Application of Full Waveform Inversion and Q-tomography for Earth Model Building - Shallow Water, Shallow Gas Case Study

Velocity model building is one of the most difficult aspects of the seismic processing sequence. But it is also one of the most important: an accurate earth model allows an accurate migrated image to be formed, which allows the geologist a better chance at an accurate interpretation of the area. In addition, the velocity model itself can provide complementary information about the geology and geophysics of the region. Full-waveform inversion (FWI) is a popular, high-end velocity model-building tool that can generate high-resolution earth models, especially in regions of the model probed by the transmitted (diving wave) arrivals on the recorded seismic data. The history of the South Gabon Basin is complex, leading to a rich geologic picture today and a very challenging velocity model-building process. We have developed a case study from the offshore Gabon area showing that FWI is able to help with the model-building process, and the resulting velocity model reveals features that improve the migrated image. The application of FWI is made on an extremely large area covering approximately 25,000 [Formula: see text], demonstrating that FWI can be applied to this magnitude of survey in a timely manner. In addition, the detail in the FWI velocity model aids the geologic interpretation by highlighting, among other things, the location of shallow gas pockets, buried channels, and carbonate rafts. The concept of actively using the FWI-derived velocity model to aid the interpretation in areas of complex geology, and/or to identify potential geohazards to avoid in an exploration context, is applicable to many parts of the world.

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Application of multiparameter full-waveform inversion in Central Luconia Basin, Sarawak

Interpretation ◽

10.1190/int-2016-0038.1 ◽

2016 ◽

Vol 4 (4) ◽

pp. SU17-SU24 ◽

Cited By ~ 3

Author(s):

Vanessa Goh ◽

Kjetil Halleland ◽

René-Édouard Plessix ◽

Alexandre Stopin

Keyword(s):

Model Building ◽

Waveform Inversion ◽

Velocity Model ◽

Thin Layers ◽

Earth Model ◽

Full Waveform Inversion ◽

Data Set ◽

Full Waveform ◽

Velocity Variations ◽

Central Luconia

Reducing velocity inaccuracy in complex settings is of paramount importance for limiting structural uncertainties, therefore helping the geologic interpretation and reservoir characterization. Shallow velocity variations due, for instance, to gas accumulations or carbonate reefs, are a common issue offshore Malaysia. These velocity variations are difficult to image through standard reflection-based velocity model building. We have applied full-waveform inversion (FWI) to better characterize the upper part of the earth model for a shallow-water field, located in the Central Luconia Basin offshore Sarawak. We have inverted a narrow-azimuth data set with a maximum inline offset of 4.4 km. Thanks to dedicated broadband preprocessing of the data set, we could enhance the signal-to-noise ratio in the 2.5–10 Hz frequency band. We then applied a multiparameter FWI to estimate the background normal moveout velocity and the [Formula: see text]-parameter. Full-waveform inversion together with broadband data processing has helped to better define the faults and resolve the thin layers in the shallow clastic section. The improvements in the velocity model brought by FWI lead to an improved image of the structural closure and flanks. Moreover, the increased velocity resolution helps in distinguishing between two different geologic interpretations.

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Reducing Project Turnaround by Optimizing the Model Building Workflow Using Full-waveform Inversion and Reflection Tomography - A North Sea Case Study

79th EAGE Conference and Exhibition 2017 ◽

10.3997/2214-4609.201701161 ◽

2017 ◽

Author(s):

S. Gupta ◽

M. Steiger-Jarvis ◽

J. Bailey

Keyword(s):

North Sea ◽

Model Building ◽

Waveform Inversion ◽

Full Waveform Inversion ◽

Full Waveform ◽

Reflection Tomography

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Accelerating Subsurface Data Processing and Interpretation with Cloud-Based Full Waveform Inversion Systems

10.2118/207744-ms ◽

2021 ◽

Author(s):

Sirivan Chaleunxay ◽

Nikhil Shah

Keyword(s):

Service Sector ◽

Model Building ◽

Job Scheduling ◽

Waveform Inversion ◽

Earth Model ◽

Full Waveform Inversion ◽

Spare Capacity ◽

Velocity Models ◽

Full Waveform ◽

Job Scheduler

Abstract 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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