Realization ranking of seismic geostatistical inversion based on a Bayesian lithofacies classification - A case study from an offshore field.

Seismic inversion which is defined as the process of converting seismic boundary reflections into layer elastic properties plays a vital role in different methods of seismic reservoir characterization. It can be done through either deterministic or geostatistical approaches, and geostatistical inversion is the recommended approach for heterogeneous and complicated reservoir due to its capability in capturing the details of a reservoir with the associated uncertainties. However, a common issue with the geostatistical approach is generating a large number of realizations with the same probability which needs to be ranked based on some pre-defined criteria. This study applied a pre-stack geostatistical inversion on an offshore field in the Persian Gulf using a Bayesian framework to produce the joint posterior probability density function (PDF) of P-wave seismic impedance (I P) and S-wave seismic impedance (I S). This global PDF was then, decomposed into local ones at the trace locations using a sequential Gaussian simulation technique. Furthermore, these local PDFs were sampled and 250 realizations of I P and I S generated accordingly. These elastic properties were then converted into lithofacies, and were ranked based on the oil-producing volume lithofacies. Well log data showed a useful distinction of the oil sand lithofacies in the I P vs. V P /V S crossplot, and this domain was used to compute the PDFs for classifying lithofacies derived from the seismic inversion. Moreover, the oil lithofacies volumes were calculated, and the realizations were sorted to generate the probability of P10, P50 and P90 scenarios. Also, the probability of oil presence within the reservoir was computed using all of the classified realizations. • A common issue with geostatistical inversion is generating many realizations. • Oil facies volume is a proper criterion for realization ranking. • Different lithofacies are separated in acoustic impedance versus Vp/Vs crossplot. • Rock physics template improves the lithofacies probability density functions. • Getting away from well site reduces the oil probability due to uncertainties. [ABSTRACT FROM AUTHOR]