ALBERT

Description: Geohazards have a direct impact on the drilling and completion of wells; they present safety risks and are costly. They can be caused by formation properties such as overpressure and can be associated with geological structures such as faults and salt bodies. Critical to drilling success, seismic data provide information used to construct an Earth model consisting of 3D structural depth images showing geological targets or hazards and formation properties relevant to drilling such as pore pressure. However, predrill estimates of structural depth images and formation pressures typically have large uncertainties, which elevate safety concerns and drilling risks and could increase the cost of wells. This risk is especially problematic in challenging environments such as deep water, where rig rates are high and continue to increase.

Description: A series of ocean-bottom cable (OBC) surveys has been conducted in the Bohai Sea in China in recent years to overcome difficulties experienced with streamer surveys in shallow water, such as strong currents, missing near offsets, and obstacles. The main challenges in OBC data imaging include steeply dipping structures, serious multiples in the shallow-water environment, large lateral velocity variations, fault shadow effects, and low signal-to-noise ratio (S/N). To obtain optimal images, advanced processing technologies have been developed and applied to OBC data which involve effective PZ summation and shallow-water demultiple, a high-fidelity beam migration in the wide-azimuth domain, and accurate velocity-model building in 3D tilted-transverse-isotropy (TTI) media. The PZ summation and shallow-water demultiple methods aim to effectively eliminate shallow-water ghosts to achieve broadband seismic data. Furthermore, high-fidelity controlled-beam migration (CBM) and TTI velocity-model updates greatly enhance steep dip imaging, improve S/N, and reduce turnaround time. Through the combination of these technologies, OBC data processing provides high-quality images with well-defined steeply dipping structures to reduce exploration risk in the Bohai area.

Description: 〈span〉〈div〉ABSTRACT〈/div〉The presently available staggered-grid finite-difference (SGFD) schemes for the 3D first-order elastic-wave equation can only achieve high-order spatial accuracy, but they exhibit second-order temporal accuracy. Therefore, the commonly used SGFD methods may suffer from visible temporal dispersion and even instability when relatively large time steps are involved. To increase the temporal accuracy and stability, we have developed a novel time-space-domain high-order SGFD stencil, characterized by (2M)th-order spatial and (2N)th-order temporal accuracies (N〈5), to numerically solve the 3D first-order elastic-wave equation. The core idea of this new stencil is to use a double-pyramid stencil with an operator length parameter N together with the conventional second-order SGFD to approximate the temporal derivatives. At the same time, the spatial derivatives are discretized by the orthogonality stencil with an operator length parameter M. We derive the time-space-domain dispersion relation of this new stencil and determine finite-difference (FD) coefficients using the Taylor-series expansion. In addition, we further optimize the spatial FD coefficients by using a least-squares (LS) algorithm to minimize the time-space-domain dispersion relation. To create accurate and reasonable P-, S-, and converted wavefields, we introduce the 3D wavefield-separation technique into our temporal high-order SGFD schemes. The decoupled P- and S-wavefields are extrapolated by using the P- and S-wave dispersion-relation-based FD coefficients, respectively. Moreover, we design an adaptive variable-length operator scheme, including operators M and N, to reduce the extra computational cost arising from adopting this new stencil. Dispersion and stability analyses indicate that our new methods have higher accuracy and better stability than the conventional ones. Using several 3D modeling examples, we demonstrate that our SGFD schemes can yield greater temporal accuracy on the premise of guaranteeing high-order spatial accuracy. Through effectively combining our new stencil, LS-based optimization, large time step, variable-length operator, and graphic processing unit, the computational efficiency can be significantly improved for the 3D case.〈/span〉

Description: 〈span〉〈div〉ABSTRACT〈/div〉Accurate estimation of shallow subsurface velocity models with complex topography is crucial for statics corrections and imaging deep structures. The correlation-based wave-equation traveltime inversion (CWTI) method is suitable for estimating such shallow subsurface velocity structures. However, the CWTI objective function suffers an inherent resolution-loss problem because the traveltime weighted crosscorrelation misfit does not fall to zero even when the model is perfectly matched. Furthermore, the Born-approximation-based CWTI gradient cannot provide an effective model update during each iteration. To overcome these problems, we have developed a least-squares correlation-based full traveltime inversion (LCFTI) method, in which the least-squares correlation-based objective function was designed to minimize the traveltime weighted difference between the autocorrelation and the crosscorrelation. By incorporating the autocorrelation, LCFTI indicates better convergence and higher resolution than CWTI. The LCFTI model updates are derived using the Rytov approximation to avoid incorrect model updates by emphasizing phase matching. Furthermore, to accurately simulate wave propagation, we use the spectral-element method as the modeling engine, in which the mesh of the complex topography is flexibly represented. Synthetic and field data examples are performed to demonstrate the effectiveness of the proposed method in shallow subsurface velocity reconstruction.〈/span〉

Description: Many natural phenomena, including geologic events and geophysical data, are fundamentally nonstationary. They may exhibit stationarity on a short timescale but eventually alter their behavior in time and space. We developed a 2D $$t\hbox{ - }x$$ adaptive prediction filter (APF) and further extended this to a 3D $$t\hbox{ - }x\hbox{ - }y$$ version for random noise attenuation based on regularized nonstationary autoregression (RNA). Instead of patching, a popular method for handling nonstationarity, we obtained smoothly nonstationary APF coefficients by solving a global regularized least-squares problem. We used shaping regularization to control the smoothness of the coefficients of APF. Three-dimensional space-noncausal $$t\hbox{ - }x\hbox{ - }y$$ APF uses neighboring traces around the target traces in the 3D seismic cube to predict noise-free signal, so it provided more accurate prediction results than the 2D version. In comparison with other denoising methods, such as frequency-space deconvolution, time-space prediction filter, and frequency-space RNA, we tested the feasibility of our method in reducing seismic random noise on three synthetic data sets. Results of applying the proposed method to seismic field data demonstrated that nonstationary $$t\hbox{ - }x\hbox{ - }y$$ APF was effective in practice.

Description: An airborne electromagnetic (AEM) survey often covers hundreds of square kilometers. Huge amounts of survey data make 2D/3D data inversion very difficult. However, due to the compact configurations of AEM systems, the sensitive area for each single survey station is much smaller than the whole survey area, which makes it possible to only invert partial survey data. The sensitive area is called the footprint . Based on "moving-footprint" technology, the entire survey can be divided into subareas and the data are first inverted individually and then combined to form the inversions of the entire survey area, so that the cost for forward and inverse modeling can be vastly reduced. Contrary to previous electromagnetic (EM) footprints defined only for an EM transmitter or for a perfectly conductive earth, we defined the frequency-domain AEM footprint by considering a complete AEM transmitter-receiver system over an earth with limited conductivity. We used the tensor Green’s function to calculate the secondary magnetic field from the induced underground current and evaluate the EM footprint as the volume in which the induced current contributes 90% to the total secondary magnetic field at the EM receiver. Numerical experiments for horizontal coplanar and vertical coaxial coil configurations revealed that among all influence factors on the AEM footprint, the flight altitude was dominant, with a high flight altitude corresponding to a large EM footprint, whereas the transmitting frequency and earth resistivity played a secondary role and in a combined way of induction number, with the low frequency or high earth resistivity (small induction number) corresponding to large EM footprints.

Description: Staggered-grid finite-difference (SFD) methods are widely used in modeling seismic-wave propagation, and the coefficients of finite-difference (FD) operators can be estimated by minimizing dispersion errors using Taylor-series expansion (TE) or optimization. We developed novel optimal time-space-domain SFD schemes for acoustic- and elastic-wave-equation modeling. In our schemes, a fourth-order multiextreme value objective function with respect to FD coefficients was involved. To yield the globally optimal solution with low computational cost, we first used variable substitution to turn our optimization problem into a quadratic convex one and then used least-squares (LS) to derive the optimal SFD coefficients by minimizing the relative error of time-space-domain dispersion relations over a given frequency range. To ensure the robustness of our schemes, a constraint condition was imposed that the dispersion error at each frequency point did not exceed a given threshold. Moreover, the hybrid absorbing boundary condition was applied to remove artificial boundary reflections. We compared our optimal SFD with the conventional, TE-based time-space-domain, and LS-based SFD schemes. Dispersion analysis and numerical simulation results suggested that the new SFD schemes had a smaller numerical dispersion than the other three schemes when the same operator lengths were adopted. In addition, our LS-based time-space-domain SFD can obtain the same modeling accuracy with shorter spatial operator lengths. We also derived the stability condition of our schemes. The experiment results revealed that our new LS-based SFD schemes needed a slightly stricter stability condition.

Description: Earth modeling plays a decisive role in seismic imaging. Presently, methods such as tomography and full-waveform inversion (FWI) are widely used to generate 3D high-resolution velocity models across sedimentary basins. However, given the nonunique nature of the solution, the relative fidelity of these velocity models remains low. Moreover, earth-model building must incorporate a dual strategy. In particular, it must derive a velocity model for migration and yield a series of rock properties such as density, temperature, effective stress, and pore pressure. These large-scale, basin-sized rock properties are used for drilling purposes and as an initial model for small-scale reservoir property inversion. Hence, one must inquire: How can we improve an earth model built at basin-scale using seismic data? Traditional workflows will simply convert seismic-derived velocity into rock properties, thereby propagating the uncertainty without addressing the issue. To alleviate this problem and fully exploit the potential of seismic data, an alternate workflow will be discussed. The new workflow involves using rock physics to link rock properties with velocity, and the physical range of the rock properties is used to constrain velocity when derived from surface seismic data; this way we reinforce the reliability of the final earth model. The application of this new workflow is demonstrated using Gulf of Mexico examples.

Description: A workflow for inverting high-velocity conglomerates by simultaneous joint inversion (SJI) of seismic and nonseismic data was developed during a depth-migration project using land 3D data sets from western China. Large localized velocity variations caused by conglomerates severely limited the effectiveness of ray-based seismic tomography in generating an accurate velocity field. Single-domain inversion indicated different responses of particular lithologies for various nonseismic methods, and well-log crossplots enabled estimation of the relationships between models generated from different geophysical measurements. Using the high-resistivity property of the conglomerates, SJI of seismic first-break and magnetotelluric (MT) data was conducted to estimate spatial thickness and velocity. Combining the relatively low-resolution information derived from the MT data with the higher-resolution seismic image, the distribution of conglomerates could be interpreted with the help of well markers and well-logging horizons. The velocity model then was adjusted iteratively. The model that was updated by using the SJI workflow showed better consistency with borehole data compared with the initial tomographic model. The depth-migrated image created by using the updated model showed more geologically plausible structures both at target level and in the overburden.

Description: Traditionally, diffractions are always regarded as a special type of noise, considering that the mechanism of diffraction wavefields is completely different from that of reflection wavefields. Most routine imaging methods, which incline to take the full wavefield as a reflection wavefield, might lead to blurry images, and many useful signals are probably veiled by the influence of seismic diffractions. However, seismic diffractions carry valuable information related to structures and caved reservoirs, and they might cast new light on special understandings about complex geologies of the subsurface. For different imaging purposes, two complementary aspects make use of seismic diffractions. On one hand, a specularity-weighted stack technology images better sequence strata by suppressing diffractions and other noises. On the other hand, diffractions are extracted from dip-angle gathers with generalized Radon transform and then are imaged separately to reveal more potential drilling targets.