We develop Bayesian methodology for integrating the use of controlled source electromagnetic (CSEM) data for exploration. We compute the probability for hydrocarbon presence based on the information in the CSEM data. The method is fast compared with Bayesian inversion techniques, and avoids inversion for the complete resistivity model it computes the chance of hydrocarbon presence directly. The method utilize that CSEM data from hydrocarbon reservoirs and brine filled reservoirs will have different signatures. The methodology adapts to the local geology. Detailes of the algorithmes and displayed cases are found in Kolbjørnsen et. al. (2012).

Figure: The signature for multiple hydrocarbon reservoirs (success) and brine reservoirs (failure).

The method is tested on data from Troll where it provides evidence for hydrocarbon presence and on another data set where it provides evidence against hydrocarbons.

Figure: Risk update for CSEM line at Troll. The figure shows  large probability for hydrocarbons at Troll.

Figure: Risk update for a CSEM line. The figure show reduced probability for hydrocarbons.

The methodology can also be used to find the value of CSEM data before data is collected. The value of the CSEM data will off course depend on the price of drilling the prospect and the potential value, as well as the initial estimate of probability of hydrocarbon presence.

Figure: Value of CSEM data.

The figure shows that CSEM data have little value if the initial probability of hydrocarbon presence is large, or the prospect value is large, for these cases it is unlikely that the CSM data will affect the decision, it will be drilled anyway. Also if the initial prospect value is small and the initial probability of hydrocarbon presence is small, the CSM data will not affect the decision, the prospect will not be drilled. However in the middle zone the value of CSEM data can be as large as 50mUSD.