Bandwidth Of Seismic Data Research Articles

Nonlinear elastic impedance inversion in the complex frequency domain based on an exact reflection coefficient

The low-frequency information is extremely crucial to the stability, reliability, and enhanced resolution of the elastic impedance (EI) inversion. Bandwidth of seismic data has been extended during the development of seismic acquisition technology (especially for the acquisition of low-frequency components). In new exploration areas without sufficient prior geological information and drilling, low-frequency initial models are difficult to be established. In order to overcome the model dependence problem in conventional EI inversion, the Bayesian framework, in the complex frequency domain, is utilized to estimate the low-frequency information. The inverted parameters (EI) and likelihood function are considered to obey the Cauchy and Gaussian probability distribution, respectively. Linear EI equations has two disadvantages: low accuracy of reflection coefficient and poor applicability when the elastic parameters of the upper and lower media vary dramatically (high contrast situations), so we derived a novel nonlinear EI equation in terms of compressional wave (P-wave) impedances, shear wave (S-wave) impedances, and density. Its accuracy is basically consistent with the exact Zoeppritz equation. The P-and S-wave impedances and density can be extracted from the direct inversion algorithm Artificial Neural Network Inversion Tool (ANNIT) and EI data, and the sensitivity analysis of the novel EI equation indicates that the inversion results of impedances will be better than density. The feasibility of this method is verified by the model tests and field data examples.

Acquisition and Processing of Wider Bandwidth Seismic Data in Crystalline Crust: Progress with the Metal Earth Project

The Metal Earth project acquired 927 km of deep seismic reflection profiles from August to November of 2017. Seismic data acquired in this early stage of the Metal Earth project benefited greatly from recent advances in the petroleum sector as well as those in mineral exploration. Vibroseis acquisition with receivers having a 5 Hz response (10 dB down) generated records from a sweep signal starting at 2 Hz, sweeping up to 150 Hz or 200 Hz. Not only does this broadband signal enhance reflections from the deepest to the shallowest crust, but it also helps the use of full waveform inversion (e.g., to mitigate cycle-skipping) and related techniques. Metal Earth regional-scale transects using over 5000 active sensors target mineralizing fluid pathways throughout the crust, whereas higher spatial-resolution reflection and full-waveform surveys target structures at mine camp scales. Because Metal Earth was proposed to map and compare entire Archean ore and geologically similar non-ore systems, regional sections cover the entire crust to the Moho in the Abitibi and Wabigoon greenstone belts of the Superior craton in central Canada. Where the new sections overlap with previous Lithoprobe surveys, a clear improvement in reflector detection and definition is observed. Improvements are here attributed to the increased bandwidth of the signal, better estimates of refraction and reflection velocities used in processing, and especially the pre-stack time migration of the data.

High-resolution land seismic acquisition with Broadsweep

Seismic acquisition techniques have steadily progressed over time to achieve higher productivity and greater data bandwidth. Vibroseis sources with new hydraulics and plate and chassis design (Bagaini, 2008; Wei, 2017) are able to generate sweeps between 1-2 Hz to 300-400 Hz (Wei, 2015). Receivers have graduated from complicated patterns to the single-point omnidirectional receiver. Trace density of surveys has increased to provide higher signal-to-noise ratio and homogeneous azimuthal and offset coverage. Vibroseis is a controllable source and its amplitude and frequency characteristics can be manipulated to shape the signal of the desired amplitude and phase spectrum. This remarkable feature of the Vibroseis source, however, remains underutilized: most of the acquisitions are performed with linear sweeps or sweeps with a very mild degree of non-linearity in the frequency- time domain. Increasing the bandwidth of seismic data has several advantages from reducing ambiguity in statics and velocity models during processing to more reliable seismic-to-well ties and seismic inversion results. The result is better reservoir characterization and reduced exploration risk. High quality data with increased signal-to-noise ratio provides for faster conventional interpretation as well as quantitative interpretation utilizing sophisticated multi-trace attributes, machine learning-based algorithms and azimuthal seismic anisotropy analysis (Curia et al., 2018). We have developed a real-time adaptive Vibroseis acquisition technology utilizing a new controller hardware and software, which takes advantage of real-time data feedback and adjusts sweep parameters to compensate for changing near-surface conditions while delivering broadband seismic data with high signal-tonoise ratio. Electronic sweeps, signals from the inertial mass and baseplate are stored, and can be used during processing to further increase data quality. This autonomous system can be used with any vibrator and without any special hardware modifications. The technology can be tuned for a particular reservoir interval to achieve maximum signal-to-noise ratio and resolution, which is further enhanced in processing. This targeted approach is especially beneficial for tight unconventional reservoirs for time-lapse hydraulic fracturing monitoring.

Nonstationary differential resolution: An algorithm to improve seismic resolution

High-resolution seismic data enables better well ties, structure delineation, stratigraphic mapping, and reservoir characterization. Differential resolution (DR) is a data-driven method to improve seismic resolution, but it can introduce a false spatial amplitude variation in the seismic output due to whole trace normalization. The newly proposed normalization technique decomposes the input seismic trace using a translating Gaussian window and then implements the DR algorithm on each window. The introduced weight factors give the interpreter control of the degree of spectral broadening. These developments enable the algorithm to account for the nonstationary properties of the seismic trace, reduce spurious spatial amplitude variation, and provide broader bandwidth seismic data for detailed analysis. We described the mathematical derivation of the nonstationary differential resolution (NSDR) algorithm and its implementation on synthetic and real seismic data. A comparison of NSDR with the original and DR shows better relative amplitude preservation.

Reservoir Prediction Under Control of Sedimentary Facies

Acoustic impedance (AI) from seismic inversion can indicate rock properties and can be used, when combined with rock physics, to predict reservoir parameters, such as porosity. Solutions to seismic inversion problem are almost nonunique due to the limited bandwidth of seismic data. Additional constraints from well log data and geology are needed to arrive at a reasonable solution. In this paper, sedimentary facies is used to reduce the uncertainty in inversion and rock physics modeling; the results not only agree with seismic data, but also conform to geology. A reservoir prediction method, which incorporates seismic data, well logs, rock physics and sedimentary facies, is proposed. AI was first derived by constrained sparse spike inversion (CSSI) using a sedimentary facies dependent low-frequency model, and then was transformed to reservoir parameters by sequential simulation, statistical rock physics and [Formula: see text]-model. Two numerical experiments using synthetic model and real data indicated that the sedimentary facies information may help to obtain a more reasonable prediction.

Extending the useable bandwidth of seismic data with tensor-guided, frequency-dependent filtering

Improving the bandwidth of seismic data has been an ongoing endeavour in increasing the usability of seismic data in many plays throughout the world. While various broadband acquisition techniques have been recently developed for marine acquisition, the most significant issue for land seismic has been the low signal-to-noise ratio, particularly at the low and high ends of the frequency spectrum. In this paper, we describe a frequency-dependent filtering technique that can significantly increase the available bandwidth of the seismic data. We show a field data example and demonstrate that the enhanced bandwidth seismic data ties with well logs in the area. A variety of methods have been proposed to increase the bandwidth of seismic data that fall broadly into three categories: 1. Inversion-type approaches where some pre-determined information or assumption is used to overcome the inherent non-uniqueness of the inversion solution (Zhang and Castangna, 2011). 2. Methods that use the available bandwidth with high signal-to-noise to extrapolate or ‘predict’ the low and high-frequency components (Smith et al., 2008). 3. Methods that apply some sort of frequency-dependent filtering to the seismic data to improve the signal-to-noise of the high and/or low-frequency components (Whitcome and Hodgson, 2007).

Dual-sensor towed streamer: from concept to fleet-wide technology platform

The launch of the dual-sensor towed streamer technology in 2007 is seen by many in the industry as the most important milestone in marine seismic technology in the last decade. The introduction of the technology triggered a significant interest and demand for broader bandwidth seismic data and increased the industry-wide awareness of the geophysical benefits of such broadband data for both frontier exploration and production monitoring in mature basins. It also resulted in the rapid development of new acquisition and processing technology, both concerning the source and receiver side, as well as changes to seismic vessel design and equipment. The geophysical benefits of broadband data and the availability of up- and down-going wavefields as part of the dual-sensor deghosting methodology are now routinely exploited throughout the entire seismic value chain, including seismic imaging and reservoir characterization. After the first 2D dual-sensor survey in 2007, which was quickly followed by the first 3D acquisition commencing on New Year’s Eve 2008, PGS has steadily converted its seismic fleet from hydrophone-only to dual-sensor streamers. The pace of the technology roll-out has been largely driven by the life-cycle of existing streamer inventory and the equipment needs for newly launched seismic vessels as part of an ongoing fleet renewal process. The fleet-wide roll-out of dual-sensor technology will finally be completed in the 4th quarter of 2015 with the upgrade of the last Ramform vessel. Given the scale and complexity of replacing and industrializing a complete acquisition platform, there have naturally been significant lessons, some of which we will be sharing in this article. We will also discuss some of the acquisition and processing technologies that have been developed and/or adapted in order to fully utilise this new marine seismic technology platform.

Increasing the effective bandwidth of seismic data through sparse-layer inversion: A case study from Mangala field, Rajasthan, India

Mangala field is in the northern part of the onshore Barmer Basin in India. The primary reservoir in the field is the Fatehgarh Formation, deposited in the rifting phase that created the Barmer Basin during the Late Cretaceous to early Paleocene period. The majority of reservoir oil is contained within the upper FM1 member of the Fatehgarh Formation, composed of single-story and multistory stacked, meandering-channel sands. These sands vary in thickness from 3 to 7 m, with net to gross ranging from 18% to 78%. For such a heterogeneous fluvial system, correlation of floodplain shales and fluvial sands poses a major challenge for reservoir characterization when based on well data alone. Conventional 3D seismic data do not resolve the thin FM1 sands. For more accurate reservoir modeling, various techniques, which included sparse-spike inversion and spectral decomposition, were attempted with limited success. Sparse-layer reflectivity inversion performed on 3D stack PSTM seismic data, however, resulted in improved detectability and resolution and has provided a greater understanding of the lateral continuity of these thin fluvial reservoir units. The 7- to 50-Hz bandwidth of the input seismic data increased to 7 to 100 Hz by the inversion process. The improved imaging of channel geometries has enabled geobody mapping and their input into a revised geologic model.

Stochastic spectral inversion for sparse-spike reflectivity by presetting the number of non-zero spikes as a prior sparsity constraint

In this paper, presetting the number of non-zero spikes is applied as a prior sparsity constraint to implement the proposed stochastic spectral inversion for sparse-spike reflectivity. Instead of being set artificially, the preset number of non-zero spikes can be estimated directly by the selected frequency bandwidth of seismic data using the signal sampling and reconstruction theory with finite rate of innovation. Then the spectral equations of sparse-spike reflectivity can be derived in the frequency domain by adding the proposed prior sparsity constraint. Using an iterative algorithm combining very fast simulated annealing with a linear least-squares method, we can solve the spectral equations to obtain both the time locations and the amplitudes of non-zero sparse-spike reflectivity and reconstruct the sparse-spike reflectivity series. Examples using 1D synthetic data show that the proposed prior sparsity constraint can ensure that the inversion results are robust and reliable and maximize the number of the reconstructed true spikes with the minimum number of false spikes. Moreover, statistical results of stochastic spectral inversion using a thin-layer model illustrate that the proposed method has the ability of detecting the thin layers below the tuning thickness. Results using 2D synthetic data for the Marmousi2 model demonstrate that the reconstructed sparse-spike reflectivity section can preserve the lateral continuity of layers and distinguish thin beds in the sparse-layer model. The application of 2D field data confirms the validity of the proposed inversion method.

Broadband seismology in oil and gas exploration and production — Introduction

The last decade has seen considerable success toward solving long-standing exploration and reservoir characterization challenges, such as subsalt reservoir imaging. These advances have been enabled through new acquisition techniques in combination with advances in methods for processing, imaging and inversion of seismic data that is rich in low frequencies. In particular, methods such as reverse-time migration rely on highly accurate velocity models. Deriving such velocity models in turn fundamentally requires the acquisition of low-frequency seismic data. In addition, as is nicely demonstrated in one of the papers in this special section, low frequencies also benefit higher resolution images and enables deeper penetration in the subsurface (ten Kroode et al., 2013). The need for extending the temporal bandwidth of seismic data has therefore become increasingly evident and is a key area for research and development both in industry and academia. In this special section we have included papers both describing marine and land applications. We have included papers that The techniques for providing broadband frequency data in turn follow three different directions. First, seeking to widen the data bandwidth by filling in the notches caused by ghost arrivals, several papers in the special section present techniques for deghosting conventional marine seismic data. Second, aiming to push data acquisition toward …

An application of the autoregressive extrapolation technique to enhance deconvolution results: a 2D marine data example

Interpreting a post-stack seismic section is difficult due to the band-limited nature of the seismic data even post deconvolution. Deconvolution is a process that is universally applied to extend the bandwidth of seismic data. However, deconvolution falls short of this task as low and high frequencies of the deconvolved data are either still missing or contaminated by noise. In this paper we use the autoregressive extrapolation technique to recover these missing frequencies, using the high signal-to-noise ratio (S/N) portions of the spectrum of deconvolved data. I introduce here an algorithm to extend the bandwidth of deconvolved data. This is achieved via an autoregressive extrapolation technique, which has been widely used to replace missing or corrupted samples of data in signal processing. This method is performed in the spectral domain. The spectral band to be extrapolated using autoregressive prediction filters is first selected from the part of the spectrum that has a high signal-to-noise ratio (S/N) and is then extended. As there can be more than one zone of good S/N in the spectrum, the results of prediction filter design and extrapolation from three different bands are averaged. When the spectrum of deconvolved data is extended in this way, the results show higher vertical resolution to a degree that the final seismic data closely resemble what is considered to be a reflectivity sequence of the layered medium. This helps to obtain acoustic impedance with inversion by stable integration. The results show that autoregressive spectral extrapolation highly increases vertical resolution and improves horizon tracking to determine continuities and faults. This increase in coherence ultimately yields a more interpretable seismic section.

Acquisition Efficiency And Flexibility Of Multichannel Recording

The high cost of seismic surveys and drilling demands that modern seismic interpretation provides an accurate structural and stratigraphic analysis of an area the first time around. An interpreter cannot interpret subtle stratigraphic and small scale structural features he cannot see, thus it is necessary to acquire and to enhance in processing, high resolution, broad band-width seismic data.