High-resolution characterization of shallow aquifer by 2D viscoelastic full-waveform inversion of shallow seismic wavefield acquired at the Krauthausen test site

Full-waveform inversion (FWI) method has been proved as an effective tool for high-resolution imaging of the subsurface. We have investigated the potential of shallow seismic-wave 2D viscoelastic FWI as a method in high-resolution hydrogeological near-surface characterization. FWI is applied to two orthogonal profiles acquired at the Krauthausen natural laboratory (Germany). The multiparameter models of viscoelastic FWI (P-and S-wave velocities, attenuation of P- and S-waves, density) show pronounced lateral variations below the profiles. The groundwater table is located at around 2 m, where a sudden P-wave velocity increase occurs. An S-wave low velocity layer exists at the depth of 4-6 m with a high Poisson’s ratio value close to 0.5, which corresponds to a saturated sand layer know from previous studies. A $K$-mean cluster analysis is used to correlate and integrate information contained in the inverted results. By considering the derived Poisson’s ratio, P-wave, and S-wave velocities by FWI, we can convert the complex relationship between the multivariate data into a lithological meaningful zonation of the survey region. By comparing the lithological units in the alluvial aquifer with the cone penetration tests clusters, the face maps provide valuable information about the subsurface heterogeneity and connectivity. This experiment indicates that the multiparameter models derived by viscoelastic FWI contain usefull information for high resolution near-surface aquifer characterization

Application of different classification methods for litho-fluid facies prediction: A case study from the offshore Nile Delta

In this work we test four classification methods for litho-fluid facies identification in a clastic reservoir located in offshore Nile Delta. The ultimate goal of this study is to find an optimal classification method for the area under examination. The geologic context of the investigated area allows us to consider three different facies in the classification: shales, brine sands and gas sands. The depth at which the reservoir zone is located (2300-2700 m) produces a significant overlap of the P- and S-wave impedances of brine sands and gas sands that makes the discrimination between these two litho-fluid classes particularly problematic. The classification is performed on the feature space defined by the elastic properties that are derived from recorded reflection seismic data by means of Amplitude Versus Angle (AVA) Bayesian inversion. As classification methods we test both deterministic and probabilistic approaches: the quadratic discriminant analysis and the neural network methods belong to the first group, whereas the standard Bayesian approach and the Bayesian approach that includes a 1D Markov chain prior model to constrain the vertical continuity of litho-fluid facies, belong to the second group. The capability of each method to discriminate the different facies is evaluated both on synthetic seismic data (computed on the basis of available borehole information) and on field seismic data. The outcomes of each classification method are compared with the known facies profile derived from well log data and the goodness of the results is quantitatively evaluated using the so called confusion matrix. It results that all methods return vertical facies profiles in which the main reservoir zone is correctly identified. However, the consideration of as much prior information as possible in the classification process is the winning choice to derive a reliable and a physically plausible predicted facies profile

Seismic attribute assisted depositional facies identification and stratigraphic correlation in the central Gulf Coast region of Texas

The sandstone deposits in the fluvial-dominated delta systems in the Lower Wilcox Group above the Cretaceous carbonate shelf edges of the Texas Gulf Coast Region are considered as high exploration potential reservoir bodies. However, the multi-phase regression and transgression during the late Paleocene to the early Eocene complicate the local depositional structure. The depositional facies is hard to be identified in details using the traditional seismic stratigraphic interpretation method. Seismic attributes are effective in detecting depositional facies, especially in mapping channels and investigating reservoir characteristics. In this study, we utilize seismic attributes to identify the principal depositional facies in the delta front environment. The major selected attributes are root mean square (RMS) amplitude, instantaneous phase, waveform classification, and spectral decomposition. The stratal slicing technology is utilized to better image the complex depositional systems. The lateral variations of depositional facies inside the canyon-fill sequence are further recognized by stratigraphic correlation from the well log data. After integrating the seismic attributes with stratigraphic correlation, we successfully identify the underwater distributary channels, the channel-margin levee, the debris fan deposits, and the mounded turbidite lobes in the incised canyon system. Identification of such depositional facies provides a significant constraint on predicting the distribution of the reservoir sand. We speculate that the potential reservoirs mainly exist in the vicinity of the canyon mouth, and the sandstones sliding along underwater channels inside the canyon system also have high exploration potential --Abstract, page iv

Exploring the seismic expression of fault zones in 3D seismic volumes

Acknowledgments The seismic interpretation and image processing has been run in the SeisLab facilty at the University of Aberdeen (sponsored by BG, BP and Chevron) Seismic imaging analysis was performed in GeoTeric (ffA), and Mathematica (Wolfram research). Interpretation of seismic amplitudes was performed Petrel 2014 (Schlumberger). We thank Gaynor Paton (Geoteric) for in depth discussion on the facies analysis methodology and significant suggestions to improve the current paper. We thank the New Zealand government (Petroleum and Minerals ministry) and CGG for sharing the seismic dataset utilized in this research paper. Seismic images used here are available through the Virtual Seismic Atlas (www.seismicatlas.org). Nestor Cardozo and an anonymous reviewer are thanked for their constructive comments and suggestions that strongly improved the quality and organization of this paper.Peer reviewedPostprin

Machine Learning for Seismic Exploration: where are we and how far are we from the Holy Grail?

Machine Learning (ML) applications in seismic exploration are growing faster than applications in other industry fields, mainly due to the large amount of acquired data for the exploration industry. The ML algorithms are constantly being implemented to almost all the steps involved in seismic processing and interpretation workflow, mainly for automation, processing time reduction, efficiency and in some cases for improving the results. We carried out a literature-based analysis of existing ML-based seismic processing and interpretation published in SEG and EAGE literature repositories and derived a detailed overview of the main ML thrusts in different seismic applications. For each publication, we extracted various metadata about ML implementations and performances. The data indicate that current ML implementations in seismic exploration are focused on individual tasks rather than a disruptive change in processing and interpretation workflows. The metadata shows that the main targets of ML applications for seismic processing are denoising, velocity model building and first break picking, whereas for seismic interpretation, they are fault detection, lithofacies classification and geo-body identification. Through the metadata available in publications, we obtained indices related to computational power efficiency, data preparation simplicity, real data test rate of the ML model, diversity of ML methods, etc. and we used them to approximate the level of efficiency, effectivity and applicability of the current ML-based seismic processing and interpretation tasks. The indices of ML-based processing tasks show that current ML-based denoising and frequency extrapolation have higher efficiency, whereas ML-based QC is more effective and applicable compared to other processing tasks. Among the interpretation tasks, ML-based impedance inversion shows high efficiency, whereas high effectivity is depicted for fault detection. ML-based Lithofacies classification, stratigraphic sequence identification and petro/rock properties inversion exhibit high applicability among other interpretation tasks