Model-based fluid factor for controlled source electromagnetic data

We have developed a new Bayesian methodology for use of controlled source electromagnetic (CSEM) data in prospect risking. The methodology modifies the probability for hydrocarbon presence based on the information in the CSEM data. This is done by identifying the linear combination of the data where the weighted sum provides the optimal separation of cases with and without hydrocarbons. The weighted sum of the CSEM data is a model-based parallel to the well-known fluid factor used in seismic amplitude analysis. The optimal weighting is constructed based on forward modeling of models with and without hydrocarbons. We further compute the statistical distributions of the fluid factor with and without hydrocarbon presence, and use these distributions to compute the value of CSEM data and update the probability for hydrocarbon presence. The method does not involve inversion for the complete resistivity model but computes the chance of hydrocarbon presence directly. The method was tested on data from Troll, where it provided evidence for hydrocarbon presence, and on another data set, where it provided evidence against hydrocarbons. These real case examples showed the importance of rock physical knowledge. Improved control on the rock properties leads to stronger conclusions and higher value of the CSEM data. The proposed model-based approach is fast and robust.