A B S T R A C TThis paper investigates the effects of uncertainty in rock-physics models on reservoir parameter estimation using seismic amplitude variation with angle and controlledsource electromagnetics data. The reservoir parameters are related to electrical resistivity by the Poupon model and to elastic moduli and density by the Xu-White model. To handle uncertainty in the rock-physics models, we consider their outputs to be random functions with modes or means given by the predictions of those rock-physics models and we consider the parameters of the rock-physics models to be random variables defined by specified probability distributions. Using a Bayesian framework and Markov Chain Monte Carlo sampling methods, we are able to obtain estimates of reservoir parameters and information on the uncertainty in the estimation. The developed method is applied to a synthetic case study based on a layered reservoir model and the results show that uncertainty in both rock-physics models and in their parameters may have significant effects on reservoir parameter estimation. When the biases in rock-physics models and in their associated parameters are unknown, conventional joint inversion approaches, which consider rock-physics models as deterministic functions and the model parameters as fixed values, may produce misleading results. The developed stochastic method in this study provides an integrated approach for quantifying how uncertainty and biases in rock-physics models and in their associated parameters affect the estimates of reservoir parameters and therefore is a more robust method for reservoir parameter estimation. I N T R O D U C T I O NRock-physics models are needed for reservoir parameter estimation using seismic amplitude variation with angle (AVA) and controlled-source electromagnetics (CSEM) data. In practice, these models are often derived from suitable nearby borehole logs. Firstly, appropriate families of rock-physics models are chosen. For instance, we may choose a sand-clay model by Dvorkin and Nur (1996), the Xu-White model by Xu and White (1995) or a differential effective medium model by * (1985) and Mavko, Mukerji and Dvorkin (1998) for seismic AVA data and we may choose Archie's law (Archie 1942) or the Poupon model (Poupon, Loy and Tixier 1954;Poupon and Leveaux 1971) for CSEM data. NorrisSecondly, the parameters in the chosen rock-physics models are estimated by fitting them to the selected borehole logs. Since relationships between the reservoir parameters and the geophysical attributes are often nonlinear and non-unique, the derived rock-physics models are inevitably subject to uncertainty and even unknown biases. These may include uncertainty and biases due to an inappropriate choice of the rockphysics model families and those due to inaccurate estimation of the associated model parameters.

No abstract

No abstract

No abstract

Large-scale controlled-source electromagnetic (CSEM) threedimensional (3D) geophysical imaging is now receiving considerable attention for electrical-conductivity mapping of potential offshore oil and gas reservoirs. To cope with the typically large computational requirements of the 3D CSEM imaging problem, our strategies exploit computational parallelism and optimized finite-difference meshing. We report on an imaging experiment utilizing 32,768 tasks (and processors) on the IBM Blue Gene/Le (BG/L) supercomputer at the IBM T. J. Watson Research Center. Over a 24-hour period, we were able to image a large-scale marine CSEM field dataset that previously required more than 4 months of computing time on distributed clusters utilizing 1,024 tasks on an InfiniBandt fabric. The total initial data-fitting errors (i.e., ''misfits'' ) could be decreased by 67% within 72 completed inversion iterations, indicating the existence of an electrically resistive region in the southern survey area below a depth of 1,500 m underneath the seafloor. The major part of the residual misfit stems from transmitter-parallel receiver components that have an offset from the transmitter sail line (broadside configuration). Modeling confirms that improved broadside data fits can be achieved by considering anisotropic electrical conductivities. While delivering a satisfactory gross-scale image for the depths of interest, the experiment provides important evidence for the necessity of discriminating between horizontal and vertical conductivities for maximally consistent 3D CSEM inversions.