Robert G. Clapp

Incorporating geologic information into reflection tomography

In areas of complex geology, prestack depth migration is often necessary if we are to produce an accurate image of the subsurface. Prestack depth migration requires an accurate interval velocity model. With few exceptions, the subsurface velocities are not known beforehand and should be estimated. When the velocity structure is complex, with significant lateral variations, reflection-tomography methods are often an effective tool for improving the velocity estimate. Unfortunately, reflection tomography often converges slowly, to a model that is geologically unreasonable, or it does not converge at all. The large null space of reflection-tomography problems often forces us to add a sparse parameterization of the model and/or regularization criteria to the estimation. Standard tomography schemes tend to create isotropic features in velocity models that are inconsistent with geology. These isotropic features result, in large part, from using symmetric regularization operators or from choosing a poor model parameterization. If we replace the symmetric operators with nonstationary operators that tend to spread information along structural dips, the tomography will produce velocity models that are geologically more reasonable. In addition, by forming the operators in helical 1D space and performing polynomial division, we apply the inverse of these space-varying anisotropic operators. The inverse operators can be used as a preconditioner to a standard tomography problem, thereby significantly improving the speed of convergence compared with the typical regularized inversion problem. Results from 2D synthetic and 2D field data are shown. In each case, the velocity obtained improves the focusing of the migrated image.

Beyond Traditional Microprocessors for Geoscience High-Performance Computing Applications

The oil and gas industry is a major user of high-performance computing, and geoscience computational cycles are dominated by kernels that are relatively few and well defined. This project explores accelerating geoscience applications using FPGA-based hardware, optimizing the algorithm and the hardware to achieve maximum performance. This approach can deliver speedup of 20 to 70 times compared with a conventional HPC node.

Application of image segmentation to tracking 3D salt boundaries

Delineating salt boundaries is a necessary step in the velocity-model building process. The salt-delineation problem can be thought of as an image-segmentation problem. Normalized cuts image segmentation (NCIS) finds the cut (or cuts) that result in an image being broken into portions which have dissimilar, by some measure, characteristics. We apply a modified version of the NCIS method to partition seismic images along salt boundaries. NCIS can track boundaries that are not continuous, where conventional horizon-tracking algorithms may fail, by calculating a weight connecting each pixel in the image to every other pixel within a local neighborhood. The weights are determined using problem-dependent combinations of attributes, the most important being instantanteous amplitude and dip. The weights for the entire image are used to segment the image via an eigenvector calculation. The weight matrices for 3D seismic data cubes can be quite large and computationally expensive. By imposing bounds and by distributing the algorithm on a parallel cluster, we significantly increase efficiency. This method is demonstrated to be effective on a 3D field seismic data cube.

Time‐lapse seismic noise correlation tomography at Valhall

We show that a reliable and statistically significant group velocity time-lapse difference between 2004 and 2010 can be retrieved from ambient seismic noise in an offshore hydrocarbon exploitation setting. We performed a direct comparison of Scholte wave group velocity images obtained using regularized tomography. We characterize the expected variation in group velocity images from the 2004 or 2010 recordings that result from fluctuations in the cross correlations by looking at cross correlations of portions of the recordings. We prove that the time-lapse difference is statistically significant. The time-lapse group velocity image from ambient noise data shows strong similarities with a time-lapse phase velocity map obtained from controlled source data. The most striking features are a northern and a southern group velocity increase due to compaction and subsidence as a result of reservoir production.

Eliminating the memory bottleneck: an FPGA-based solution for 3d reverse time migration

Memory-related constraints (memory bandwidth, cache size) are nowadays the performance bottleneck of most computational applications. Especially in the scenario of multiple cores, the performance does not scale with the number of cores in many cases. In our work, we present our FPGA-based solution for the 3D Reverse Time Migration (RTM) algorithm. As the most computationally demanding imaging algorithm in current oil and gas exploration, RTM involves various computational challenges, such as a high demand for storage size and bandwidth, and a poor cache behavior. Combining optimizations from both the algorithmic and architectural perspectives, our FPGA-based solution manages to remove the memory constraints and provide a high performance that can scale well with the amount of computational resources available. Compared with an optimized CPU implementation using two quad-core Intel Nehalem CPUs, our solution achieves 4x speedup on two Virtex-5 FPGAs, and 8x speedup on two Virtex-6 FPGAs. Our projection demonstrates that the performance will continue to scale with the future increase of FPGA capacities.

Scaling Reverse Time Migration Performance through Reconfigurable Dataflow Engines

Seismic migrations dominate about 90 percent of the computation cycles in the oil and gas industry. With the demand to handle high-density data and more complicated physics models, migration applications always call for more computing power, and they adopt new architectures quickly. Current multicore and many-core architectures have significantly improved the density of computational resources within a chip, but they also have made memory bandwidth a bottleneck that stops the scaling of performance over the increased number of cores. In this article, the authors present their reverse time migration design based on reconfigurable data-flow engines. Combining both algorithmic and architectural optimizations, they manage to achieve a balanced utilization of various resources (computational logic, local buffers, memory bandwidth, and so on) in the system, with none of them becoming the performance bottleneck. Their data-flow design provides performance equivalent to 72 Intel CPU cores, and achieves 10 times higher power efficiency than the multicore CPU architecture.

Imaging by target-oriented wave-equation inversion

Wave-equation inversion is a powerful technique able to build higher-resolution images with balanced amplitudes in complex subsurface areas relative to migration alone. Wave-equation inversion can be performed in image space without making velocity-model or acquisition-geometry approximations. Our method explicitly computes the least-squares Hessian matrix, defined from the modeling/migration operators, and uses a linear solver to find the solution of the resulting system of equations. One important advantage of the explicit computation of the Hessian, compared to iterative modeling/migration operations schemes, is that most of the work (precomputing the Hessian) is done up front; afterward, different inversion parameters or schemes can be tried at lower cost. Another advantage is that the method canhandle 3D data in a target-oriented fashion. The inversion in the presence of a complex overburden leads to an ill-conditioned system of equations that must be regularized to obtain a stable numerical solution...

Salt delineation via interpreter-guided 3D seismic image segmentation

AbstractAlthough it is a crucial component of seismic velocity model building, salt delineation is often a major bottleneck in the interpretation workflow. Automatic methods like image segmentation can help to alleviate this bottleneck, but issues with accuracy and efficiency can hinder their effectiveness. However, a new graph-based segmentation algorithm can, after modifications to account for the unique nature of seismic data, quickly and accurately delineate salt bodies on 3D seismic images. In areas where salt boundaries are poorly imaged, limited manual interpretations can be used to guide the automatic segmentation, allowing for interpreter insight to be combined with modern computational capabilities. A successful 3D field data example demonstrates that this method could become an important tool for interactive interpretation tasks.

Revisiting finite difference and spectral migration methods on diverse parallel architectures

With a continual request for faster speed and better resolution, seismic migration continues to be one of the most computationally demanding geoscience applications. The recent emergence of new high performance computing (HPC) architectures, such as multi-core CPUs, Graphic Processing Units (GPUs), and Field Programmable Gate Arrays (FPGAs), offers significant speed up if the method can be effectively mapped. For high-end finite difference and spectral migration methods, the differencing stencils and FFTs are usually the dominant cost. In our work, we parallelize and optimize the stencil and FFT kernels on multi-core CPUs, GPUs, and FPGAs. We make an extensive comparison between the finite difference method and the spectral method on both numerical accuracy and parallel computation performance. Our experiments demonstrate that, although spectral methods eliminate the spatial dispersion errors and can lead to a reduced computational complexity, finite difference migrations are able to achieve the same accuracy with both a better performance and a better scalability in many cases because of the more regular computation and memory access patterns. The only exception is spectral methods based on 2D FFTs, which continue to scale with the parallel computation capacity of modern architectures. The technological trends indicate that these findings will continue.

3D plane-wave migration in tilted coordinates: A field data example

We have extended isotropic plane-wave migration in tilted coordinates to 3D anisotropic media and applied it on a Gulf of Mexico data set. Recorded surface data are transformed to plane-wave data by slant-stack processing in inline and crossline directions. The source plane wave and its corresponding slant-stacked data are extrapolated into the subsurface within a tilted coordinate system whose direction depends on the propagation direction of the plane wave. Images are generated by crosscorrelating these two wavefields. The shot sampling is sparse in the crossline direction, and the source generated by slant stacking is not really a plane-wave source but a phase-encoded source. We have discovered that phase-encoded source migration in tilted coordinates can image steep reflectors, using 2D synthetic data set examples. The field data example shows that 3D plane-wave migration in tilted coordinates can image steeply dipping salt flanks and faults, even though the one-way wave-equation operator is used for wavefield extrapolation.