Sixth EAGE High Performance Computing Workshop

The finite difference solution of the second-order acoustic wave equation is a fundamental algorithm in seismic exploration. In contrast to the explicit finite difference (EFD) schemes that usually suffer from the so-called “saturation effect”, the implicit FD schemes can obtain a spectral-like resolution with a relatively short operator length. Unfortunately, these implicit schemes are not widely implemented because band matrices need to be solved implicitly, which is not suitable for high-performance computing (HPC). We introduce an explicit solution to overcome this limitation by applying causal and anti-causal integrations, and the new solution can be proved to be equivalent to the traditional implicit LU decomposition solution. Besides, we also compare the accuracy of the new schemes with traditional EFD schemes up to the 32nd order, and the results indicate that the optimized implicit FD scheme is more accurate. The computational cost for the newly proposed scheme is standard 8th order EFD plus two causal and anti-causal integrations, which can be realized recursively.

Full Waveform Inversion (FWI) can establish an accurate shallow velocity model as a wave-based solution. The accurate velocity model is essential for generating high-fidelity seismic images to find reservoirs. Moreover, its successful implementation on a 3D onshore dataset depends on the quality of low-frequency components of seismic data. A two-stage waveform inversion workflow has been carried out to obtain a high-resolution shallow velocity model on the 3D land dataset to overcome this problem. As a first stage of the workflow, we update the velocity model to minimize the time difference of the first arrivals between field and synthetic data. In the second stage, we perform traveltime-oriented FWI to update the model by minimizing phase differences of seismic events between field and modeled data. Most of the functions used in waveform inversion are written by the compute unified device architecture (CUDA) program language to optimize performances further and reduce computing time. In addition, we switch on the static task distribution to our waveform inversion implementations instead of the dynamic task distribution, which is proven a powerful scheduler in the reverse time migration program.

Modelling of seismic wave propagation is widely used in the study and imaging of the Earth’s interior. Especially for the techniques of reverse time migration and full waveform inversion, an accurate and efficient seismic wave modelling solver plays a key role in handling complex and large-scale problems. In addition to developing novel modelling algorithms from a mathematical standpoint, it is necessary to study how to implement existing methods on modern heterogeneous high-performance computing (HPC) platforms, including at least one type of accelerator. Utilizing GPUs as accelerators has been shown to be attractive in the geophysical applications. To benefit from this multi-threaded architecture, we explore its implementation in the spectral element modelling (SEM) engine of our full waveform modelling and inversion code SEM46. Based on the features of SEM algorithm and the Cartesian-based structured mesh in SEM46, we investigate GPU kernels with three different parallel prototypes with particular focus on the modelling accuracy and computational efficiency. The memory constraint in the 3D implementation is addressed by domain decomposition using multiple GPUs with CUDA-AWARE MPI to achieve direct GPU-to-GPU communications. The resulting GPU solver exhibits a high speedup and excellent scaling over multi-GPUs, making it promising for large-scale realistic 3D problems.

As the energy industry transition to hydrocarbon alternatives for automotive and electricity production, fossil energy continues to be needed in those segments as well as solvent, plastic, solvent and consumer goods. The discovery of oil and gas-bearing formation is an increasing challenge as easily accessible resources has depleted. It requires to go deeper in the earth crust and image more complex geological topologies of the subsurface. Seismic imaging is key to understand the subsurface velocities and is one of the most demanding workloads for high performance computing. The need for high resolution image led to higher frequency processing and more complex wave equation. Compute and storage requirements have grown accordingly to accommodate those needs. Cloud computing is an attractive technology that provides the benefit of quickly access additional compute and storage capability for new algorithms development or production projects. In this paper, we present architecture best practices and performance recommendation for finite difference kernel method such as RTM and FWI. Devito is used to illustrate performance and runtime guidance on the latest AMD Milan and Intel Icelake instances. We will show performance using different compilers and flags as well as MPI, OpenMPI and hybrid on single and multi-instances.

Oil and Gas exploration or drill sequence planning is a difficult optimization problem which infeasible to be solved by classical computers today. They are called combinatorial optimization problems (or NPHard). Solving such challenge can yield tremendous benefits in terms of improved success ratio and rescued cost and resources required. These problems are expected to be solved by Quantum Computers (QC) called Quantum Annealing (QA). The current evolution of the quantum hardware or limited availability of actual qubits. This limitation makes the pure QA solution infeasible for solving large-scale real-world problems or large number of qubits. For overcoming limitation and simulating annealing, Vector Engine Accelerator (VE) has the capability of simulating 100K fully connected qubits per PCIe based card. QA works using quantum fluctuations. Quantum Simulated Annealing (SA) or Vector Annealing (VA) works simulating the quantum effect using meta-heuristic approach on a classical computer. VA on VE solution has advantage in comparison to other SA on classical computers is large number of qubits support and capability of accelerating the same. In the presentation we will be showcasing not only the capability to solve combinatorial optimization problem but also user can accelerate the performance and making a real-world applicable solution.

For developers, one of the challenges to maintain their code base is to address the issue of interoperability or portability of the application across different platform / GPU architectures. In this report, we shall explore tools and methodologies to enable porting of existing software to different GPU architectures with minimal changes, albeit keeping the performance number nearly the same, if not better.

Real-time mapping of formations, detection of structural anomalies and evaluation of near-well reservoir properties can be achieved through logging while drilling (LWD). Hydrocarbon deposits have typically a quasilayered structure, however, complex situations with strong 3-D features like faults or salt domes are becoming increasingly common. Here, we extend the Green’s function methodology to compute fully 2–3D scalar wave fields. This finds application in elastodynamic forward and inverse scattering, where often times, as a first approximation, elastic coupling between P- and S-waves is ignored. However, we anticipate that our computational approach will equally benefit borehole acoustics (e.g., LWD borehole acousto-elastodynamic scattering), reservoir acoustics (e.g., distributed acoustic sensing) and reservoir seismics (e.g., surface seismic survey and vertical seismic profiling) applications. In this paper, we explore fully multi-dimensional geological models and accelerate the physics simulations with edge parallel computing. Along the road, high-performance LWD navigation can achieve safe, fast and confident well placement. We developed highly efficient and scalable computational means for modeling acoustic responses in multi-dimensional reservoir. The results demonstrate nearly 2 orders of magnitude acceleration for small-to-medium problems through edge computing. Multi-physics LWD, inverse reservoir mapping and monitoring as well as variety of AI/ML tasks can benefit from our platform-agnostic approach.

I/O from seismic algorithms are challenging to manage. ML workloads have demanding I/O that are different than seismic algorithms. Compute to I/O resources imbalance is real. We investigated I/O profiles from our heavy I/O demanding applications. Based on our analysis, we have implemented a way to alleviate file system bottlenecks.

With real world HPC applications, it is unlikely that all the resources of a cluster node can be fully used simultaneously by a single application. However, if a single application completely exhausts one of the node’s resources, it might prevent the possibility for other applications to exploit the remaining resources, which are likely to remain partially unused.

Moreover, since most of the seismic imaging algorithms are embarrassingly parallel, in the context of a seismic imaging multi-user and multi-project scenario the challenge is often to efficiently execute a very large number of asynchronous and loosely coupled tasks. This combines with the nature of modern full wave imaging and velocity analysis, which results in variability of memory requirement and computing unit occupancy, that can both span several orders of magnitude.

In this presentation we will discuss what are the recommendable actions that can be taken to maximize cluster resource utilization whenever the majority of the workload has these characteristics.