Roy Burnstad

Data-driven internal multiple attenuation - Applications and issues on land data

Summary Surface-related and internal multiple elimination schemes, firmly rooted to the acoustic wave equation, have been successfully applied to marine datasets. In land however, the applicability of this type of technology is rather limited. Using the CFP-based, layer-related internal multiple removal algorithm, we propose two data-driven, practical strategies aiming for the estimation and subsequent attenuation of internal multiples on land data. Specific issues and assumptions related to this type of technology with emphasis on land data applications are also considered. The effectiveness of the proposed methodologies is demonstrated with a number of field datasets from the Arabian Peninsula.

New Concepts in Quality Control for 3D Near Surface Modeling

The ever increasing number of traces in land 3D surveys has prompted important developments in the field of near surface velocity modeling. One development that stands out is replacement of semi-interactively picked refraction arrivals with batch picked refraction arrivals (Tim Keho, personal communication). The objective of this study is to design and implement a robust methodology to quality control batch refraction arrival picks prior to their use in near surface inversion algorithms. Examples of new quality control tools presented here include: (1) decomposed relative arrival time versus offset and azimuth with optional overlay on shot records, (2) slowness maps which can be converted to velocity grids for potential input to near surface modeling packages and for comparison with hard data such as uphole velocity surveys, and (3) maps indicating the location of anomalous jumps in first arrival time picks. These anomalies, referred to as shingling, are usually associated with velocity inversions. Knowing where near surface velocity inversions occur is critical to identifying challenging data zones that may require additional near surface analysis or noise suppression processing. A key requirement of our approach is the ability to execute in a batch mode on large data volumes within a reasonable amount of wall clock time.

Image integration with learned dictionaries and application to seismic monitoring

Sparse coding can be applied to train an overcomplete dictionary on time-lapse seismic data or images. The learned dictionary generally consists of sparse representations of one or more images. We then use such sparse representations, along with L1-regularization techniques, to predict missing values in seismic images by solving an inverse problem. The practical outcome of the proposed methodology can be a significant reduction in field operational costs by requiring only sparse instead of dense surveys, and by integrating in the seismic images the information captured by the learned dictionary from previous time-lapse and baseline images. A synthetic example is presented to test the method.

Target-oriented stratigraphic processing for AVZ analysis

The advent of wide-azimuth, long-offset land seismic data makes possible more routine application of P-wave amplitude variation with azimuth (AVZ) analysis for fracture fairway and stress-field identification and characterization. Such analysis, however, requires revisiting the processing approach which prepares the data for inversion. We expand upon an industry standard amplitude variation with offset (AVO) processing flow, which is designed to achieve this end, by incorporating azimuth into surface-consistent processes without generation of azimuthal stack volumes.