Abstract
The Monte Carlo rejection and Monte Carlo Markov Chain methods are two common sampling techniques in Bayesian seismic inversion.Conventional methods of pseudo well stochastic seismic inversion for reservoir properties uses Monte Carlo rejection sampling of pseudo-wells to select those that best match seismic traces.The selected pseudo-wells do not influence the selection/rejection of subsequent pseudo-wells.The process requires a very large number of prior pseudo-wells to draw a reasonable outcome,which consumes a major part of the computation time.In conventional methods,the best-matched pseudo-wells often have dissimilarly shaped volume of shale logs,which can degrade the resolution of the outcome.Though Agglomerative clustering is well-known but the idea of grouping pseudo-wells based on similarly shaped volume of shale logs in pseudo well stochastic seismic inversion is unique.We propose an agglomerative clustering technique to group the prior pseudo-wells according to the shape of their volume of shale logs,before matching to seismic.The centroids of the pseudo-well clusters summarize the large number of prior pseudo-wells.The best-matched pseudo-well cluster centroid with seismic gives the most likely outcome while the two next best-matched cluster centroids give alternative results.The alternative results are useful to understand the uncertainty in the outcome.It has been observed that,the proposed method has reduced computation time(50-55% less than conventional pseudo well stochastic inversion).Meanwhile,the quantification of sand volume from the proposed method shows improvement over the conventional method.A comparison between the proposed and the conventional method has been shown on a synthetic wedge model and real seismic.In the wedge model(volume of sand is 0.65),the resultant volume of sand predicted in the modified method(0.7)is better than that predicted using the conventional method(0.8).The modified pseudo-well stochastic seismic inversion method has been applied on seismic data from deepwater Krishna-Godavari basin.The result shows an improvement in the characterization of thin shale layers within the reservoir.
NSTL