Shuki Ronen

Seismic-guided estimation of log properties; Part 1, A data-driven interpretation methodology

Seismic data are routinely and effectively used to estimate the structure of reservoir bodies but often play no role in the essential task of estimating the spatial distribution of reservoir or rock properties. Yet, for a long time, we have been using attributes or other features of seismic data to gain useful clues in the interpretation process. Since the 1960s, we have known that reflection amplitude is sensitive to the thickness of thin beds. In the 1970s, bright spots were discovered to be useful in forecasting the presence of gas sands. Then, in the 1980s, amplitude variation with offset (AVO) analysis was identified as an even more refined indicator for gas sands or other situations, giving rise to Poisson’s ratio contrasts. Other examples exist, such as predicting porosity from calibrated acoustic impedance values computed from seismic data.

Least‐squares reverse time migration/inversion for ocean bottom data: A case study

We present a method based on least-squares reverse time migration/inversion (LSRTM) for imaging ocean bottom data. We show that by using LSRTM, we not only enhance the resolution of the image, suppress the migration artifacts, and more importantly, bring up the relative amplitude of true reflectors in the subsurface. This method can also be extended to image with higher-order multiples. In certain geometry LSRTM with multiples can further improve the image with a bigger aperture. We demonstrate the concept and methodology in 2D and apply our proposed scheme to an ocean bottom field survey located in the northern Cascadia continental margin.

Mirror imaging of OBS data

The world’s demand for energy is accelerating, while its hydrocarbon reserves are diminishing. Producers are compelled to explore and produce oil and gas in more challenging environments and to maximize recovery in existing reservoirs. New technology has always been a key to success. One such new technology is seismic acquisition using ocean bottom station (OBS) nodes (Berg et al., 1994; Ronen et al., 2003; Amal et al., 2005; Docherty et al., 2005; Granger et al., 2005).

Least-squares DMO and migration

Conventional processing, such as Kirchhoff dip moveout (DMO) and prestack full migration, are based on independent imaging of subsets of the data before stacking or amplitude variation with offset (AVO) analysis. Least‐squares DMO (LSDMO) and least‐squares migration (LSMig) are a family of developing processing methods which are based on inversion of reverse DMO and demigration operators. LSDMO and LSMig find the earth model that best fits the data and a priori assumptions which can be imposed as constraints. Such inversions are more computer intensive, but have significant advantages compared to conventional processing when applied to irregularly sampled data. Various conventional processes are approximations of the inversions in LSDMO and LSMig. Often, processing is equivalent to using the transpose of a matrix which LSDMO/LSMig inverts. Such transpose processing is accurate when the data sampling is adequate. In practice, costly survey design, real‐time coverage quality control, in‐fill acquisition, re...

Seismic‐guided estimation of log properties (Part 2: Using artificial neural networks for nonlinear attribute calibration)

We saw in Part 1 that when we have 3-D data together with a number of logged wells, we can look at possible relationships between some attributes of the seismic data and various properties measured on the logs. At multiple well locations, where we have both seismic and log data, we can look for trends in these two data types on crossplots. If we see a trend, we can quantify it with a derived or specified functional relationship. This functional relationship can be used to convert the attribute values to log properties, and when followed by a residual correction, provide a means to estimate the distribution of these properties away from the wells.

Wide-area imaging from OBS multiples

The subseafloor structure offshore western Canada was imaged using first-order water-layer multiples from oceanbottom seismometer OBS data and the results were compared to conventional imaging using primary reflections. This multiple-migration mirror-imaging method uses the downgoingpressurewavefieldjustabovetheseafloor,which is devoid of any primary reflections but consists of receiverside ghosts of these primary reflections. The mirror-imaging method employs a primaries-only Kirchhoff prestack depth migration algorithm to image the receiver ghosts. The additional travel path of the multiples through the water layer is accountedforbyasimplemanipulationofthevelocitymodel and processing datum: the receivers lie not on the seabed but on a sea surface twice as high as the true water column. Migration results show that the multiple-migrated image provides a much broader illumination of the subsurface than is possible for conventional imaging using the primaries, especially for the very shallow reflections and sparse OBS spacing. The resulting image from mirror imaging has illumination comparable to the vertical incidence surface streamer single-channelreflectiondata.

Imaging downgoing waves from ocean bottom stations

We present a method for imaging the downgoing waves recorded on the seabed. The first step in our method is a conventional combination of the hydrophone and the vertical geophone data to produce of up going and downgoing P waves. Conventionally one proceeds only with the upgoing waves, we proceed also with the downgoing waves, which contain no primary reflections. We migrate the downgoing waves by pretending that the data were acquired not on the seabed, but above the sea surface on top of an virtual water layer whose thickness is twice the sea depth. We applied this method to OBS data recorded in the North Sea and found that the downgoing waves provide a better image than the upgoing waves. We speculate that this is because of improved illumination and reduced exposure to shallow inhomogeneous anomalies directly below the seabed.

Joint Least-squares Inversion of Up- And Down-going Signal For Ocean Bottom Data Sets

We present a joint least-squares inversion method for imaging the acoustic primary (up-going) and mirror (down-going) signals for ocean-bottom seismic processing. Joint inversion combines the benefits of wider illumination from the mirror signal and better signal-to-noise ratio from the primary signal into one image. Results from two modified 2D Marmousi models show a better illumination of the subsurface and improved resolution in geologically complex areas.

Seismic-guided estimation of log properties; Part 3, A controlled study

In the first two parts of this series (published in the previous two issues of TLE), we discussed an alternative way of generating maps of rock or reservoir properties by using seismic attribute guidance. Normal procedure in generating such property maps (e.g., porosity, water or hydrocarbon saturation) takes the well data as control values, and uses 3-D seismic mainly for structural control. Using 3-D seismic attributes to guide the estimate of properties takes more effort, so there must be a reason that we choose to do it that way. In Part 1, we saw that property maps generated using attributes show greater detail. We also saw some theoretical considerations suggesting greater accuracy compared to maps from log data alone, but greater accuracy was not proven or demonstrated. Here we address the question Are the maps really more accurate? by reviewing the results of a controlled study.

Robust estimation of fracture directions from 3-D converted-waves

Summary We present a new method for estimating fracture directions from 3-D converted wave data. The method is designed to alleviate some of the usual requirements placed upon the acquisition geometry – in particular regarding the distribution of azimuths within an analysis gather. We present a synthetic example, comparing the new method with a standard approach based on polarity changes, to illustrate the superior accuracy of the new method. We also present initial results on a 3-C field dataset, which indicate the new method performs well compared to an existing technique.