Scott Boyer

Wide-azimuth land processing: Fracture detection using offset vector tile technology

Using a modern wide-azimuth land survey, we demonstrate the power of offset vector tile (OVT) processing and subsequent analysis of offset vector gathers (OVG) to identify potential anisotropy and fracture characteristics of certain reservoirs of interest. Migration of the inherently azimuth-limited OVT gathers and the accompanying velocity updating scheme, based on surface fitting in offset and azimuth, yields robust measurements critical to this analysis. Both the kinematic and dynamic aspects of the processing are considered and contrasted. The results of the processing and analysis are then confirmed by comparison to the values predicted from two wells in the area.

Application of POCS Interpolation to Exploration

Powerful new algorithms have emerged in the last few years that are taking interpolation of sparse data sets to a new level of sophistication. Projection onto a Convex Set, or POCS, is one of these algorithms. In this paper we will briefly review the technique and demonstrate its usefulness and versatility in many areas of geophysical data processing. From extrapolation to zero offset for 3D SRME to regularization in the offset vector tile (OVT) domain to enhance imaging, we show that this powerful technique has many potential applications.

Enhanced Shallow Water Demultiple with Water Bottom Reflection Modeling

Shallow Water Demultiple (SWD) is a very challenging problem for marine seismic data processing. In shallow water environments, water bottom reflections are recorded only on a few near offset traces because critical reflection angle is reached quickly. In very shallow water, water bottom reflections may disappear completely. This poses a limitation to any convolution based demultiple methods such as Surface Related Multiple Elimination (SRME) and SWD to predict first order multiple. In this paper we propose a way to enhance these aforementioned methods by modeling the water bottom reflection and then adding it to the recorded seismic data. The modified data can then be used to predict first order multiple using SRME and/or SWD. We call these methods enhanced SRME and enhanced SWD, respectively. We will also demonstrate that an optimal way to perform the multiple elimination is to cascade the enhanced SWD followed by SRME. We call this methodology Cascaded Enhanced Shallow Water Demultiple (CESWD). Our test results show that enhanced SWD is better than enhanced SRME, and CESWD is better than enhanced SWD. Finally a comparison of these methods is presented by applying them to a real data example. The enhanced methods produce better than their conventional counterparts.

Shallow Water Demultiple Using a Multichannel Prediction

fective ways are needed to eliminate these types of multiples in shallow water environments. Many model-driven methods for Shallow Water Demultiple (SWD) have been developed in the past with partial success. The success of multiple predictions lays heavily on the ability to make an accurate water bottom model. The current trend in the industry for SWD is to derive a 2D predictive operator with a predictive lag calculated from bathymetry, thus making it a totally data driven method. This approach has showed some improvements but much is left to be done. We derive and present here a data-driven method which is inherently more challenging but as we shall see it produces better results. Tests results show that our SWD methodology works better than SRME. Furthermore we will also show that a cascaded SWDSRME approach can give an even better result than either of these applied alone. We have successfully applied this technique in many production environments around the world and we will show some examples in this paper.