ALBERT

Description: 〈span〉〈div〉ABSTRACT〈/div〉Viscoacoustic migration can significantly compensate for the amplitude loss and phase distortion in migration images computed from highly attenuated data. However, solving the viscoacoustic wave equation requires a significant amount of storage space and computation time, especially for least-squares migration methods. To mitigate this problem, we used acoustic reverse time migration (RTM) instead of viscoacoustic migration to migrate the viscoacoustic data and then we correct the amplitude and phase distortion by hybrid deblurring filters in the image domain. Numerical tests on synthetic and field data demonstrate that acoustic RTM combined with hybrid deblurring filters can compensate for the attenuation effects and produce images with high resolution and balanced amplitudes. This procedure requires less than one-third of the storage space and is O(N−1) times faster compared with the viscoacoustic migration, but at the cost of mildly reduced accuracy. Here, N represents the number of iterations used for least-squares migration method. This method can be extended to 3D migration at even a greater cost saving.〈/span〉

Description: Distributed acoustic sensing (DAS) is a novel technology that uses an optical fiber cable as a sensor for acoustic signals and can take almost any downhole fiber-optic installation or deployment and turn the fiber-optic cable into a large downhole seismic array. This array can provide enhanced vertical seismic profile (VSP) imaging and monitor fluids and pressure changes in the hydrocarbon-production reservoir. Walkaway VSP data acquired over a formerly producing well in northeastern China provided a rich set of high-quality DAS walkaway VSP data. A standard VSP data preprocessing workflow was applied, followed by prestack Kirchhoff time migration. In the DAS preprocessing step, we were faced with additional challenges: strong coherent noise due to cable slapping and ringing along the borehole casing. Compared with an earlier offset VSP data set using 327 levels acquired with conventional 3C downhole geophones in the same well, the final preprocessed DAS walkaway VSP has a larger vertical aperture, resulting in a wider lateral image. The single-well DAS walkaway VSP images provide a good result with higher vertical and lateral resolution than the surface seismic in the objective area. The vertical-well environment, which lacks the ability to effectively "clamp" the sensor to the borehole-casing wall by touching, creates a unique set of challenges. Although earth signal was recorded with almost all the shots, there was also a considerable amount of noise. Much of the noise was due to the physical placement of the wireline in the well and was expressed by slapping and ringing. Reported here are lessons learned in handling the wireline cable and subsequent special DAS data processing steps developed to remediate some of the practical wireline deployment issues. Optical wireline cable as a conveyance of fiber-optic cables for VSP in vertical wells will open the use of the DAS system to wider applications.

Description: The cutoff value of nuclear magnetic resonance (NMR) transversal relaxation time $${T}_{2}$$ is vital for pore structure characterization, permeability prediction, and irreducible water saturation calculation. Conventional default values often lead to inaccurate results for rocks with complex pore structure. Based on NMR experiments and multifractal theory, we have developed an effective statistical method to predict $${T}_{2}$$ cutoff values without other petrophysical information. The method is based on multifractal theory to analyze the NMR $${T}_{2}$$ spectrum with the assumption that the $${T}_{2}$$ spectrum is an indicator of pore size distribution. Multifractal parameters, such as multifractal dimension, singularity strength, and mass exponent, are calculated to investigate the multifractal behavior of $${T}_{2}$$ spectrum via NMR experiments and a dyadic scaling-down algorithm. To obtain the optimal $${T}_{2}$$ cutoff value, the rotation speed and time of centrifugation are enlarged increasingly to optimal centrifugal state. A predicating model for $${T}_{2}$$ cutoff value based on multiple linear regressions of multifractal parameters was proposed after studying the influential factors. On the basis of the multifractal analysis of NMR $${T}_{2}$$ spectrum, a reasonable predication model for $${T}_{2}$$ cutoff value was rendered. Upon testing, the predicted results were highly consistent with the experimental results.

Description: Velocity analysis is an essential step in seismic reflection data processing. The conventional and fastest method to estimate how velocity changes with increasing depth is to calculate semblance coefficients. Traditional semblance has two problems: low time and velocity resolution and an inability to handle amplitude variation-with-offset (AVO) phenomenon. Although a method known as the AB semblance can arrive at peak velocities in the areas with an AVO anomaly, it has a lower velocity resolution than conventional semblance. We have developed a weighted AB semblance method that can handle both problems simultaneously. We have developed two new weighting functions to weight the AB semblance to enhance the resolution of velocity spectra in the time and velocity directions. In this way, we increase the time and velocity resolution while eliminating the AVO problem. The first weighting function is defined based on the ratio between the first and the second singular values of the time window to improve the resolution of velocity spectra in velocity direction. The second weighting function is based on the position of the seismic wavelet in the time window, thus enhancing the resolution of velocity spectra in time direction. We use synthetic and field data examples to show the superior performance of our approach over the traditional one.

Description: Multichannel singular spectrum analysis (MSSA) is an effective algorithm for random noise attenuation; however, it cannot be used to suppress coherent noise. This limitation results from the fact that the conventional MSSA method cannot distinguish between useful signals and coherent noise in the singular spectrum. We have developed a randomization operator to disperse the energy of the coherent noise in the time-space domain. Furthermore, we have developed a novel algorithm for the extraction of useful signals, i.e., for simultaneous random and coherent noise attenuation, by introducing a randomization operator into the conventional MSSA algorithm. In this method, which we call randomized-order MSSA, the traces along the trajectory of each signal component are randomly rearranged. Two ways to extract the trajectories of different signal components are investigated. The first is based on picking the extrema of the upper envelopes, a method that is also constrained by local and global gradients. The second is based on dip scanning in local processing windows, also known as the Radon method. The proposed algorithm can be applied in 2D and 3D data sets to extract different coherent signal components or to attenuate ground roll and multiples. Different synthetic and field data examples demonstrate the successful performance of the proposed method.

Description: Viscoacoustic least-squares reverse time migration, also denoted as Q-LSRTM, linearly inverts for the subsurface reflectivity model from lossy data. Compared with conventional migration methods, it can compensate for the amplitude loss in the migrated images due to strong subsurface attenuation and can produce reflectors that are accurately positioned in depth. However, the adjoint Q propagators used for backward propagating the residual data are also attenuative. Thus, the inverted images from Q -LSRTM with a small number of iterations are often observed to have lower resolution when compared with the benchmark acoustic LSRTM images from acoustic data. To increase the resolution and accelerate the convergence of Q -LSRTM, we used viscoacoustic deblurring filters as a preconditioner for Q -LSRTM. These filters can be estimated by matching a simulated migration image to its reference reflectivity model. Numerical tests on synthetic and field data demonstrate that Q -LSRTM combined with viscoacoustic deblurring filters can produce images with higher resolution and more balanced amplitudes than images from acoustic RTM, acoustic LSRTM, and Q -LSRTM when there is strong attenuation in the background medium. Our preconditioning method is also shown to improve the convergence rate of Q -LSRTM by more than 30% in some cases and significantly compensate for the lossy artifacts in RTM images.

Description: Linear coherent noise attenuation is a troublesome problem in a variety of seismic exploration areas. Traditional methods often use the differences in frequency, wavenumber, or amplitude to separate the useful signal and coherent noise. However, the application of traditional methods is limited or even invalid when the aforementioned differences between useful signal and coherent noise are too small to be distinguished. For this reason, we have managed to develop a new algorithm from the differences in the shape of seismic waves, and thus, introduce mathematical morphological filtering (MMF) into coherent noise attenuation. The morphological operation is calculated in the trace direction of a rotating coordinate system. This rotating coordinate system is along the direction of the trajectory of coherent noise to make the energy of the coherent noise distributed along the horizontal direction. The MMF approach is more effective than mean and median filters in rejecting abnormal values and causes fewer artifacts compared with f - k filtering. Our technique requires that coherent noise can be picked successfully. Application of our technique on synthetic and field seismic data demonstrates its successful performance.

Description: Representation of a signal in a sparse way is a useful and popular methodology in signal-processing applications. Among several widely used sparse transforms, dictionary learning (DL) algorithms achieve most attention due to their ability in making data-driven nonanalytical (nonfixed) atoms. Various DL methods are well-established in seismic data processing due to the inherent low-rank property of this kind of data. We have introduced a novel data-driven 3D DL algorithm that is extended from the 2D nonnegative DL scheme via the multitasking strategy for random noise attenuation of seismic data. In addition to providing parts-based learning, we exploit nonnegativity constraint to induce sparsity on the data transformation and reduce the space of the solution and, consequently, the computational cost. In 3D data, we consider each slice as a task. Whereas 3D seismic data exhibit high correlation between slices, a multitask learning approach is used to enhance the performance of the method by sharing a common sparse coefficient matrix for the whole related tasks of the data. Basically, in the learning process, each task can help other tasks to learn better and thus a sparser representation is obtained. Furthermore, different from other DL methods that use a limited random number of patches to learn a dictionary, the proposed algorithm can take the whole data information into account with a reasonable time cost and thus can obtain an efficient and effective denoising performance. We have applied the method on synthetic and real 3D data, which demonstrated superior performance in random noise attenuation when compared with state-of-the-art denoising methods such as MSSA, BM4D, and FXY predictive filtering, especially in amplitude and continuity preservation in low signal-to-noise ratio cases and fault zones.

Description: The simultaneous-source shooting technique can accelerate field acquisition and improve spatial sampling but it will cause strong interferences in the recorded data and artifacts in the final image. The previously proposed structural smoothing operator can effectively attenuate artifacts for relatively simple reflection structures during least-squares inversion, but it will cause damage to complicated reflection events such as discontinuities. To preserve discontinuities in a seismic image, we apply the singular spectrum analysis (SSA) operator to attenuate artifacts during least-squares inversion. Considering that global SSA cannot deal with overcomplicated data very well, we use local SSA to remove noise and to better preserve the steeply dipping components. The local SSA operator corresponds to a local low-rank constraint applied in the inversion process. The migration operator used in the study is the reverse time migration (RTM) operator. Tests using the Marmousi model showed the superior performance of the proposed algorithm in preserving the discontinuities of seismic images.

Description: Two 4D seismic data sets are compared which were acquired simultaneously in a deepwater field but with differently sized acoustic sources with 2450 in 3 and 360 in 3 volumes. The data sets were processed using similar runstreams, enabling side-by-side comparison of the 4D features. Compared with the large-source data, the small-source data showed similar 4D signals, albeit with higher but acceptable levels of 4D noise. An overprint of the acquisition methodology was found to detrimentally impact the small-source data, but this was mitigated in processing. Opportunities for improvement of the small-source data in future dedicated surveys are proposed. A cost–benefit analysis is presented to show the relative value increase by using smaller, lower-cost surveys for frequent reservoir monitoring.