Oleg Mikhailov

Electroseismic investigation of the shallow subsurface: Field measurements and numerical modeling

Recent studies have demonstrated that electroseismic phenomena in porous media have the potential to detect zones of high fluid mobility and fluid chemistry contrasts in the subsurface. However, there have only been a few field studies of these phenomena since they were first observed 60 years ago. None of these studies were able to support observations with an explicit comparison to results of full waveform modeling. In this paper, we demonstrate that the electroseismic phenomena in porous media can be observed in the field, explained, and modeled numerically, yielding a good agreement between the field and the synthetic data. We first outline the design of our field experiment and describe the procedure used to reduce noise in the electroseismic data. After that, we present and interpret the field data, demonstrating how and where different electroseismic signals originated in the subsurface. Finally, we model our field experiment numerically and demonstrate that the numerical results correctly simulate arrival times, polarity, and amplitude variation with offset behavior of the electroseismic signals measured in the field.

Using borehole electroseismic measurements to detect and characterize fractured (permeable) zones

In 1996, we measured Stoneley‐wave‐induced electrical fields in an uncased water well drilled in fractured granite and diorite near Hamilton, Massachusetts. Stoneley waves generated by sledgehammer blows to the surface casing produced a flow of pore fluid in permeable zones intersected by the borehole. In turn, this flow induced a streaming electrical field. Even though these electrical signals were very small (tens of microvolts), we were able to detect them using electrodes placed in the borehole, after power line and telluric signals were canceled by remote referencing and notch‐filtering. Amplitude analysis of the electrical fields confirmed that they were induced by fluid flow in the fractured formation. The normalized amplitudes of these electrical fields correlate with the fracture density log and agree with the theoretical model for this electroseismic phenomenon. Our Biot‐theory‐based model predicts that borehole electroseismic measurements can be used to characterize permeable zones. According t...

Practical approach to joint imaging of multicomponent data

Recent advances in the ocean-bottom cable (OBC) acquisition technology allow recording of high quality multi-component data in the marine environment. These data have been used to image reservoirs obscured by gas clouds (e.g., Valhall) and reservoirs with low P -wave impedance contrast that are hard to see in conventional streamer data (e.g., Alba). Further benefits of using multicomponent data in exploration may come from analyzing P -wave ( PP ) and converted-wave ( PS ) data jointly to obtain more information about a reservoir than is available from PP data alone. Joint interpretation of multicomponent data is complicated by the fact that PP data are traditionally imaged in PP time and PS data are imaged in PS time. Thus, the PP and PS images have different vertical scales. To reconcile these scales, an interpreter has to identify events in both images that correspond to the same reflector and then stretch one of the images to match the other. The event identification is not always straightforward because some interfaces generate PP and PS reflections of the same polarity and other interfaces of the opposite polarity. Thus, there may not be a natural choice of troughs or peaks to correlate. To eliminate the need to stretch PP and PS images and to facilitate joint interpretation, we developed a methodology for joint imaging of multicomponent data in depth. We image PP and PS data in depth by anisotropic pre-stack depth migration. For PS data, our migration algorithm combines P -wave propagation from a source and S -wave propagation from a receiver to a reflection point. The algorithm also handles the OBC acquisition geometry. Prestack depth migration of multicomponent data requires a complete transversely isotropic (TI) velocity model for the subsurface. This model consists of four parameters: P -wave velocity, S -wave velocity, and the Thomsen anisotropy parameters, …

The influence of stacking velocity uncertainties on structural uncertainties

Summary Errors in the velocity eld used to migrate seismic data are a leading cause of errors in the positionining of structural events in the processing of seismic data: uncertainty in the velocity eld leads to structural uncertainty. In this paper, we investigate the broader question of how errors in stacking velocity, time to an event in a stacked section, and the slope of an event in a time section lead to errors in the positioning of structural events for an isotropic medium. We perform a sensitivity analysis, obtaining simple formulas for the errors in structure that are rstorder in the errors in stacking velocity, zero-oset time, and slope. These formulas are geometrically explicit: if we make a small change in stacking velocity (or time or slope), we then know the direction and magnitude of the resulting change to each point on the selected event. Being the result of sensitivity analysis, these formulas are linear. Thus if we had a probability distribution for the errors in velocity (i.e., we knew the uncertainty in velocity), we could use these formulas to obtain a probability distribution for the errors in position for points on the selected event (i.e., the uncertainty in structure). Our analysis focuses on the neighborhood of a single point on an event and assumes a homogeneous velocity eld. Although the analysis is based on a very simple model, numerical experiments show that the relationships are valid approximately for moderate heterogeneities in the velocity eld. In a companion paper (Bube et al., 2004), we use these results to investigate errors in structural location due to uncertainty in weak anisotropy.

Structural uncertainty and Bayesian inversion

Summary Previous workers have concluded that the primary source of uncertainty in the shape of structures observed in processed seismic data is due to uncertainty in the underlying velocity field (or, equivalently, slowness). Sources of information on the slowness field include sonic logs and traveltime vs. depth picks from checkshots. We must combine the information from these different data sets and extrapolate it to all locations in a 3-D slowness cube in order to model the uncertainty in the 3-D seismic image. We present Bayesian inversion as a framework for incorporating multiple data sets and their associated uncertainty into a 3-D stochastic model of slowness. We restrict ourselves to situations where the geology is laterally smooth and most uncertainty lies in the depth conversion. This allows us to avoid expensive remigration for multiple slowness models and concentrate on uncertainty in depth conversion and how it translates into uncertainty on a picked structure. We present a Monte Carlo method for converting the uncertainty in the slowness field to spatial uncertainty on picked horizons. We further present another method for choosing a location for optimal future well placement.

Alba 4C - Improved Imaging With Converted-wave Anisotropic Prestack Time Migration

The converted-wave data over the Alba field, acquired and first processed in 1998, has become one of the better known 3D converted-wave surveys in the North Sea. Since that time, converted-wave processing tools have improved significantly and this paper will discuss some of the new 3D processing methods that have recently been applied to this survey. We present the results and the methods that contributed to the much improved image.