We propose a method for computing the value of information in petroleum exploration, a field in which decisions regarding seismic or electromagnetic data acquisition and processing are critical. We estimate the monetary value, in a certain context, of a seismic amplitude or electromagnetic-resistivity data set before purchasing the data. The method is novel in the way we incorporate spatial dependence to solve large-scale, real-world problems by integrating the decision-theoretical concept of value of information with rock physics and statistics. The method is based on a statistical model for saturation and porosity on a lattice along the top reservoir. Our model treats these variables as spatially correlated. The porosity and saturation are tied to the seismic and electromagnetic data via nonlinear rock-physics relations. We efficiently approximate the posterior distribution for the reservoir variables in a Bayesian model by fitting a Gaussian at the posterior mode for transformed versions of saturation and porosity. The value of information is estimated based on the prior and posterior distributions, the possible revenues from the reservoir, and the cost of drilling wells. We illustrate the method with three examples.