resolving complex velocity and gas absorption features with full- waveform … · 2019-08-28 ·...

3
AEGC 2019: From Data to Discovery – Perth, Australia 1 Resolving complex velocity and gas absorption features with full- waveform inversion in the Taranaki Basin, New Zealand Yuelian Gong* Dominic Fell Robert Hunn WesternGeco WesternGeco WesternGeco Level 5, 256 St Georges Terrace Level 5, 256 St Georges Terrace Level 5, 256 St Georges Terrace Perth WA 6000, Australia Perth WA 6000, Australia Perth WA 6000, Australia [email protected] [email protected] [email protected] Richard Bisley Alexander Karvelas Bee Jik Lim WesternGeco WesternGeco WesternGeco Level 5, 256 St Georges Terrace Level 5, 256 St Georges Terrace Level 5, 256 St Georges Terrace Perth WA 6000, Australia Perth WA 6000, Australia Perth WA 6000, Australia [email protected] [email protected] [email protected] INTRODUCTION The Western Platform 3D survey is in the Taranaki Basin, offshore New Zealand. The survey was designed and positioned to provide a high-resolution 3D broadband data set on trend to and over multiple oil and gas fields to provide new insights into and allow better analysis of proven and unproven plays. The Taranaki Basin was formed in response to continental rifting that saw the New Zealand landmass break away from Australia and Antarctica in the Cretaceous. This rifting was characterized by rapid subsidence and the formation of numerous deep half-grabens that were subsequently filled by terrestrial and coastal environment siliciclastics. Miocene uplift resulted in an increase of sediment supply and the progradation of the shelf edge through the survey area. Pliocene extension and the onset of back-arc rifting in the Pleistocene resulted in reactivating faults as seen in the Maui sub-basin to the east of the survey area. Deriving an accurate, high-resolution, and geologically- consistent velocity model is always a challenge for the depth imager, especially in areas where there are shallow gas clouds distributed widely across the survey. The absorption effects of the shallow gas clouds result in phase distortion and amplitude attenuation beneath the gas, hindering the velocity model building of deeper structures if using the conventional ray- based common image point (CIP) tomography method (Xie et al., 2018 & Menzel-Jones, 2015). Full-waveform inversion (FWI) velocity model building operates on the raw shot gathers, providing the ability to achieve more-accurate and higher-resolution velocity models than can be achieved with tomography. In this survey, the gas clouds are very shallow. For example, the gas clouds over the Maari field are just 100 m below the seabed, while, in the Maui field, the gas is approximately 200 m below the seabed. Water depths in the survey are less than 100 m, which makes deriving an accurate Q model with reflection-based Q-tomography challenging due to the limited near-offset data content. If the shallow gas is not addressed properly, the strong amplitude attenuation and phase distortion could significantly impact the velocity model building and obscure and dim the imaging under the gas clouds. Visco-acoustic full-waveform inversion (Q-FWI), driven by diving-waves, is used to estimate the Q model for the shallow gas clouds (Cheng et al., 2015). The final imaging using the accurate high-resolution velocity and Q models successfully compensated for the complexities associated with the gas clouds, thereby significantly improving the reservoir level imaging. METHOD AND RESULTS The model building workflow is shown in Figure 1 and consists of the following steps: first, a detailed overburden velocity was obtained using two bands of diving-wave FWI followed by four CIP tomography updates. Then, Q-FWI was performed to resolve the shallow gas Q bodies, followed by least-squares FWI using reflection data to derive a high- SUMMARY The Western Platform multiclient survey is in the Taranaki Basin, offshore New Zealand. Legacy imaging efforts have suffered due to being unable to overcome the presence of multiple geological challenges. A complex shallow overburden across the survey area, in addition to The Cape Egmont Fault Zone, provide a significant challenge to velocity model building. Shallow gas clouds are also prevalent throughout the survey, impacting imaging at the key zones of interest. To address these challenges, full-waveform and common image point tomography were performed to derive a high-resolution velocity model. Q-FWI was used to derive a detailed Q model representing the shallow gas bodies. In this case study, we demonstrate the successful application of diving-wave FWI, Q-FWI, and FWI using reflection energy to resolve a high-resolution velocity and Q model. This detailed model enabled the final imaging performed with Q-Kirchhoff prestack depth migration to compensate for the complex kinematics and gas-related absorption effects observed in the survey. Key words: velocity model building, Q-full-waveform inversion, least-square reflections full waveform inversion, gas cloud imaging

Upload: others

Post on 10-Jun-2020

2 views

Category:

Documents


0 download

TRANSCRIPT

Page 1: Resolving complex velocity and gas absorption features with full- waveform … · 2019-08-28 · reflection energy to resolve a high-resolution velocity and Q model. This detailed

AEGC 2019: From Data to Discovery – Perth, Australia 1

Resolving complex velocity and gas absorption features with full-waveform inversion in the Taranaki Basin, New Zealand Yuelian Gong* Dominic Fell Robert Hunn WesternGeco WesternGeco WesternGeco Level 5, 256 St Georges Terrace Level 5, 256 St Georges Terrace Level 5, 256 St Georges Terrace Perth WA 6000, Australia Perth WA 6000, Australia Perth WA 6000, Australia [email protected] [email protected] [email protected] Richard Bisley Alexander Karvelas Bee Jik Lim WesternGeco WesternGeco WesternGeco Level 5, 256 St Georges Terrace Level 5, 256 St Georges Terrace Level 5, 256 St Georges Terrace Perth WA 6000, Australia Perth WA 6000, Australia Perth WA 6000, Australia [email protected] [email protected] [email protected]

INTRODUCTION

The Western Platform 3D survey is in the Taranaki Basin, offshore New Zealand. The survey was designed and positioned to provide a high-resolution 3D broadband data set on trend to and over multiple oil and gas fields to provide new insights into and allow better analysis of proven and unproven plays. The Taranaki Basin was formed in response to continental rifting that saw the New Zealand landmass break away from Australia and Antarctica in the Cretaceous. This rifting was characterized by rapid subsidence and the formation of numerous deep half-grabens that were subsequently filled by terrestrial and coastal environment siliciclastics. Miocene

uplift resulted in an increase of sediment supply and the progradation of the shelf edge through the survey area. Pliocene extension and the onset of back-arc rifting in the Pleistocene resulted in reactivating faults as seen in the Maui sub-basin to the east of the survey area. Deriving an accurate, high-resolution, and geologically-consistent velocity model is always a challenge for the depth imager, especially in areas where there are shallow gas clouds distributed widely across the survey. The absorption effects of the shallow gas clouds result in phase distortion and amplitude attenuation beneath the gas, hindering the velocity model building of deeper structures if using the conventional ray-based common image point (CIP) tomography method (Xie et al., 2018 & Menzel-Jones, 2015). Full-waveform inversion (FWI) velocity model building operates on the raw shot gathers, providing the ability to achieve more-accurate and higher-resolution velocity models than can be achieved with tomography. In this survey, the gas clouds are very shallow. For example, the gas clouds over the Maari field are just 100 m below the seabed, while, in the Maui field, the gas is approximately 200 m below the seabed. Water depths in the survey are less than 100 m, which makes deriving an accurate Q model with reflection-based Q-tomography challenging due to the limited near-offset data content. If the shallow gas is not addressed properly, the strong amplitude attenuation and phase distortion could significantly impact the velocity model building and obscure and dim the imaging under the gas clouds. Visco-acoustic full-waveform inversion (Q-FWI), driven by diving-waves, is used to estimate the Q model for the shallow gas clouds (Cheng et al., 2015). The final imaging using the accurate high-resolution velocity and Q models successfully compensated for the complexities associated with the gas clouds, thereby significantly improving the reservoir level imaging.

METHOD AND RESULTS The model building workflow is shown in Figure 1 and consists of the following steps: first, a detailed overburden velocity was obtained using two bands of diving-wave FWI followed by four CIP tomography updates. Then, Q-FWI was performed to resolve the shallow gas Q bodies, followed by least-squares FWI using reflection data to derive a high-

SUMMARY The Western Platform multiclient survey is in the Taranaki Basin, offshore New Zealand. Legacy imaging efforts have suffered due to being unable to overcome the presence of multiple geological challenges. A complex shallow overburden across the survey area, in addition to The Cape Egmont Fault Zone, provide a significant challenge to velocity model building. Shallow gas clouds are also prevalent throughout the survey, impacting imaging at the key zones of interest. To address these challenges, full-waveform and common image point tomography were performed to derive a high-resolution velocity model. Q-FWI was used to derive a detailed Q model representing the shallow gas bodies. In this case study, we demonstrate the successful application of diving-wave FWI, Q-FWI, and FWI using reflection energy to resolve a high-resolution velocity and Q model. This detailed model enabled the final imaging performed with Q-Kirchhoff prestack depth migration to compensate for the complex kinematics and gas-related absorption effects observed in the survey. Key words: velocity model building, Q-full-waveform inversion, least-square reflections full waveform inversion, gas cloud imaging

Page 2: Resolving complex velocity and gas absorption features with full- waveform … · 2019-08-28 · reflection energy to resolve a high-resolution velocity and Q model. This detailed

Resolving velocity and gas absorption features with FWI Gong et al.

AEGC 2019: From Data to Discovery – Perth, Australia 2

resolution velocity model. Lastly, a joint velocity and epsilon update was performed using CIP tomography to optimize the gather flatness. In this project, Q-FWI did not solely use a background Q model during the propagation. Q-FWI was used with the objective of deriving a high-resolution spatially variable Q-model. To facilitate the Q-model derivation, gas clouds were identified by their low velocity and used to define a mask to constrain the Q update. FWI makes inferences about subsurface models from recorded seismograms by solving a nonlinear least-square (LS) optimization problem:

min$

ℎ(𝐩[𝐦], 𝐝) Where m stands for subsurface models, h(∙,∙) measures the misfit between observed seismograms d and the prediction p[m] from a wavefield (Sun et al., 2018). The inversion is performed by iteratively updating the model fields to reduce the data differences between observed and simulated seismic waveforms. In Q-FWI, we first derive a new set of second-order visco-acoustic wave equations in the time domain. This allows us to explicitly separate Q-induced phase dispersion with amplitude attenuation so that, during wavefield back propagation, we can compensate for the phase dispersion without altering the wavefield amplitude. The estimation of Q is driven by phase mismatch between the observed and simulated seismic waveform, which are the dominant effects of Q at low frequencies. It is recommended to do the Q estimation when the velocity inversion is close to convergence (Cheng et al., 2015). In this case study, Q-FWI was performed at a frequency band centred at 6 Hz with a constant background Q (150) as the starting model.

Figure 1. Velocity model building workflow. Reflection-based full-waveform inversion is a category of approaches that employs a waveform-fitting-driven inversion process to build kinematically sound background models with reflections, which is usually a conventional FWI approach. Due to the limited illumination depth of refraction energy, FWI must rely on reflection energy to build models at depths where refractions cannot penetrate (Sun et al., 2016). The Q model obtained by Q-FWI accurately delineated the spatial and vertical extents of the gas bodies throughout the

survey, an example of which is shown in Figure 2(c). Detailed variations in Q within the larger Maari and Maui gas bodies around the Cape Egmont Fault Zone were also resolved, improving the structure and amplitude continuity of the target events close to the faults. LS FWI using reflection data was performed after the fourth tomography and Q-FWI update, once a kinematically accurate model had been obtained. This was followed by a joint velocity and epsilon tomography update to derive a model for a final residual gather flatness correction. This case study demonstrated that this combined workflow was able to resolve fine details in both the Q and velocity models representing the gas bodies, as shown in Figures 2 and 3. Additionally, the small-scale lateral velocity contrasts across the Cape Egmont Fault Zone were enhanced. This is a key region where tomography had been unable to achieve sufficient velocity resolution to mitigate the fault shadow and thereby, improve the imaging at the zone of interest.

CONCLUSIONS We present a comprehensive depth imaging workflow using a combination of different FWI and tomography methods to achieve high-resolution velocity and Q models in a complex geologic environment. With a kinematically accurate velocity model in place, applying Q-FWI followed by LS reflection FWI enabled obtaining detailed high-resolution models for both Q and velocity. Performing the imaging with Q-Kirchhoff prestack depth migration successfully compensated for the kinematic and absorptive effects of the shallow gas anomalies. By mitigating the complex overburden effects, reservoir-level imaging in this historically challenging basin were significantly improved.

ACKNOWLEDGEMENTS

We thank WesternGeco multiclient for their collaboration on this work and permission to present the results.

REFERENCES

Cheng, X., Kun, J., Sun, D. and Vigh, D., 2015, A new approach of visco-acoustic waveform inversion in the time domain: 85th Annual International Meeting, SEG, Expanded Abstracts, 1183-1187. Menzel-Jones, G., 2015, Application of Full Waveform Inversion and Q-Tomography for earth model building: shallow water, shallow gas case study: APGCE 2015, 25831. Mora, P., 1989, Inverion = migration + tomograpy: Geophysics, 54, 1575-1586. Sun, D., Kun, J., Sun, D. and Vigh, D., 2016, Reflection based Waveform Inversion: 86th Annual International Meeting, SEG, Expanded Abstracts, Expanded Abstracts, 1151-1156. Sun, D., Kun, J., Cheng, X. and Vigh, D., 2018, Keys to robust reflection-based full-waveform inversion: 88th Annual International Meeting, Expanded Abstracts, 1283-1287.

Page 3: Resolving complex velocity and gas absorption features with full- waveform … · 2019-08-28 · reflection energy to resolve a high-resolution velocity and Q model. This detailed

Resolving velocity and gas absorption features with FWI Gong et al.

AEGC 2019: From Data to Discovery – Perth, Australia 3

Xie, Y., Wang, N.I.L., Xiao, N.I.L. and Latter, N.I.L., 2018, Recent advances in Q model building and Q-compensating migration for imaging in the presence of complex gas clouds using P waves: 80th EAGE Conference & Exhibition, Expanded Abstracts, WS07.

Figure 2. (a) Tomography Update 04 seismic overlay with velocity model. (b) Tomography Update 05 seismic overlay with model. (c) Inverse Q model derived by Q-FWI.

Figure 3. (a) Q-PSDM stack after Tomography Update 04. (b) Q-PSDM stack after Tomography Update 05.