Fifth EAGE Workshop on High Performance Computing for Upstream

Quantum computing offers a theoretical speed-up (in terms computational complexity) when performing certain computational tasks. Successful inclusion of quantum computing units in existing HPC solutions is contingent on identifying appropriate and realistic use-cases, of which, to date, there are very few candidates. This is particularly true for upstream business. This is because, there are few quantum algorithms, each coming with a number of caveats which ought to be carefully studied. We suggest a potential simple use-case in geophysics and 1D inverse scattering theory and provide detailed analysis of the requirements needed to be met in order to achieve the promised speed-up.

Reverse Time Migration (RTM) is a key application used in seismic imaging and accounts for a significant portion of HPC resource utilization in the Oil & Gas exploration industry. The number of nodes used per shot and the corresponding domain decomposition can have a significant impact on RTM performance and project cycle time. In this work we will describe a method to automatically select the optimal number of nodes and the best domain decomposition for each shot in a RTM project. In addition to the computational savings and reduction in cycle time, the method presented here also improves the user experience for seismic processors as they only need to focus on tuning the parameters that affect the results and do not have to worry about the HPC parameters. This becomes even more important as compute environments become more heterogeneous and projects try to use all available compute resources in order to reduce cycle time. Also in a pay-per-use model, it is helpful to be able to predict the compute costs for a project.

This paper will discuss the added value of a cloud environment in an innovative risk assessment solution involving the batch run of a geomodelling application and a flow simulator. The project was conducted jointly by Emerson and AWS. The first phase of the collaboration focused on assessing cloud parameters on the performance of the software and the cost of completing typical activities. It was then applied to a case study using the Volve oil field on the Norwegian continental shelf* to draw comparisons with current on-premises installations and evaluate the solution from a reservoir management perspective. The study first showed that while the optimal cloud configuration for history matching is dependent on global parameters, it must be fine-tuned for each reservoir model. A method to reduce simulation time and cloud costs was established. The study then showed that use of the cloud has a positive impact on operations. The results are available within hours instead of days; same-day evaluation leads to faster decisions and improved operational efficiency. Moreover, enhanced stochastic analyses are now possible for those without high-performance on-premises clusters.

GEOSX is an open-source, exascale-ready, multiphysics simulator for geological formations. This simulator is currently developed by Lawrence Livermore National Laboratory, Stanford University, and Total. GEOSX is designed to address several types of complex simulation use cases, including geological storage of carbon dioxide (CCUS). Numerical simulations of such operations require coupling between mass/energy transfers and rock geomechanics over large formations and for long simulation periods. GEOSX provides such simulation solutions for mixed-architecture systems. The code is not limited to a specific, unique, target architecture by the use of two libraries called RAJA and CHAI. Here, we present the essential functionalities and underlying building blocks of GEOSX. Examples of use cases are shown. We discuss challenges encountered along the way and especially those related to multiphysics modeling. Last, we provide all references required to access, download the sources, and build GEOSX (under LGPL 2.1 license).

Efficient frequency-domain Full Waveform Inversion (FWI) can be applied on long-offset/wide-azimuth stationary-recording seabed acquisitions carried out with ocean-bottom cables (OBC) and ocean bottom nodes (OBN) since the wide angular illumination provided by these surveys allows for limiting the inversion to a few discrete frequencies. In the frequency domain, the forward problem is a boundary value problem requiring the solution of large and sparse linear systems with multiple right-hand sides. In this study, we revisit the potential of the massively-parallel sparse multifrontal solver MUMPS to perform efficiently the multi-source forward problem of 3D visco-acoustic FWI. The execution time and memory consumption of the solver are further improved by exploiting the low rank properties of the sub-blocks of the dense frontal matrices, the sparsity of the right-hand sides (seismic sources) and the work in progress on the use of mixed precision arithmetic. We revisit a 3D OBC case study from the North Sea in the 3.5~Hz-13~Hz frequency band using between 10 and 70 nodes of the Jean-Zay supercomputer of IDRIS and show that, even without exploiting low rank properties, problems involving 50 millions of unknowns and probably more can be tackled today with this technology.

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We show in this work that using the NSIMD vectorization library allowed to obtain better performances on the EFISPEC3D kernel, a spectral-finite-element method to solve the forward seismic wave propagation problem. Moreover the same code without modification can be compiled to target different SIMD extensions with different vector sizes without degrading performances.

DAOS-SEIS mapping layer is introduced to the seismic community, utilizing the evolving DAOS technology, to solve some of the seismic IO bottlenecks caused by the SEGY data format through leveraging the graph theory in addition to the DAOS object-based storage to design and implement a new seismic data format natively on top of the DAOS storage model in order to accelerate data access, provide in-storage compute capabilities to process data in place and to get rid of the serial seg-y file constraints. The DAOS-SEIS API is built on top of the DAOS file system(dfs) and seismic data is accessed and manipulated using the DAOS-SEIS API after accessing the root seismic dfs object. The mapping layer is perfectly utilizing the graph theory and the object storage to split the acquisition geometry represented by the traces headers away from the time-series data samples

In this paper we demonstrated that the overall simulation throughput of full-GPU reservoir simulators can be further improved significantly without any modifications to the software, using NVIDIA’s Multi-Processing-Service and Multi-Instance-GPU infrastructure. For models with just a few thousand cells, a throughput increase of 7x is achieved while for problems with a million cells a 60% improvement is achieved using MPS. Furthermore, when using either MPS or MIG, the smaller models can achieve 80% of the peak achievable performance of larger models. In the context of uncertainty quantification workflows, these performance improvements are significant.