First Arrival Traveltime Tomography - When Simpler Is Better

Taillandier, Cédric; Deladerrière, N.; Therond, A.; Meur, D. Le

doi:10.3997/2214-4609.20149305

Cited by 6 publications

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“…Picking the first-arrival traveltimes is usually performed automatically due to the high number of traces involved and can be contaminated by some erroneous picks caused by high levels of noise in the input data. To reduce the influence of such erroneous picks in the minimization process, we use a more robust L1 misfit function in our applications: In this work, we employ an accurate eikonal solver based on the fast sweeping method (Noble et al, 2014) to compute the first-arrival traveltimes t(c) for a given velocity model c. The gradient ∇J (c) of the misfit function is computed on-the-fly by backpropagating the residuals along rays from the receiver points to the source, hence avoiding the need to build and store a very large Jacobian matrix (Taillandier et al, 2011). The misfit function J(c) is then iteratively minimized using a limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) optimization algorithm (Nocedal and Wright, 2006), which uses gradients from previous iterations to build a computationally efficient approximation to the Hessian, allowing for better-quality model updates compared to previously employed steepest-descent schemes.…”

Section: Refracted P-wave Workflowmentioning

confidence: 99%

“…The estimation of geologically meaningful near-surface P-wave velocity models can be challenging on land surveys due to rapid lateral variation in near-surface structures. Approaches based on refracted P-waves, such as first-arrival traveltime tomography (Noble et al, 2009;Taillandier et al, 2011), usually lack resolution in the shallow part of the medium because of the horizontal nature of the diving waves, although recent advances in full-waveform inversion (Virieux and Operto, 2009) show promising results in that respect. To overcome this issue, surface-wave inversion has become increasingly popular (Socco and Strobbia, 2004;Socco et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

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Near-surface velocity modeling using a combined inversion of surface and refracted P-waves

et al. 2016

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We propose an innovative workflow based on the complementary use of Rayleigh waves alongside standard P-wave refraction tomography, which better depicts the shallow part of the near-surface P-wave velocity model. Our surface-wave processing sequence led to an S-wave near-surface velocity field that can be used as a constraint for P-wave tomography and can improve P-wave statics determination. Rayleigh waves are processed in three steps. The first step consists of an accurate frequency-dependent traveltime measurement for each selected source-receiver pair in which the phase difference between two adjacent traces is used to derive the phase velocity. Then, a frequency-dependent surface-wave velocity tomography is performed from the picked traveltimes. Finally, after surface-wave tomography, the frequency-dependent phase velocity volume output by the tomography is inverted to deliver an S-wave near-surface velocity model. This model is used to constrain the first-arrival P-wave tomography. To illustrate our method, we use a 3D narrow-azimuth land data set, acquired along a river valley. Strong lateral velocity variations exist in the shallow part, with slow velocities around the unconsolidated sediments of the riverbed and faster velocities in the consolidated sediments of the surrounding hills. A combined first-arrival tomography using the S-wave velocity model, the initial unconstrained refracted P-wave velocity model, and the original first arrivals is used to obtain a more accurate near-surface P-wave velocity model. This new approach led to a constrained P-wave velocity model from which primary statics are derived and then applied, leading to an improved image with better focusing and continuity of thin layers in the shallowest part.

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Section: Refracted P-wave Workflowmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Near-surface velocity modeling using a combined inversion of surface and refracted P-waves

et al. 2016

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“…The processing sequence included adaptive groundroll attenuation to remove surface waves (Le Meur et al, 2008), frequencydependent noise attenuation for removal of spurious industrial/environment noises (Gulunay, 2008), first arrival tomography (Taillandier et al, 2011), surface-consistent compensations that benefit from the latter simultaneous decomposition for deconvolution filters and amplitude scalars (Garceran and Le Meur, 2012), as well as long-wavelength amplitude corrections under well constraints. Moreover, a stochastic approach for surface-consistent residual statics coupled with a customized IT…”

Section: Array-free Processing Testmentioning

confidence: 99%

Processing and Preliminary Interpretation of the Ultra High-Density Full-Azimuth 3D Seismic Survey, Dukhan Field, Qatar

et al. 2014

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The Dukhan Field 3D seismic survey is an ultra high-density wide azimuth survey (i.e., point source and receiver, short source and receiver line interval, and full azimuth recording) intended to provide a significant uplift in quality and resolution to enable the imaging of reservoir sub-layers and fine-scale structural features. This paper presents the challenges and lessons learned from full-field processing and inversion. Seismic interpretation demonstrates added value to the field, which leads to improved reservoir models and support to well operations through improved well placement. Subtle structural, stratigraphic, and diagenetic features are now imaged.Knowledge gained from processing this survey includes: 1) cost, time, and effort invested on the Dukhan 3D survey is worthwhile as high quality results are obtained at 15m bin processing; 2) 3.75m bin processing is now possible, and could be performed to improve image quality over the full seismic bandwidth for all reservoir intervals; 3) Pre-Stack Depth Migration (PSDM) workflow is recommended for optimal offset and azimuth reconciliation and noise attenuation; 4) the adverse effect of near-surface geologic variability on seismic data quality can be improved by utilizing microgravity and resistivity (Vertical Electrical Sounding) techniques; and 5) proper preconditioning of the input data is critical for reliable prediction of reservoir properties away from well control.While industry's knowledge and experience in acquisition and field operation of ultra high-density full-azimuth seismic has increased over the last 3 years, processing of such data to ensure optimal imaging and reliable reservoir property predictions (e.g., porosity) has lagged. QP believes that lessons learned through this round of processing can provide insight for prediction of rock properties in the major Dukhan reservoirs.

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“…First-arrival travel time tomography has become a valuable tool for obtaining the shallow velocity information needed for deriving refraction statics corrections, velocity model building in pre-stack depth migration or full waveform inversion (Taillandier et al, 2011). In refraction statics tomography the first break picks in the seismic data are used to derive a detailed velocity model of the near surface through tomographic inversion of travel times.…”

Section: First-arrival Tomographic Inversion Processingmentioning

confidence: 99%

Improved Statics Model of a Marine 3D Seismic Dataset Through the Application of Refraction Statics Tomography Processing, Offshore Qatar

Lange¹,

Hanitzsch²,

Zahran

et al. 2014

All Days

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This paper describes the refraction statics processing based on tomographic inversion of a 503km 2 subset of a modern marine 3D seismic dataset acquired in very shallow waters offshore Qatar. The objective of the seismic survey was imaging of the Mesozoic interval from 0.4 -1.8 seconds two way travel time below sea level. Characteristic for the study area is the presence of local shoal bodies, often associated with coral reefs at sea bottom and in the near surface below sea bottom. These features can have a significant effect on the imaging of seismic data and therefore the prospectivity assessment of the exploration area as their typically high velocity introduces distortions in the timing of events, i.e. false structures might be generated or true structures suppressed. Compensating for the reef structures in the statics model results in a more accurate image of the subsurface.This was achieved by applying first-arrival travel time tomography to obtain the shallow velocity information needed to calculate refraction statics corrections. Refraction statics tomography uses the first break travel time picks of the seismic data to derive a velocity model of the near surface. This velocity model is then used to generate static shifts to correct the data to a final datum plane using a known replacement velocity, thereby removing the velocity variation caused by the sea bottom and near surface features. The tomographic inversion algorithm for land data was adapted to marine data by including a new option to freeze the water column velocity, which should be constant and not taken into account in the velocity updates. Refraction statics tomography is superior to conventional refraction statics because the inverted velocity model reveals the lateral and vertical velocity variations in the near surface.The dense shot and receiver spacing of this data set provided a large number of first break picks for the tomographic inversion process and resulted in a stable near surface velocity model. The computed static shifts corrected for some of the time shifts observed below the sea bottom features. The application of refraction statics tomography in this study provided an improved subsurface image compared to the original processing.

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First Arrival Traveltime Tomography - When Simpler Is Better

Cited by 6 publications

References 0 publications

Near-surface velocity modeling using a combined inversion of surface and refracted P-waves

Near-surface velocity modeling using a combined inversion of surface and refracted P-waves

Processing and Preliminary Interpretation of the Ultra High-Density Full-Azimuth 3D Seismic Survey, Dukhan Field, Qatar

Improved Statics Model of a Marine 3D Seismic Dataset Through the Application of Refraction Statics Tomography Processing, Offshore Qatar

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