Seabed logging (SBL) is an application of the marine controlled-source
electromagnetic (CSEM) technique to discover offshore hydrocarbon reservoirs
underneath the seabed. This application is based on electrical resistivity
contrast between hydrocarbon and its surroundings. In this paper, simulation
and forward modeling were performed to estimate the hydrocarbon depths in
one-dimensional (1-D) SBL data. 1-D data, consisted offset distance (input)
and magnitude of electric field (output), were acquired from SBL models
developed using computer simulation technology (CST) software. The computer
simulated outputs were observed at various depths of hydrocarbon reservoir
(250 m–2,750 m with an increment of 250 m) with frequency of 0.125 Hz.
Gaussian processes (GP) was employed in the forward modeling by utilizing
prior information which is electric field (E-field) at all observed inputs
to provide E-field profile at unobserved/untried inputs with uncertainty
quantification in terms of variance. The concept was extended for
two-dimensional (2-D) model. All observations of E-field were then
investigated with the 2-D forward GP model. Root mean square error (RMSE)
and coefficient of variation (CV) were utilized to compare the acquired and
modeled data at random untried hydrocarbon depths at 400 m, 950 m, 1,450 m,
2,100 m and 2,600 m. Small RMSE and CV values have indicated that developed
model can fit well the SBL data at untried hydrocarbon depths. The measured
variances of the untried inputs revealed that the data points (true values)
were very close to the estimated values, which was 0.003 (in average). RMSEs
obtained were very small as an average of 0.049, and CVs found as very
reliable percentages, an average of 0.914%, which implied well fitting of
the GP model. Hence, the 2-D forward GP model is believed to be capable of
predicting unobserved hydrocarbon depths.