Innovative application of full-waveform inversion applied to extended wide-azimuth marine streamer seismic in a complex salt environment

We demonstrated the application of full-waveform inversion (FWI) guided velocity model building to an extended wide-azimuth towed streamer (EWATS) seismic data set in the Gulf of Mexico. Field data were collected over a historically challenging imaging area, colloquially called the “grunge zone” due to the formation of a compressional allosuture emplaced between two colliding salt sheets. These data had a poor subsalt image below the suture with conventional narrow-azimuth data. Additional geologic complexities were observed including high-velocity carbonate carapace near the top of salt and multiple intrasalt sedimentary inclusions. As such, improved seismic imaging was required to plan and execute wells targeting subsalt strata. Significant improvements to the velocity model and subsalt image were evident with wide-azimuth towed streamer and later EWATS data using conventional top-down velocity model building approaches. Then, high-impact improvements were made using EWATS data with an FWI velocity model building workflow; this study represented an early successful application of FWI used to update salt body geometries from streamer seismic data, in which many past applications were limited to improving sedimentary velocities. Later petrophysical data verified the new FWI-derived model, which had significantly increased confidence in the structural and stratigraphic interpretation of subsalt reservoir systems below the grunge zone.

Quantitative mapping of thickness variations along a ray path using geometrical full waveform inversion and guided wave mode conversion

Quantitative guided wave thickness mapping in plate-like structures and pipelines is of significant importance for the petrochemical industry to accurately estimate the minimum remaining wall thickness in the presence of corrosion, as guided waves can inspect a large area without needing direct access. Although a number of inverse algorithms have been studied and implemented in guided wave reconstruction, a primary assumption is widely used: the three-dimensional guided wave inversion of thickness is simplified as a two-dimensional acoustic wave inversion of velocity, with the dispersive nature of the waves linking thickness to velocity. This assumption considerably simplifies the inversion procedure; however, it makes it impossible to account for mode conversion. In reality, mode conversion is quite common in guided wave scattering with asymmetric wall loss, and compared with non-converted guided wave modes, converted modes may provide greater access to valuable information about the thickness variation, which, if exploited, could lead to improved performance. Geometrical full waveform inversion (GFWI) is an ideal tool for this, since it can account for mode conversion. In this paper, quantitative thickness reconstruction based on GFWI is developed in a plate cross-section and applied to study the performance of thickness reconstruction using mode conversion.

Introduction to this special section: Seismic acquisition

In the life cycle of a seismic product, the lion's share of the budget and personnel hours is spent on acquisition. In most modern seismic surveys, acquisition involves hundreds of specialized personnel working for months or years. Seismic acquisition also must overcome potential liabilities and health, safety, and environmental concerns that rival facility, pipeline, construction, and other operational risks. As only properly acquired data can contribute effectively to processing and interpretation strategies, a great deal of importance is placed on acquisition quality. Arguably, many of the advances the seismic industry has experienced find their origin arising from advances in acquisition techniques. Full-waveform inversion (FWI), for example, can reach its full potential only when seismic acquisition has provided both low frequencies and long offsets.

Multisource Seismic Full Waveform Inversion of Metal Ore Bodies

The seismic exploration method could explore deep metal ore bodies (depth > 1000 m). However, it is difficult to describe the geometry of the complex metal ore body accurately. Seismic full waveform inversion is a relatively new method to achieve accurate imaging of subsurface structures, but its success requires better initial models and low-frequency data. The seismic data acquired in the metal mine area is usually difficult to meet the requirements of full waveform inversion. The passive seismic data usually contains good low frequency information. In this paper, we use both passive and active seismic datasets to improve the full waveform inversion results in the metal mining area. The results show that the multisource seismic full waveform inversion could obtain a suitable result for high-resolution seismic imaging of metal ore bodies.

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.