Value of information of time-lapse seismic data by simulation-regression: comparison with double-loop Monte Carlo

Abstract

Simulation-regression is a computationally efficient methodology to estimate the value of information (VOI), as it involves directly estimating the value outcomes corresponding to different data realizations by building a statistical relationship between the prospect values and the data, rather than estimating the model parameters from the data and then estimating the value outcomes given the model parameters. The simulation-regression workflow is applied to estimate the VOI of time-lapse seismic data in a 2D reservoir case using partial least squares regression (PLSR) and principal components regression (PCR), and the variance in the VOI result is estimated using bootstrapping for a varying number of realizations. The VOI results from the two regression techniques are found to be consistent, and it is seen from the bootstrap results that the variance in the VOI decreases with an increasing number of realizations and the VOI ranges obtained by a higher number of realizations are captured by those obtained by fewer realizations. The VOI results from simulation-regression are then compared with those obtained by a double-loop Monte Carlo method, where the posterior model realizations are sampled using rejection sampling for each possible data realization, and then the prospect values are estimated for each model realization using flow simulation. Finally, the simulation-regression method is applied to estimate the VOI of time-lapse seismic data in a complex production optimization case involving sequential well placement and control decisions.