The paper presents a review of classic rock physics models used for clastic sedimentary rocks and how they have been combined into extended models through the introduction of a few parameters associated with a compositional or textural property of the rock. The models are used on a variety of real data sets to showcase how rock properties can be inferred from elastic properties.
In this paper, we present a high-resolution FWI case study from the foothills of the Southern Carpathians, in Romania. The input data was acquired in 2022, mostly with dynamite source, and designed for optimal subsurface wide azimuthal illumination. The varied rough terrain and the presence of a complex thrust body represent the main imaging challenges. Firstly, we will show how the combination of Multi-Wave Inversion (MWI), and Full-Waveform Inversion (FWI) enabled us to construct a high-resolution near-surface velocity model that solved some important imaging distortions. Next, we will outline the benefit of inverting data to 50Hz to attain a high resolution FWI model which fully captures the strong lateral and vertical velocity contrasts of the upper folded structures and the underlying stratigraphy. Finally, the migrated image and derived reflectivity from this 50Hz FWI model has helped to de-risk the reservoir uncertainties in both size and positioning of the crest. These enhance the imaging to better define the spill point of the prospect.
Well drilling and abandonment work offshore Brunei is very challenging due to the presence of complex shallow geological features, such as corals, river channels, gas clouds/chimneys, and small faults. Therefore high-resolution seismic images, which can reveal those features, are desired to help avoid potential geohazards. In this study, Least-Squares Wave-Equation Multiple Migration (LSWEMM) was used to produce 3D high-resolution shallow images. The new results have both high spatial and temporal resolution. The near water bottom geological features are more clearly revealed than on conventional primary images. The resolution of fine channels and small faults is also greatly improved compared to nearfield hydrophone (NFH) images. The new mapping of shallow channels is very helpful for ongoing geohazard assessment work near a planned well. LSWEMM was applied on existing OBN data, hence is much more cost effective compared with an additional survey, such as 3D P-cable acquisition. Moreover, decimation studies show great value in optimizing future OBN acquisition and 2D high-resolution survey design, which could further reduce the cost associated with geohazard assessment work.
Deriving an accurate high-resolution seismic quality factor (Q) model is necessary to both compensate for the phase dispersion and amplitude attenuation effects of the Earth’s anelasticity during imaging, and to reduce parameter cross-talk during velocity model building (VMB). Various methods exist for deriving Q, each with potential inherent limitations that can restrict their suitability for scalable applications, such as over the ?14,000 km2 area presented here in the Norwegian North Sea. We describe a multi-process Q derivation workflow, including dual-azimuth Q full-waveform inversion (Q-FWI), time-lag FWI guided ray-based Q tomography, and well-synthetic validations using Q sensitive metrics to derive a regional high-resolution Q model. When incorporated into VMB and subsequent Q migrations, the regional Q model is shown to both locally and regionally compensate for amplitude attenuation and phase dispersion, and providing an associated improvement in imaging.
We present a 4D pre-salt reservoir monitoring study from two wide-azimuth towed-streamer (WATS) surveys. Advanced flows were implemented to mitigate complex salt-related challenges and WATS repeatability issues in the image domain. Three models of converted waves interfering at reservoir level were generated via dual salt-flood Kirchhoff demigrations, and then subtracted at the post-migration stage. Repeatability issues were also addressed in the image domain via a novel 4D Least-Squares Wave-Equation Kirchhoff flow. The results reveal an unexpected level of 4D signal in such a complex geological setting.
Much recent work has been done on comparing machine learning and geophysical inversion techniques to the extraction of model parameters from seismic reflection data. In our profession we are used to analyzing the physics of geophysical problems in detail. However, in many of the recent studies the machine learning algorithms are treated almost as “black boxes”. In this study l I will use a straightforward numerical example to illustrate the difference between geophysical inversion and machine learning inversion. In doing so I will try to “demystify” machine learning algorithms and show that, like inverse problems, they have a definite mathematical structure that can be written down and understood. The example used is this tutorial is the extraction of the underlying reflection coefficients from an overlapping wavelet response that was created by convolving a reflection coefficient dipole with a symmetric wavelet. In discussing the solution to this problem I will cover the topics of deconvolution, recursive inversion, linear regression and nonlinear regression using a feedforward neural network. I will present both the full inverse approach as well as gradient descent algorithms, which can be applied to both linear and nonlinear problems. This will lead to a description of the backpropagation algorithm, which is used to train a feedforward neural network. In the final section of the tutorial I will look at the impact of local minima in the search for a global minimum in the backpropagation algorithm.
The hunt for less obvious deeper targets below the Base Cretaceous Unconformity (BCU) within the Norwegian North Sea greatly relies on the accuracy of the velocity model as it impacts the definition of the structural traps. The presence of the limestone/carbonate sequence with high velocities overlying the targeted lower velocity mudstone units represents the main challenge in term of velocity model building and is usually out of the reach of diving waves for the full waveform inversion (FWI) application when using streamer-based data with offset limited to 8km. The recent shift of acquisition industry toward hybrid acquisition combining streamer and sparse node opens the road for deeper application of FWI and even more. In this paper, we show how this new hybrid acquisition design can help to build a reliable high-resolution velocity model down to the Brent level. Moreover, we also present how elastic FWI enables us to better explain the elastic effects induced by the large impedance contrast at BCU level.
Full-waveform inversion (FWI) has demonstrated tremendous potential to provide high-resolution models of the subsurface in different geological settings. However, in complex environments such as salt and subsalt, relying on acoustic approximations in FWI limits the accuracy of Earth models derived from modern field data, and may require complex workflows to mitigate the challenges associated with large elastic parameter variations. Recent case studies highlight this issue and suggest that more accurate models can be produced by elastic FWI using simpler workflows in these areas. In addition to elasticity, wave propagation in the subsurface undergoes anelastic (viscous) effects, especially through absorption in gas-charged layers. Although anelastic effects are known to be significant in many visco-acoustic case studies, few examples consider both elastic and anelastic effects in FWI. In this paper, we present a visco-elastic FWI to invert for P-wave velocity and associated viscosity. We demonstrate our approach using ocean-bottom node data from the Central North Sea in a complex area associated with strong velocity contrasts and shallow absorption anomalies. Results in this area demonstrate that visco-elastic FWI can provide high-resolution viscosity and velocity models, and an FWI Image with improved event continuity, resolution, and signal-to-noise ratio compared to visco-acoustic FWI.
The Culzean field, in the North Sea, has been producing since 2019 gas condensate from fluvial sandstones located within dipping rotated fault blocks at approximately 4km of depth. Two surveys have been acquired with ocean bottom sensors to image and then monitor the evolution of the reservoir during production. In addition to classical time-lapse seismic processing, a time-lapse FWI has been performed to estimate the velocity variation over the production time. Due to the thick chalk layer located just above the target structure and the dipping nature of the reservoir, 4D FWI is the ideal tool compared to more conventional 1D approach based on time-shift estimations. This fast velocity layer represents a challenge for velocity model building and processing in general as it prevents the penetration of diving waves even with 7km of offset and also generates strong multiple curtains covering the reservoir interval. Despite the shallow water environment and complex geology, the 4D FWI implemented in this project was able to recover velocity variations as weak as 1% after only 3 years of production, providing crucial information that can help reservoir evolution assessment.