For wavelengths relevant to seismic monitoring, rock formations respond as an effective elastic medium with spatially varying properties. For a given rock matrix, the saturating fluids affect the effective elastic properties . The change in these properties during fluid displacement is calculated using fluid substitution models. The accuracy of these models has become very important due to the growth in enhanced oil recovery and CO2 storage projects. The industry currently the Batzle-Wang model (Batzle et al. (1996), originaly developed for hydrocarbon gases. The commonly useed modification of the model for CO2 (Xu, 2006) is a substantial improvement, but is not accurate enough for interpretation of seismic monitoring. In this work, we introduce a new CO2 fluid substitution model and compare our results with the modified Batzle et al.. model for CO2 (Xu, 2006). The impact of the new model for CO2-EOR and CO2 storage crosswell seismic predictions is also evaluated. Our analysis shows that the effect of fluid substitution model on first arrival predictions is largest under Gassmann assumptions but may be comparable to the effect of petrophysical uncertainty.
Hydraulic fracturing is a process that generates (micro)seismic events due to fluid and stress interactions with the existing rock. In this paper, we examine different approaches to assessing the dynamics of the microseismicity in a reservoir during a fracture treatment: b-value analysis, stress release as estimated through apparent stress and static stress drop, and fracture complexity as assessed through Seismic Moment Tensor Inversion (SMTI). The b-value is often cited as relating to the complexity of the network but our results show that for the dataset considered, that fracture complexity is most closely associated with the stress release and inversely related to the b-value. Our results indicate that a combined analysis incorporating b-values, source parameters and SMTI is necessary for obtaining an understanding of the growth of event clusters during fracture treatments.
Full wavefield migration (FWM) is an inversion-based imaging algorithm that utilizes the complete reflection measurements: primaries as well as all multiples, both surface and internal. Using multiples in the imaging can extend the illumination of the subsurface. In this paper we concentrate the study on the internal scattering that can be helpful in imaging structures from below that are otherwise difficult to image by primaries, i.e. an undershooting setting. This can be fruitful in the case of the obstacles, like oil-production facilities, or in the case of poor illumination, like sub-salt imaging or near-surface complexities. We demonstrate such approach on two synthetic examples.
The purpose of this work is to evaluate the geo-potentials of graptolitic argillite in northern Estonia from the point of geology and even more from economic perspective, i.e. to understand spatial features of the shale layers including changes in thickness and compositional dynamics. The Late Cambrian to Ordovician crustal section of Estonia contains kukersite oil shale as well as graptolite argillite. Kukersite has been predominantly mined because of its higher calorific value (9–11 MJ/kg). On the other hand, graptolite argillite has a lower calorific value (4.2–6.7 MJ/kg); consequently it has not been mined as a source of fuel. However, graptolite argillite is characterized by significant amounts of U, Mo, Pb and other metals; thus can be treated as metal ore and two-fold energy source including U and shale oil. Sequel to the identified objectives, spatial analysis tools in ArcGIS have been used to integrate geologic, geochemical and environmental information to evaluate, better understand and spatially analyse the graptolite argillite in northern Estonia. A relatively easy pancake geology as induced from the bedrock geological map of Estonia indicates more or less even pattern of resource distribution but spatial analysis of available well data revealed that relatively high contents of microelements: U (>200 ppm), Pb (> 200 ppm), Mo (> 300 ppm), Zn (> 200 ppm), V (> 1000 ppm), Th (> 11 ppm) and Ag (> 0.7 ppm) occur in northeastern Estonia. More so, thickness of graptolitic argillite ranges from 0–2 m in northeastern Estonia and reaches up to 8 m in the northwestern area. Depth of the upper and lower surfaces of graptolitic argillite in northern Estonia increases from the northeastern area with values from 0.1 m and 0.4 m, respectively to the northwestern area where it reaches 266.2 m. From the results of cost-benefit analysis, it can be adduced that the microelements in graptolitic argillite hold highly scientific as well as significant economic value and possibly form future energy/ore resources.
This study assessed possible application of the friable sand model for shallow mechanically compacted overconsolidated sands from experientially derived velocity and porosity relations of six brine saturated sand aggregates and three published sand compaction datasets. The results showed that the friable sand model established with the assumption of the sediments normally consolidated can also be used for overconsolidated sands caused by stress released as long as the pore pressure is hydrostatic during unloading. We also found that the friable sand model used for overconsolidated sands not only describes change in depositional sorting but also variation in preconsolidation stress related to the amount of uplift. The study outcomes expand the rock physics diagnostic approach to predict seismic properties of shallow overconsolidated sands that have undergone complex burial history (i.e., loading-unloading-reloading) in uplifted basins like the Barents Sea.
AlTheyab, Abdullah (King Abdullah University of Science and Technology) | Wang, Xin (King Abdullah University of Science and Technology) | Schuster, Gerard T. (King Abdullah University of Science and Technology)
We apply the incomplete Gauss-Newton full-waveform inversion (TDIGN-FWI) to Gulf of Mexico (GOM) data in the space-time domain. In our application, iterative least-squares reverse-time migration (LSRTM) is used to estimate the model update at each non-linear iteration, and the number of LSRTM iterations is progressively increased after each non-linear iteration. With this method, model updating along deep reflection wavepaths are automatically enhanced, which in turn improves imaging below the reach of diving-waves. The forward and adjoint operators are implemented in the space-time domain to simultaneously invert the data over a range of frequencies. A multiscale approach is used where higher frequencies are down-weighted significantly at early iterations, and gradually included in the inversion.Synthetic data results demonstrate the effectiveness of reconstructing both the high- and low-wavenumber features in the model without relying on diving waves in the inversion. Results with Gulf of Mexico field data show a significantly improved migration image in both the shallow and deep sections.
We describe a modification of the design criteria normally adopted for Offset Vector Tiles, so that they can be used more effectively with converted-wave data. Our modification is based on the asymptotic conversion point correction and is a function of the Vp/Vs ratio.
There are a number of potential applications of this approach in converted-wave processing. Here, we demonstrate the improvement that results in converted-wave prestack time migration of single offset vector tiles using this PS Offset Vector Tile design.
The ultimate goal for seismic depth imaging is to find new hydrocarbon prospects or improve existing ones. High quality seismic data and an accurate velocity model are the main drivers for good imaging. Prospects may be identified on seismic data in areas which can range from high to low signal. Roughly speaking, prospects generated for shallow targets will be on good signal data and prospects for deeper targets will be in low signal areas, with the signal strength somewhere in between for the mid-range depths.
Different imaging approaches, including migration algorithms and velocity model building techniques, are needed for the different situations of varying geology and signal-to-noise ratios.In areas of high signal, tomography is useful for refining the velocities for three-way (e.g. fault traps) or four-way depth closures. Where signal-to-noise is lower, more modern tools will be required.
Areas of medium signal might include salt overhangs and fold-and-thrust belts, where we might want to define closure under a high angle thrust fault. These areas could benefit from Reverse Time Migration (RTM) based Delayed Imaging Time (DIT) scans. For deeper targets where the signal is often low, efficient RTM layer stripping can be very effective for improving the imaging of plays below salt or beneath a detachment or unconformity.
We are presenting a case study showing improvement in the overall imaging in terms of fault closure, subsalt sediments truncating against the salt flanks, and better focusing around the salt overhang in an area of the Gulf of Mexico (GOM). This paper will demonstrate the benefit of tomography for sediment velocity model building and updating for depth imaging, along with the improvements gained by using RTM based DIT scans and layer stripping RTM.
Kirchhoff migration is still the most popular imaging algorithm because it easily handles irregular geometries that occur due permitting limitations and surface obstacles. However, if not properly interpolated, irregular geometries also hinder proper amplitude preservation. Preconditioned least-squares migration (PLSM) offers an alternative to 5D interpolation and provides an approximation to Earth's reflectivity that resembles the inverse of seismic modeling. PLSM can also be viewed as an interpolation method based on the subsurface velocity-depth model. The impact of PSLM on final stacked results is minimal, with the main advantage being clean prestack gathers for subsequent AVO and AVAz analysis. Other potential uses of PLSM include regularization of data prior to RTM, as well as a component of diffraction imaging. The main disadvantage of the method is the increase in computational time by a factor of 6-12.
Tomographic full waveform inversion (TFWI) provides a framework to invert the seismic data that is immune to cycle-skipping problems. This is achieved by extending the wave equation and adding an offset axis to the velocity model. However, this extension makes the propagation considerably more expensive because each multiplication by velocity becomes a convolution. We provide an alternative formulation which computes the backscattering and the forward scattering components of the gradient separately. To maintain high resolution results of TFWI, the two components of the gradient are first mixed and then separated based on a Fourier domain scale separation. This formulation is based on the Born approximation where the medium parameters are broken into a long-wavelength and short-wavelength components. The inversion setup includes two steps that maintain the high resolution results of TFWI. First, the linearized residual are updated in a nested inversion scheme. This step corrects for the underlying assumption that the data contain primaries only without multiples. Second, the two components of the gradient are first mixed and then separated based on a Fourier domain scale separation to allow for a fully simultaneous inversion of model scales. After deriving the equations, we test the theory with two synthetic examples. The results of both the Marmousi and BP models show that convergence is possible even with large errors in the initial model that would have prevented convergence to conventional FWI.