L. Guerreiro

Stochastic Inversion with a Global Perturbation Method

Geostatistics has been commonly used in forward modeling and in inverse modeling to integrate seismic information in stochastic fine gride models. The quality of seismic and the downscaling of seismic attributes to the fine grid of the well measurements are still challenges to which existing geostatistical methods only give partial answers. In this paper an iterative inversion methodology is proposed based on a direct sequential simulation and co-simulation approaches. Several images of acoustic impedances of entire field are simulated in a first step. Afterwards, co-simulations are used for the global transformation of images of acoustic impedances in an iterative process: after the convolution, local areas of best fit of the different images are selected and “merged” into a secondary image for the direct co-simulation of the next iteration. The iterative and convergent process continues until a given match with an objective function is reached. Spatial dispersion and patterns of acoustic impedances (histograms and variograms) are reproduced at the final acoustic impedance cube.

Geostatistical seismic inversion for frontier exploration

AbstractNumerical 3D high-resolution models of subsurface petroelastic properties are key tools for exploration and production stages. Stochastic seismic inversion techniques are often used to infer the spatial distribution of the properties of interest by integrating simultaneously seismic reflection and well-log data also allowing accessing the spatial uncertainty of the retrieved models. In frontier exploration areas, the available data set is often composed exclusively of seismic reflection data due to the lack of drilled wells and are therefore of high uncertainty. In these cases, subsurface models are usually retrieved by deterministic seismic inversion methodologies based exclusively on the existing seismic reflection data and an a priori elastic model. The resulting models are smooth representations of the real complex geology and do not allow assessing the uncertainty. To overcome these limitations, we have developed a geostatistical framework that allows inverting seismic reflection data without...

Geostatistical Seismic Inversion Applied to a Carbonate Reservoir

A complex Middle East carbonate reservoir was selected to test a new stochastic seismic inversion method. A small zone of the seismic cube with scarce sonic data was used. The main goal was to check if even in very hard conditions the new method could capture the main geological features of the reservoirs. The method is based on an iterative approach where the seismic acoustic impedances models are co-simulate using auxiliary data which is generate in the previously iteration. A global correlation of 88% was achieved between the real and the synthetic seismic data. The major geological features, such as the complex structures of the clinoformes, are reproduced in acoustic impedances final image.

Estimation of Reserves at Different Phases in the History of an Oil Field

To each phase of a petroleum reservoir description study corresponds a given level of available information, at a certain scale. In the end of each phase, a set of estimations must be performed in order to sustain the decisions required to proceed the venture. In this case study, concerning a Middle East oil field, it is aimed to assess, for each phase in the history of the reservoir, the performance of different estimation techniques — FAI-K, UK, Median Polish, Trend Surface Analysis — and their impact on oil reserves and estimation errors.