3D Post-stack Seismic Inversion using Global Optimization Techniques: Gulf of Mexico Example

Seismic inversion using a global optimization algorithm is a non-linear, model-driven process. It yields an optimal solution of the cost function – reflectivity/acoustic impedance, when prior information is sparse. The inversion result offers detailed interpretations of thin layers, internal stratigraphy, and lateral continuity and connectivity of sand bodies. This study compared two stable and robust global optimization techniques, Simulated Annealing (SA) and Basis Pursuit Inversion (BPI) as applied to post-stack seismic data from the Gulf of Mexico. Both methods use different routines and constraints to search for the minimum error energy function. Estimation of inversion parameters in SA is rigorous and more reliable because it depends on prior knowledge of subsurface geology. The BPI algorithm is a more robust deterministic process. It was developed as an alternative method to incorporating a priori information. Results for the Gulf of Mexico show that BPI gives a better stratigraphic and structural actualization due to its capacity to delineate layers thinner than the tuning thickness. The SA algorithm generates both absolute and relative impedances, which provide both qualitative and quantitative characterization of thin-bed reservoirs

3D Post-stack Seismic Inversion using Global Optimization Techniques: Gulf of Mexico Example

Seismic inversion using a global optimization algorithm is a non-linear, model-driven process. It yields an optimal solution of the cost function – reflectivity/acoustic impedance, when prior information is sparse. The inversion result offers detailed interpretations of thin layers, internal stratigraphy, and lateral continuity and connectivity of sand bodies. This study compared two stable and robust global optimization techniques, Simulated Annealing (SA) and Basis Pursuit Inversion (BPI) as applied to post-stack seismic data from the Gulf of Mexico. Both methods use different routines and constraints to search for the minimum error energy function. Estimation of inversion parameters in SA is rigorous and more reliable because it depends on prior knowledge of subsurface geology. The BPI algorithm is a more robust deterministic process. It was developed as an alternative method to incorporating a priori information. Results for the Gulf of Mexico show that BPI gives a better stratigraphic and structural actualization due to its capacity to delineate layers thinner than the tuning thickness. The SA algorithm generates both absolute and relative impedances, which provide both qualitative and quantitative characterization of thin-bed reservoirs

Seismic attribute analysis and its application to mapping folds and fractures.

Geometric attributes such as coherence and curvature have been very successful in delineating faults in sedimentary basins. While not a common exploration objective, fractured and faulted basement forms important reservoirs in Venezuela, USA (Southern California), Brazil, Libya, Algeria, Egypt, Russia, and Vietnam (Landes, 1960; Canh, 2008). Because of the absence of stratified, coherent reflectors, illumination of basement faults is more problematic than illumination of faults within the sedimentary column. In order to address these problems, it is important to carefully analyze alternative forms of the 3D seismic data, which in this dissertation will be primarily combinations of one or more seismic attributes, and interpret them within the context of an appropriate structural deformation model. For that purpose, in this research, I concentrate on analyzing structural dip and azimuth, amplitude energy gradients, and a large family of attributes based on curvature to better illuminate fracture `sweet spots' and estimate their density and orientation. I develop and calibrate these attribute and interpretation workflows through application to a complexly folded and faulted, but otherwise typical, geologic target in the Chicontepec Basin of Mexico. I then apply this calibrated workflow to better characterize faults and build fracture models in the granite basement of the Cuu Long Basin, Vietnam, and the granite and rhyolite-metarhyolite basement of Osage County, Oklahoma, USA. In the Cuu Long granite basement, it forms an important unconventional reservoir. In Osage County, we suspect basement control of overlying fractures in the Mississippian chat deposits

Seismic characterisation based on time-frequency spectral analysis

We present high-resolution time-frequency spectral analysis schemes to better resolve seismic images for the purpose of seismic and petroleum reservoir characterisation. Seismic characterisation is based on the physical properties of the Earth's subsurface media, and these properties are represented implicitly by seismic attributes. Because seismic traces originally presented in the time domain are non-stationary signals, for which the properties vary with time, we characterise those signals by obtaining seismic attributes which are also varying with time. Among the widely used attributes are spectral attributes calculated through time-frequency decomposition. Time-frequency spectral decomposition methods are employed to capture variations of a signal within the time-frequency domain. These decomposition methods generate a frequency vector at each time sample, referred to as the spectral component. The computed spectral component enables us to explore the additional frequency dimension which exists jointly with the original time dimension enabling localisation and characterisation of patterns within the seismic section. Conventional time-frequency decomposition methods include the continuous wavelet transform and the Wigner-Ville distribution. These methods suffer from challenges that hinder accurate interpretation when used for seismic interpretation. Continuous wavelet transform aims to decompose signals on a basis of elementary signals which have to be localised in time and frequency, but this method suffers from resolution and localisation limitations in the time-frequency spectrum. In addition to smearing, it often emerges from ill-localisation. The Wigner-Ville distribution distributes the energy of the signal over the two variables time and frequency and results in highly localised signal components. Yet, the method suffers from spurious cross-term interference due to its quadratic nature. This interference is misleading when the spectrum is used for interpretation purposes. For the specific application on seismic data the interference obscures geological features and distorts geophysical details. This thesis focuses on developing high fidelity and high-resolution time-frequency spectral decomposition methods as an extension to the existing conventional methods. These methods are then adopted as means to resolve seismic images for petroleum reservoirs. These methods are validated in terms of physics, robustness, and accurate energy localisation, using an extensive set of synthetic and real data sets including both carbonate and clastic reservoir settings. The novel contributions achieved in this thesis include developing time-frequency analysis algorithms for seismic data, allowing improved interpretation and accurate characterisation of petroleum reservoirs. The first algorithm established in this thesis is the Wigner-Ville distribution (WVD) with an additional masking filter. The standard WVD spectrum has high resolution but suffers the cross-term interference caused by multiple components in the signal. To suppress the cross-term interference, I designed a masking filter based on the spectrum of the smoothed-pseudo WVD (SP-WVD). The original SP-WVD incorporates smoothing filters in both time and frequency directions to suppress the cross-term interference, which reduces the resolution of the time-frequency spectrum. In order to overcome this side-effect, I used the SP-WVD spectrum as a reference to design a masking filter, and apply it to the standard WVD spectrum. Therefore, the mask-filtered WVD (MF-WVD) can preserve the high-resolution feature of the standard WVD while suppressing the cross-term interference as effectively as the SP-WVD. The second developed algorithm in this thesis is the synchrosqueezing wavelet transform (SWT) equipped with a directional filter. A transformation algorithm such as the continuous wavelet transform (CWT) might cause smearing in the time-frequency spectrum, i.e. the lack of localisation. The SWT attempts to improve the localisation of the time-frequency spectrum generated by the CWT. The real part of the complex SWT spectrum, after directional filtering, is capable to resolve the stratigraphic boundaries of thin layers within target reservoirs. In terms of seismic characterisation, I tested the high-resolution spectral results on a complex clastic reservoir interbedded with coal seams from the Ordos basin, northern China. I used the spectral results generated using the MF-WVD method to facilitate the interpretation of the sand distribution within the dataset. In another implementation I used the SWT spectral data results and the original seismic data together as the input to a deep convolutional neural network (dCNN), to track the horizons within a 3D volume. Using these application-based procedures, I have effectively extracted the spatial variation and the thickness of thinly layered sandstone in a coal-bearing reservoir. I also test the algorithm on a carbonate reservoir from the Tarim basin, western China. I used the spectrum generated by the synchrosqueezing wavelet transform equipped with directional filtering to characterise faults, karsts, and direct hydrocarbon indicators within the reservoir. Finally, I investigated pore-pressure prediction in carbonate layers. Pore-pressure variation generates subtle changes in the P-wave velocity of carbonate rocks. This suggests that existing empirical relations capable of predicting pore-pressure in clastic rocks are unsuitable for the prediction in carbonate rocks. I implemented the prediction based on the P-wave velocity and the wavelet transform multi-resolution analysis (WT-MRA). The WT-MRA method can unfold information within the frequency domain via decomposing the P-wave velocity. This enables us to extract and amplify hidden information embedded in the signal. Using Biot's theory, WT-MRA decomposition results can be divided into contributions from the pore-fluid and the rock framework. Therefore, I proposed a pore-pressure prediction model which is based on the pore-fluid contribution, calculated through WT-MRA, to the P-wave velocity.Open Acces

Advanced Techniques for Ground Penetrating Radar Imaging

Ground penetrating radar (GPR) has become one of the key technologies in subsurface sensing and, in general, in non-destructive testing (NDT), since it is able to detect both metallic and nonmetallic targets. GPR for NDT has been successfully introduced in a wide range of sectors, such as mining and geology, glaciology, civil engineering and civil works, archaeology, and security and defense. In recent decades, improvements in georeferencing and positioning systems have enabled the introduction of synthetic aperture radar (SAR) techniques in GPR systems, yielding GPR–SAR systems capable of providing high-resolution microwave images. In parallel, the radiofrequency front-end of GPR systems has been optimized in terms of compactness (e.g., smaller Tx/Rx antennas) and cost. These advances, combined with improvements in autonomous platforms, such as unmanned terrestrial and aerial vehicles, have fostered new fields of application for GPR, where fast and reliable detection capabilities are demanded. In addition, processing techniques have been improved, taking advantage of the research conducted in related fields like inverse scattering and imaging. As a result, novel and robust algorithms have been developed for clutter reduction, automatic target recognition, and efficient processing of large sets of measurements to enable real-time imaging, among others. This Special Issue provides an overview of the state of the art in GPR imaging, focusing on the latest advances from both hardware and software perspectives

Investigating Key Techniques to Leverage the Functionality of Ground/Wall Penetrating Radar

Ground penetrating radar (GPR) has been extensively utilized as a highly efficient and non-destructive testing method for infrastructure evaluation, such as highway rebar detection, bridge decks inspection, asphalt pavement monitoring, underground pipe leakage detection, railroad ballast assessment, etc. The focus of this dissertation is to investigate the key techniques to tackle with GPR signal processing from three perspectives: (1) Removing or suppressing the radar clutter signal; (2) Detecting the underground target or the region of interest (RoI) in the GPR image; (3) Imaging the underground target to eliminate or alleviate the feature distortion and reconstructing the shape of the target with good fidelity. In the first part of this dissertation, a low-rank and sparse representation based approach is designed to remove the clutter produced by rough ground surface reflection for impulse radar. In the second part, Hilbert Transform and 2-D Renyi entropy based statistical analysis is explored to improve RoI detection efficiency and to reduce the computational cost for more sophisticated data post-processing. In the third part, a back-projection imaging algorithm is designed for both ground-coupled and air-coupled multistatic GPR configurations. Since the refraction phenomenon at the air-ground interface is considered and the spatial offsets between the transceiver antennas are compensated in this algorithm, the data points collected by receiver antennas in time domain can be accurately mapped back to the spatial domain and the targets can be imaged in the scene space under testing. Experimental results validate that the proposed three-stage cascade signal processing methodologies can improve the performance of GPR system

SEISMIC ATTRIBUTE ANALYSIS OF UNCONVENTIONAL RESERVOIRS, AND STRATIGRAPHIC PATTERNS

Seismic volumetric attributes have become one of the key components in aiding interpretation and investigation of the hydrocarbon reservoirs. These reservoirs can be either conventional or unconventional. The application of seismic attributes in conventional reservoirs with mapping bright spots, faults, and channels has been quite successful. Now we face challenges in mapping unconventional reservoirs such as shales, tight gas sands, and carbonates as well as igneous reservoir. This dissertation focuses on developing new workflows to map unconventional reservoirs in a qualitative or quantitative fashion using seismic attributes. The unconventional reservoirs under study include shales, carbonates and volcanic build-ups.A common challenge with many unconventional reservoirs is that they have low permeability, such that fractures are critical to economic success. I apply a different workflow measuring azimuthal anisotropy in the Barnett Shale of the Fort Worth Basin after hydraulic fracturing. The resulting anisotropy is not only heterogeneous, but compartmentalized by previous (Pennsylvanian) deformation. I also develop a workflow to correlate production with proximity of the well to curvature lineaments by scanning hypothesized open fractures as a function of azimuth. I calibrate this workflow to a previously studied Mississippian limestone reservoir from Kansas prior to application to the Woodford Shale of the Arkoma Basin. My final study is a volcanic extrusive reservoir from the Songliao Basin, Northeast China. Volcanic are usually avoided, while I hope that this example may serve as an analogue for others.As part of my analysis, I also document the extension of the previous workflow and development of new algorithm related to spectral-decomposition

スパースアレイ・レーダにおける信号処理技術

Tohoku University佐藤源之課

Maximum Likelihood Estimation of Exponentials in Unknown Colored Noise for Target Identification in Synthetic Aperture Radar Images

This dissertation develops techniques for estimating exponential signals in unknown colored noise. The Maximum Likelihood (ML) estimators of the exponential parameters are developed. Techniques are developed for one and two dimensional exponentials, for both the deterministic and stochastic ML model. The techniques are applied to Synthetic Aperture Radar (SAR) data whose point scatterers are modeled as damped exponentials. These estimated scatterer locations (exponentials frequencies) are potential features for model-based target recognition. The estimators developed in this dissertation may be applied with any parametrically modeled noise having a zero mean and a consistent estimator of the noise covariance matrix. ML techniques are developed for a single instance of data in colored noise which is modeled in one dimension as (1) stationary noise, (2) autoregressive (AR) noise and (3) autoregressive moving-average (ARMA) noise and in two dimensions as (1) stationary noise, and (2) white noise driving an exponential filter. The classical ML approach is used to solve for parameters which can be decoupled from the estimation problem. The remaining nonlinear optimization to find the exponential frequencies is then solved by extending white noise ML techniques to colored noise. In the case of deterministic ML, the computationally efficient, one and two-dimensional Iterative Quadratic Maximum Likelihood (IQML) methods are extended to colored noise. In the case of stochastic ML, the one and two-dimensional Method of Direction Estimation (MODE) techniques are extended to colored noise. Simulations show that the techniques perform close to the Cramer-Rao bound when the model matches the observed noise