No abstract
Deep neural networks (DNNs) have the potential to streamline the integration of seismic data for reservoir characterization by providing estimates of rock properties that are directly interpretable by geologists and reservoir engineers instead of elastic attributes like most standard seismic inversion methods. However, they have yet to be applied widely in the energy industry because training DNNs requires a large amount of labeled data that is rarely available. Training set augmentation, routinely used in other scientific fields such as image recognition, can address this issue and open the door to DNNs for geophysical applications. Although this approach has been explored in the past, creating realistic synthetic well and seismic data representative of the variable geology of a reservoir remains challenging. Recently introduced theory-guided techniques can help achieve this goal. A key step in these hybrid techniques is the use of theoretical rock-physics models to derive elastic pseudologs from variations of existing petrophysical logs. Rock-physics theories are already commonly relied on to generalize and extrapolate the relationship between rock and elastic properties. Therefore, they are a useful tool to generate a large catalog of alternative pseudologs representing realistic geologic variations away from the existing well locations. While not directly driven by rock physics, neural networks trained on such synthetic catalogs extract the intrinsic rock-physics relationships and are therefore capable of directly estimating rock properties from seismic amplitudes. Neural networks trained on purely synthetic data are applied to a set of 2D poststack seismic lines to characterize a geothermal reservoir located in the Dogger Formation northeast of Paris, France. The goal of the study is to determine the extent of porous and permeable layers encountered at existing geothermal wells and ultimately guide the location and design of future geothermal wells in the area.
SummaryBroadband acquisition and dedicated processing can be regarded as one of the most recent innovations in Geophysics. The benefits of a new dataset acquired using broadband technology, BroadSeis®, and the 3D elastic inversion results are used to determine robust high-resolution estimates for P-impedance and Poisson's Ratio volumes leading to a more reliable reservoir characterization. In this study we show the impact of broadband in key stages of the reservoir characterisation workflow (inversion and pseudo-Vclay estimation), for a more quantitative reservoir interpretation. The main challenges of the area, located deep offshore Angola in the Miocene interval, are the structural complexity associated to salt proximity and steep dips at the flanks of the structure. Key steps of this elastic inversion were the multi-well-driven deterministic wavelet extraction and the accurate velocity model derived from FWI that allowed enhancing the a-priori model building. This method enables better delineation of the sand bodies' architecture, resulting in an updated geomodel structural grid and reliable litho-seismic attributes for future development well targeting. We conclude that pseudo-Vclay on BroadSeis® shows more lateral discontinuity (heterogeneity) than on conventional data, as confirmed by recent well analysis. The pseudo-Vclay on BroadSeis® has also confirmed sand quality, as proved by wells results.
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