利用基于云的全波形反演系统加速地下数据处理和解释

Sirivan Chaleunxay, N. Shah
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引用次数: 0

摘要

了解地球地下的情况对勘探和生产(E&P)行业的需求至关重要,以最大限度地降低风险和最大化采收率。直到最近,该行业的服务部门还没有在基于原始勘探地震数据的数据驱动的自动化地球模型构建方面取得多少进展。但幸运的是,这种情况现在已经改变了。业界领先的技术是全波形反演(FWI),在建立地球内部视图时,可以获得前所未有的分辨率和精度提高。先进的FWI配方能够仅使用未经处理的原始数据自动建立地下模型。基于云的FWI通过将最复杂的波形反演技术与地球上最大的计算设施相结合,帮助加速了这一进程。这结合了可验证的准确性,更多的自动化和更高的效率。在本文中,我们描述了使基于云的FWI能够本地利用公共云平台在灵活性和按需可扩展性方面的主要优势的转变。我们从一个遗留的基于mpi的应用程序开始,该应用程序被设计为由传统的本地作业调度器运行。我们的具体目标是:(1)在生产FWI运行的整个生命周期中利用一组异构的计算硬件,而不必在整个持续时间内提供它们;(2)在没有正常运行时间保证的情况下利用经济高效的空闲容量计算实例;(3)维护一个既可以在本地HPC系统上运行又可以在云上运行的代码库。为了实现这些目标,意味着将作业调度和通信代码的“令人尴尬的并行”方面从使用MPI转移到各种基于云的编排系统,以及寻找基于云的解决方案,这些解决方案可以很好地用于广播/缩减操作。将这些系统放置在MPI库调用的定制的基于tcp的存根之后,允许我们在本地HPC环境中按原样运行代码,而在云上,我们可以根据需要异步地提供和暂停工作实例(可能具有非常不同的硬件配置),而无需维护静态MPI世界通信器的负担。利用这种动态的云原生架构,我们1)利用先进的FWI配方,仅使用未经处理的原始数据就能自动建立地下模型,2)从完整记录的波场(折射、反射和倍数)中提取速度模型,3)引入反射移动的明确灵敏度,传统FWI不可见,用于潜水波区以下的宏观模型更新。这使得在复杂环境中获取旧的遗留数据集成为可能,并释放出相当大的价值,直到现在,FWI还无法从一个糟糕的初始模型中成功应用。
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Accelerating Subsurface Data Processing and Interpretation with Cloud-Based Full Waveform Inversion Systems
Understanding the earth's subsurface is critical to the needs of the exploration and production (E&P) industry for minimizing risk and maximizing recovery. Until recently, the industry's service sector has not made many advances in data-driven automated earth model building from raw exploration seismic data. But thankfully, that has now changed. The industry's leading technique to gain an unprecedented increase in resolution and accuracy when establishing a view of the interior of the earth is known as the Full Waveform Inversion (FWI). Advanced formulations of FWI are capable of automating subsurface model building using only raw unprocessed data. Cloud-based FWI is helping to accelerate this journey by encompassing the most sophisticated waveform inversion techniques with the largest compute facility on the planet. This combines to give verifiable accuracy, more automation and more efficiency. In this paper, we describe the transformation of enabling cloud-based FWI to natively take advantage of the public cloud platform's main strength in terms of flexibility and on-demand scalability. We start from lift-and-shift of a legacy MPI-based application designed to be run by a traditional on-prem job scheduler. Our specific goals are to (1) utilize a heterogeneous set of compute hardware throughout the lifecycle of a production FWI run without having to provision them for the entire duration, (2) take advantage of cost-efficient spare-capacity compute instances without uptime guarantees, and (3) maintain a single codebase that can be run both on on-prem HPC systems and on the cloud. To achieve these goals meant transitioning the job-scheduling and "embarrassingly parallel" aspects of the communication code away from using MPI, and onto various cloud-based orchestration systems, as well as finding cloud-based solutions that worked and scaled well for the broadcast/reduction operation. Placing these systems behind a customized TCP-based stub for MPI library calls allows us to run the code as-is in an on-prem HPC environment, while on the cloud we can asynchronously provision and suspend worker instances (potentially with very different hardware configurations) as needed without the burden of maintaining a static MPI world communicator. With this dynamic cloud-native architecture, we 1) utilize advanced formulations of FWI capable of automating subsurface model building using only raw unprocessed data, 2) extract velocity models from the full recorded wavefield (refractions, reflections and multiples), and 3) introduce explicit sensitivity to reflection moveout, invisible to conventional FWI, for macro-model updates below the diving wave zone. This makes it viable to go back to older legacy datasets acquired in complex environments and unlock considerable value where FWI until now has been impossible to apply successfully from a poor starting model.
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