sudan,et al.  Ekofisk 4D Seismic - Seismic History Matching Workflow , 2012 .

[21]  Hilde Meisingset,et al.  Using the EnKF for Assisted History Matching of a North Sea Reservoir Model , 2007 .

[22]  Alexandre A. Emerick.  Analysis of the performance of ensemble-based assimilation of production and seismic data , 2016 .

[23]  J. R. Waggoner,et al.  Improved Reservoir Modeling With Time-Lapse Seismic in a Gulf of Mexico Gas Condensate Reservoir , 2002 .

[24]  F. Gassmann,et al.  Elastic waves through a packing of spheres , 1951 .

[25]  D. Brito,et al.  Incorporation of 4D Seismic in the Re-construction and History Matching of Marlim Sul Deep Water Field Flow Simulation Model , 2011 .

[26]  Geir Nævdal,et al.  Benchmark Study of Ensemble Kalman Filter Methodology: History Matching and Uncertainty Quantification for a Deep-Water Oil Reservoir , 2009 .

[27]  Albert C. Reynolds,et al.  History Matching a Field Case Using the Ensemble Kalman Filter with Covariance Localization , 2011, ANSS 2011.

[28]  A. Reynolds,et al.  History matching time-lapse seismic data using the ensemble Kalman filter with multiple data assimilations , 2012, Computational Geosciences.

[29]  S. Cohn,et al.  Ooce Note Series on Global Modeling and Data Assimilation Construction of Correlation Functions in Two and Three Dimensions and Convolution Covariance Functions , 2022 .

[30]  R. Kjelstadli,et al.  Quantitative History Match of 4D Seismic Response and Production Data in the Valhall Field , 2005 .

[31]  Tor Arne Johansen,et al.  Incorporating 4D Seismic Data in Reservoir Simulation Models Using Ensemble Kalman Filter , 2007 .