Over the last two decades there has been an increase in activity on the pore-scale modeling of multiphase flow in porous media. Excellent progress has been made in many areas of pore-scale modeling, particularly in (1) the representation of the rock itself and (2) our description of the pore-scale displacement physics (in model pore geome-tries). Three-dimensional voxelized images of actual rocks can be generated either numerically (e.g. from 2D thin sections) or from micro-CT imaging. A simplified network involving more idealized nodes and bonds can then be extracted from this numerical rock model and this can be used in modeling pore-scale displacement processes. Much progress has also been made in understanding these pore-scale processes (i.e. piston-like displacement, snap-off events, layer formation/collapse, pore-body filling draining). These processes can be mathematically modeled accurately for pores of non uniform wettability, if the geometry of the pore is sufficiently simple. In fact, in recent years these various pore-level processes in mixed and fractionally wet systems have been classified as "events" in an entire capillary-dominated "phase space" which can be defined in a thermodynamically consistent manner. Advances in our understanding and ability to compute several two- (and three-) phase properties a priori have been impressive and the entire flooding cycle of primary drainage (PD), aging/wetting change, and imbibition can be simulated.
In this paper, we review the successes of pore-scale network modeling and explain how it can be of great use in understanding and explaining many phenomena in flow through porous media. However, we also critically examine the issue of how predictive network modeling is in practice. Indeed, one of our conclusions on pore-scale modeling in mixed-wet systems is that we cannot predict two-phase functions reliably in "blind" tests. Interestingly, we make this statement not because we do not understand the pore-scale physics of the process, but because we do understand the physics. It is hoped that our comments will stimulate a more critical debate on the role of pore-scale modeling and its use in core analysis.
Asnaashari, Amir (Université Joseph Fourier Grenoble) | Brossier, Romain (Université Joseph Fourier Grenoble) | Garambois, Stéphane (Université Joseph Fourier Grenoble) | Virieux, Jean (Université Joseph Fourier Grenoble) | Audebert, François (TOTAL E&P) | Thore, Pierre (TOTAL E&P)
Summary We present a fast and effective method to detect and eliminate seismic interference from 3D marine data measured by four-component (4C) streamers. The interference elimination method we propose acts on each shot record independently from the others, relying on the pressure wavefield being reconstructed (and simultaneously deghosted) on a 2D grid, densely sampled in both the inline and the crossline directions. Such reconstruction is enabled by matching-pursuit-based signal processing techniques proposed recently in the literature that have the capability to explicit the information in the multicomponent measurements. Without these measurements, the reconstruction capability is seriously compromised by the strong crossline aliasing.
Mosher, Charles C. (ConocoPhillips) | Keskula, Erik (ConocoPhillips) | Kaplan, Sam T. (ConocoPhillips) | Keys, Robert G. (ConocoPhillips) | Li, Chengbo (ConocoPhillips) | Ata, Elias Z. (ConocoPhillips) | Morley, Larry C. (ConocoPhillips) | Brewer, Joel D. (ConocoPhillips) | Janiszewski, Frank D. (ConocoPhillips) | Eick, Peter M. (ConocoPhillips) | Olson, Robert A. (ConocoPhillips) | Sood, Sanjay (ConocoPhillips)
The particle velocity vector enables us to calculate the 3D upgoing wavefield at any desired position within the aperture of the seismic spread and this allows improving not only the temporal bandwidth by removing ghost notches, but also the spatial bandwidth. However, particle motion sensors measure streamer-borne noise with amplitudes typically several orders of magnitude stronger than the corresponding noise recorded by hydrophones at frequencies below about 20-Hz. Therefore, stronger noise attenuation for particle velocity data is needed at these frequencies. In this paper, we introduce a multiscale noise attenuation algorithm that provides a high-fidelity particle motion measurement at frequencies down to 3 Hz. We also show that the new technique provides improved noise attenuation on pressure data. In this paper, we present the characteristics of the noise recorded by the particle motion sensors in the multicomponent (4C) towed streamer and introduce the multiscale noise attenuation (MSNA) algorithm that is used to attenuate noise on both pressure and particle motion data. We show that the MSNA algorithm provides increased noise attenuation on hydrophone data compared to conventional methods and a high-fidelity particle motion measurement at frequencies down to 3-Hz. Introduction Robertsson et al. (2008) introduced the concept of a multicomponent (4C) towed streamer that acquires both pressure and the full particle velocity vector with inline, crossline, and vertical components.