Ten years of time-lapse seismic on Atlantis Field

2021 ◽  
Vol 40 (7) ◽  
pp. 494-501
Author(s):  
Jean-Paul van Gestel
Keyword(s):  
Waveform Inversion ◽  
Water Injection ◽  
Main Tool ◽  
Time Lapse ◽  
Velocity Model ◽  
Lessons Learned ◽  
Ocean Bottom ◽  
Injection Wells ◽  
History Match ◽  
Business Decisions

In 2019, the fourth ocean-bottom-node survey was acquired over Atlantis Field. This survey was quickly processed to provide useful time-lapse (4D) observations two months after the end of the acquisition. The time-lapse observations were immediately valuable in placing wells, refining final drilling target locations, updating well prioritization, and sequencing production and water-injection wells. These data are indispensable pieces of information that bring geophysicists and reservoir engineers together and focus the conversation on key remaining uncertainties such as fault transmissibilities and drainage areas. Time-lapse observations can confirm the key conceptional models already in place but are even more valuable when they highlight alternative models that have not yet been considered. The lessons learned from the acquisition, processing, analysis, interpretation, and integration of the data are shared. Some of these lessons are reiterations of previous work, but several new lessons originated from the latest 2019 acquisition. This was the first survey in which independent simultaneous sources were successfully deployed to collect a time-lapse survey. This resulted in a much faster and less expensive acquisition. In addition, full-waveform inversion was used as the main tool to update the velocity model, enabling a much faster turnaround in processing. The fast turnaround enabled incorporation of the latest acquisition to better constrain the velocity model update. The updated velocity model was used for the final time-lapse migration. In the integration part, the 4D-assisted history-match workflow was engaged to update the reservoir model history match. All of the upgrades led to an overall faster, less expensive, and better way to incorporate the acquired data in the final business decisions.

Target-oriented inversion using the Patched Green′s function method

Geophysics ◽  
2021 ◽  
pp. 1-77
Author(s):  
Danyelle da Silva ◽  
Edwin Fagua Duarte ◽  
Wagner Almeida ◽  
Mauro Ferreira ◽  
Francisco Alirio Moura ◽  
...  
Keyword(s):  
Condensed Matter ◽  
Waveform Inversion ◽  
Time Lapse ◽  
Velocity Model ◽  
Dyson Equation ◽  
Computational Time ◽  
Green Functions ◽  
Target Area ◽  
Computational Domain ◽  
Full Waveform

We have designed a target-oriented methodology to perform Full Waveform Inversion using a frequency-domain wave propagator based on the so-called Patched Green’s Function (PGF) technique. Originally developed in condensed matter physics to describe electronic waves in materials, the PGF technique is easily adaptable to the case of wave propagation in a spatially variable media in general. By dividing the entire computational domain into two sections, namely the target area and the outside target area, we calculate the Green Functions related to each section separately. The calculations related to the section outside the target are performed only once at the beginning of inversion, whereas the calculations in the target area are performed repeatedly for each iteration of the inversion process. With the Green Functions of the separate areas, we calculate the Green Functions of the two systems patched together through the application of a Recursive Dyson equation. By performing 2D and time-lapse experiments on the Marmousi model and a Brazilian Pre-salt velocity model, we demonstrate that the target-oriented PGF reduces the computational time of the inversion without compromising accuracy. In fact, when compared with conventional FWI results, the PGF-based calculations are identical but done in a fraction of the time.

Multi-parameter model building for the Qiuyue structure using four-component ocean-bottom seismometer data

Geophysics ◽  
2021 ◽  
pp. 1-52
Author(s):  
Yuzhu Liu ◽  
Xinquan Huang ◽  
Jizhong Yang ◽  
Xueyi Liu ◽  
Bin Li ◽  
...  
Keyword(s):  
Wave Velocity ◽  
Waveform Inversion ◽  
Velocity Model ◽  
P Wave ◽  
Velocity Analysis ◽  
Full Waveform Inversion ◽  
Ocean Bottom ◽  
Ocean Bottom Seismometer ◽  
Full Waveform ◽  
P Wave Velocity

Thin sand-mud-coal interbedded layers and multiples caused by shallow water pose great challenges to conventional 3D multi-channel seismic techniques used to detect the deeply buried reservoirs in the Qiuyue field. In 2017, a dense ocean-bottom seismometer (OBS) acquisition program acquired a four-component dataset in East China Sea. To delineate the deep reservoir structures in the Qiuyue field, we applied a full-waveform inversion (FWI) workflow to this dense four-component OBS dataset. After preprocessing, including receiver geometry correction, moveout correction, component rotation, and energy transformation from 3D to 2D, a preconditioned first-arrival traveltime tomography based on an improved scattering integral algorithm is applied to construct an initial P-wave velocity model. To eliminate the influence of the wavelet estimation process, a convolutional-wavefield-based objective function for the preprocessed hydrophone component is used during acoustic FWI. By inverting the waveforms associated with early arrivals, a relatively high-resolution underground P-wave velocity model is obtained, with updates at 2.0 km and 4.7 km depth. Initial S-wave velocity and density models are then constructed based on their prior relationships to the P-wave velocity, accompanied by a reciprocal source-independent elastic full-waveform inversion to refine both velocity models. Compared to a traditional workflow, guided by stacking velocity analysis or migration velocity analysis, and using only the pressure component or other single-component, the workflow presented in this study represents a good approach for inverting the four-component OBS dataset to characterize sub-seafloor velocity structures.

Interpreter's Corner

2020 ◽  
Vol 39 (11) ◽  
pp. 828-833
Author(s):  
Henrik Roende ◽  
Dan Chaikin ◽  
Yi Huang ◽  
Konstantin N. Kudin
Keyword(s):  
Model Building ◽  
New Technology ◽  
Waveform Inversion ◽  
Velocity Model ◽  
Ocean Bottom ◽  
Hydrocarbon Traps ◽  
Full Waveform ◽  
Velocity Model Building ◽  
Long Offset ◽  
Set Up

The U.S. Gulf of Mexico (GoM) geology is well known for prolific structural hydrocarbon traps created by salt tectonics. In many areas, these structures lie below salt overhangs or thick canopies, requiring advanced seismic imaging to identify prospects and plan exploration wells. Ever-evolving geophysical technologies, such as 3D seismic, wide azimuth, multiwide azimuth, coil, and ocean-bottom node (OBN) acquisition designs, have unlocked the image for some of these structures over the past three decades. Recently, automatic velocity model building methods, particularly full-waveform inversion (FWI), introduced another step change in the subsalt image quality and refocused the acquisition methods on the need to acquire long-offset data. To make such a long-offset program affordable, a new survey geometry was set up with sparse OBN nodes and simultaneous shooting. The actual survey was acquired in 2019 and fully processed within 15 months from the end of the acquisition. Offsets up to 65 km were recorded, enabling FWI velocity updates down to 15 km depth. To provide the reader with a glimpse of the geologic insight that the new technology enabled, we report a few examples of deep geology revealed by this survey in a hydrocarbon- and seismic-data-rich area of the GoM — the Greater Mars-Ursa Basin.

Combining reflection and transmission information in time-lapse velocity inversion: A new hybrid approach

Geophysics ◽  
2019 ◽  
Vol 84 (4) ◽  
pp. R601-R611 ◽  
Author(s):  
Maria Kotsi ◽  
Jonathan Edgar ◽  
Alison Malcolm ◽  
Sjoerd de Ridder
Keyword(s):  
Waveform Inversion ◽  
Hybrid Approach ◽  
Time Lapse ◽  
Velocity Model ◽  
Nonlinear Inversion ◽  
Image Domain ◽  
Velocity Inversion ◽  
Reflected Waves ◽  
Deep Diving ◽  
Velocity Changes

Full-waveform inversion (FWI) uses the information of the full wavefield to deliver high-resolution images of the subsurface. Conventional time-lapse FWI primarily uses the transmitted component (diving waves) of the wavefield to reconstruct the low-wavenumber component of the velocity model. This requires large-offset surveys and low-frequency data. When the target of interest is deep, diving waves cannot reach the target and FWI will be dominated by the reflected component of the wavefield. Consequently, the retrieved model resembles a least-squares migration instead of a velocity model. Image-domain methods, especially image-domain wavefield tomography (IDWT), have been developed to obtain a model of time-lapse velocity changes in deeper targets using reflected waves. The method is able to recover models of deep targets. However, it also tends to obtain smeared time-lapse velocity changes. We have developed a form of time-lapse waveform inversion that we call dual-domain time-lapse waveform inversion (DDWI), whose objective function joins FWI and IDWT, combining information from the diving waves in the data-domain FWI term with information from the reflected waves in the image-domain IDWT term. During the nonlinear inversion, the velocity model is updated using constraints from both terms simultaneously. Similar to sequential time-lapse waveform inversion, we start the time-lapse inversion from a baseline model recovered with FWI. We test DDWI on a variety of synthetic models of increasing complexity and find that it can recover time-lapse velocity changes more accurately than when both methods are used independently or sequentially.

scholarly journals Crustal-scale depth imaging via joint full-waveform inversion of ocean-bottom seismometer data and pre-stack depth migration of multichannel seismic data: a case study from the eastern Nankai Trough

Solid Earth ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 765-784 ◽  
Author(s):  
Andrzej Górszczyk ◽  
Stéphane Operto ◽  
Laure Schenini ◽  
Yasuhiro Yamada
Keyword(s):  
Seismic Data ◽  
Nankai Trough ◽  
Waveform Inversion ◽  
Velocity Model ◽  
Full Waveform Inversion ◽  
Ocean Bottom ◽  
Ocean Bottom Seismometer ◽  
Depth Migration ◽  
Full Waveform ◽  
Air Gun

Abstract. Imaging via pre-stack depth migration (PSDM) of reflection towed-streamer multichannel seismic (MCS) data at the scale of the whole crust is inherently difficult. This is because the depth penetration of the seismic wavefield is controlled, firstly, by the acquisition design, such as streamer length and air-gun source configuration, and secondly by the complexity of the crustal structure. Indeed, the limited length of the streamer makes the estimation of velocities from deep targets challenging due to the velocity–depth ambiguity. This problem is even more pronounced when processing 2-D seismic data due to the lack of multi-azimuthal coverage. Therefore, in order to broaden our knowledge about the deep crust using seismic methods, we present the development of specific imaging workflows that integrate different seismic data. Here we propose the combination of velocity model building using (i) first-arrival tomography (FAT) and full-waveform inversion (FWI) of wide-angle, long-offset data collected by stationary ocean-bottom seismometers (OBSs) and (ii) PSDM of short-spread towed-streamer MCS data for reflectivity imaging, with the former velocity model as a background model. We present an application of such a workflow to seismic data collected by the Japan Agency for Marine-Earth Science and Technology (JAMSTEC) and the Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER) in the eastern Nankai Trough (Tokai area) during the 2000–2001 Seize France Japan (SFJ) experiment. We show that the FWI model, although derived from OBS data, provides an acceptable background velocity field for the PSDM of the MCS data. From the initial PSDM, we refine the FWI background velocity model by minimizing the residual move-outs (RMOs) picked in the pre-stack-migrated volume through slope tomography (ST), from which we generate a better-focused migrated image. Such integration of different seismic datasets and leading-edge imaging techniques led to greatly improved imaging at different scales. That is, large to intermediate crustal units identified in the high-resolution FWI velocity model extensively complement the short-wavelength reflectivity inferred from the MCS data to better constrain the structural factors controlling the geodynamics of the Nankai Trough.

The impact of acquisition geometry on full-waveform inversion updates

2021 ◽  
Vol 40 (5) ◽  
pp. 335-341
Author(s):  
Denes Vigh ◽  
Xin Cheng ◽  
Kun Jiao ◽  
Wei Kang ◽  
Nolan Brand
Keyword(s):  
Model Building ◽  
Waveform Inversion ◽  
Velocity Model ◽  
Depth Image ◽  
Earth Model ◽  
Full Waveform Inversion ◽  
Ocean Bottom ◽  
Full Waveform ◽  
Data Content ◽  
The Impact

Full-waveform inversion (FWI) is a high-resolution model-building technique that uses the entire recorded seismic data content to build the earth model. Conventional FWI usually utilizes diving and refracted waves to update the low-wavenumber components of the velocity model. However, updates are often depth limited due to the limited offset range of the acquisition design. To extend conventional FWI beyond the limits imposed by using only transmitted energy, we must utilize the full acquired wavefield. Analyzing FWI kernels for a given geology and acquisition geometry can provide information on how to optimize the acquisition so that FWI is able to update the velocity model for targets as deep as basement level. Recent long-offset ocean-bottom node acquisition helped FWI succeed, but we would also like to be able to utilize the shorter-offset data from wide-azimuth data acquisitions to improve imaging of these data sets by developing the velocity field with FWI. FWI models are heading toward higher and higher wavenumbers, which allows us to extract pseudoreflectivity directly from the developed velocity model built with the acoustic full wavefield. This is an extremely early start to obtaining a depth image that one would usually produce in much later processing stages.

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