The stochastic stratigraphic well correlation method considers the stratigraphic correlation of well data as a set of possible models to sample and manage uncertainty in subsurface studies. This method was applied to the Malampaya buildup (a well documented offshore gas field located NW of the Palawan Island, Philippines), aged upper Eocene to lower Miocene. Previous studies highlight that rock petrophysical properties are mainly controlled by diagenesis. Correlation rules are thus developed in order to adapt the stochastic stratigraphic well correlation method to the study of diagenetic units. These rules are based on wireline log shape and diagenetic units types. Four stratigraphic correlation models are generated using the proposed correlation method: a deterministic one corresponding to the most probable model considering only well data and three stochastic ones. These correlation models are bound with geostatistical methods to build static reservoir models. Synthetic seismic profiles are computed from facies models conditioned to acoustic impedance models. It leads to comparable seismic amplitude images, highlighting the importance of considering several well correlation models for one given seismic survey. Stochastic stratigraphic correlations are shown to have a first-order impact on reservoir unit characterization, rock volumes and fluid flow response on the reservoir model.
International audienceEnsemble-based optimization methods are often efficiently applied to history-matching problems. Although satisfactory matches can be obtained, the updated realizations, affected by spurious correlations, generally fail to preserve prior information when using a small ensemble, even when localization is applied. In this work, we propose a multi-scale approach based on grid-adaptive second-generation wavelets. These wavelets can be applied on irregular reservoir grids of any dimensions containing dead or flat cells. The proposed method starts by modifying a few low frequency parameters (coarse scales) and then progressively allows more important updates on a limited number of sensitive parameters of higher resolution (fine scales). The Levenberg-Marquardt ensemble randomized maximum likelihood (LM-enRML) is used as optimization method with a new space-frequency distance-based localization of the Kalman gain, specifically designed for the multi-scale scheme. The algorithm is evaluated on two test cases. The first test is a 2D synthetic case in which several inversions are run using independent ensembles. The second test is the Brugge benchmark case with 10 years of history. The efficiency and quality of results of the multi-scale approach are compared with the grid-block-based LM-enRML with distance-based localization. We observe that the final realizations better preserve the spatial contrasts of the prior models and are less noisy than the realizations updated using a standard grid-block method, while matching the production data equally well
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