Fracture Density Prediction using Response Surface in Naturally Fractured Reservoirs

in: 26th gOcad Meeting, ASGA

Abstract

Natural fractures have a dramatic impact on reservoirs in term of oil recovery because they often control the hydraulic flow as conductors (open fractures) or barriers (sealed fractures). However, fracture parameters are poorly constrained by reservoir data, due to the low seismic resolution and to the clustering of 1D data along wells. Secondary data (such as seismic, structural data) are generally related, but indirectly, to fracture distribution. The integration of all, primary and secondary, available data becomes mandatory to understand and model fracture networks at field scale. This paper investigates the potentiality of an innovative approach using response surface analysis in fractured reservoir characterization. The response surface analysis is a multivariate methodology that aims at predicting the value of a dependent response variable from a collection of independent predictor variables. Multivariate functions are built in order to approximate an unknown relationship between the set of predictors and the response. A complex relationship links static measures of fracture density with many possible geological drivers (e.g. structure, thickness, lithology, faults and porosity). Here, the response surface considered is the fracture density. Consequently, these drivers are non-linearly combined to predict a fracture density index. And this combination is calibrated from well data. The mismatch between the index and well data reflects the quality of the fracturing model. The 3D fracture distribution and the underlying uncertainty can be then estimated at non-drilled locations to optimize the future field development. This methodology is illustrated on a faulted and fractured reservoir real case study.

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    BibTeX Reference

    @inproceedings{MacéRM2006,
     abstract = { Natural fractures have a dramatic impact on reservoirs in term of oil recovery because they often control the hydraulic flow as conductors (open fractures) or barriers (sealed fractures). However, fracture parameters are poorly constrained by reservoir data, due to the low seismic resolution and to the clustering of 1D data along wells. Secondary data (such as seismic, structural data) are generally related, but indirectly, to fracture distribution. The integration of all, primary and secondary, available data becomes mandatory to understand and model fracture networks at field scale. This paper investigates the potentiality of an innovative approach using response surface analysis in fractured reservoir characterization. The response surface analysis is a multivariate methodology that aims at predicting the value of a dependent response variable from a collection of independent predictor variables. Multivariate functions are built in order to approximate an unknown relationship between the set of predictors and the response. A complex relationship links static measures of fracture density with many possible geological drivers (e.g. structure, thickness, lithology, faults and porosity). Here, the response surface considered is the fracture density. Consequently, these drivers are non-linearly combined to predict a fracture density index. And this combination is calibrated from well data. The mismatch between the index and well data reflects the quality of the fracturing model. The 3D fracture distribution and the underlying uncertainty can be then estimated at non-drilled locations to optimize the future field development. This methodology is illustrated on a faulted and fractured reservoir real case study. },
     author = { Mace, Laetitia AND Fetel, Emmanuel },
     booktitle = { 26th gOcad Meeting },
     month = { "june" },
     publisher = { ASGA },
     title = { Fracture Density Prediction using Response Surface in Naturally Fractured Reservoirs },
     year = { 2006 }
    }