Identification of velocity variations in a seismic cube using neural networks

This research allow to infer that from seismic section and well data it is possible to determine velocity anomalies variations in layers with thicknesses below to the seismic resolution using neuronal networks.Applications in Artificial Intelligence - Learning and Neural NetsRed de Universidades con Carreras en Informática (RedUNCI

Identification of velocity variations in a seismic cube using neural networks

This research allow to infer that from seismic section and well data it is possible to determine velocity anomalies variations in layers with thicknesses below to the seismic resolution using neuronal networks.Applications in Artificial Intelligence - Learning and Neural NetsRed de Universidades con Carreras en Informática (RedUNCI

Estimation of Reservoir Porosity Using Seismic Post-Stack Inversion in Lower Indus Basin, Pakistan

Seismic post-stack inversion is one of the best techniques for effective reservoir characterization. This study intends to articulate the application of Model-Based Inversion (MBI) and Probabilistic Neural Networks (PNN) for the identification of reservoir properties i.e. porosity estimation. MBI technique is applied to observe the low impedance zone at the porous reservoir formation. PNN is a geostatistical technique that transforms the impedance volume into porosity volume. Inverted porosity is estimated to observe the spatial distribution of porosity in the Lower Goru sand reservoir beyond the well data control. The result of inverted porosity is compared with that of well-computed porosity. The estimated inverted porosity ranges from 13-13.5% which shows a correlation of 99.63% with the computed porosity of the Rehmat-02 well. The observed low impedance and high porosity cube at the targeted horizon suggest that it could be a probable potential sand channel. Furthermore, the results of seismic post-stack inversion and geostatistical analysis indicate a very good agreement with each other. Hence, the seismic post-stack inversion technique can effectively be applied to estimate the reservoir properties for further prospective zones identification, volumetric estimation and future exploration

Hardrock Seismic Reflection Through Cover: Defining Controls on Mineralization via Reflection Attribute Analysis

This study attempts to modify oil and gas industry seismic processing and interpretation techniques for use in Carlin-type deposit (CTD) gold exploration. Magmatic and deformation overprints on the Nevada carbonate platform-slope setting present challenges in seismic interpretation when compared to conventional seismic data, which is more commonly imaged in petroliferous basins with low levels of deformation. Barrick Gold Corporation provided 2D seismic reflection data for this case study, which assesses the viability of certain seismic practices when applied to hardrock seismic data collected in NE Nevada. Initial seismic interpretations of the pre-stack depth migrated (PSDM) sections located first-order structures and enhanced the geological model. This study uses derivatives of the PSDM, called seismic attributes, in an attempt to improve interpretability. Seismic attributes can reveal structural and stratigraphic features that are not apparent in the conventional PSDM amplitude data. Attribute analysis in this study leverages correlations made from a seismic response database of ~500 petrophysical drill core samples. These petrophysical measurements indicate that the ore zone exhibits a porosity, acoustic impedance, decarbonatization relationship that is distinguishable from unaltered rock. Down-hole geophysical data suggest an even larger contrast between altered and unaltered limestone. Given sufficient data quality, these observations make attribute analysis for detection of CTD alteration viable. An exhaustive calculation of attributes applied to one 2D reflection profile, which transects the Goldrush CTD resource, suggests that energy- and frequency-based attributes best highlight the ore zone, which is expressed as a chaotic zone of reduced amplitude within one 2D profile. RMS amplitude and instantaneous amplitude identify broad zones of low amplitude whereas an average frequency attribute highlights possible high-frequency attenuation effects in the vicinity of the ore-zone. The sweetness and frequency washout attributes combine frequency and amplitude attributes to more effectively highlight the ore zone. However, the erratic response of sweetness and frequency washout suggest that they may be negatively affected by noise. One structural model is also presented, which used the instantaneous phase attribute to better visualize possible thrust faulting

Seismic Facies Classification of an Intraslope Minibasin in The Keathley Canyon, Northern Gulf of Mexico

This work examines several volume attributes extracted from 3D seismic data with the goal of seismic facies classification and lithology prediction in intraslope minibasins. The study area is in the Keathley Canyon protraction (KC), within the middle slope of the Northern Gulf of Mexico (GOM). It lays within the tabular salt and minibasins province downdip of the main Pliocene and Pleistocene deltaic depocenters. Interaction between sedimentation and mobile salt substrate lead to the emergence of many stratigraphic patterns in the intraslope minibasins. Interest in subsalt formations left above salt formations poorly logged. Facies classification using Artificial Neural Network (ANN) was applied in those poorly logged areas. The resultant facies classes were calibrated and used to predict the lithology of the recognized facies patterns in an intraslope minibasin, away from well control. Three types of facies classes were identified: Convergent thinning, convergent baselaping and bypassing. The convergent baselaping are found to be the most sand rich among all other facies

Seismic Facies Classification of an Intraslope Minibasin in The Keathley Canyon, Northern Gulf of Mexico

This work examines several volume attributes extracted from 3D seismic data with the goal of seismic facies classification and lithology prediction in intraslope minibasins. The study area is in the Keathley Canyon protraction (KC), within the middle slope of the Northern Gulf of Mexico (GOM). It lays within the tabular salt and minibasins province downdip of the main Pliocene and Pleistocene deltaic depocenters. Interaction between sedimentation and mobile salt substrate lead to the emergence of many stratigraphic patterns in the intraslope minibasins. Interest in subsalt formations left above salt formations poorly logged. Facies classification using Artificial Neural Network (ANN) was applied in those poorly logged areas. The resultant facies classes were calibrated and used to predict the lithology of the recognized facies patterns in an intraslope minibasin, away from well control. Three types of facies classes were identified: Convergent thinning, convergent baselaping and bypassing. The convergent baselaping are found to be the most sand rich among all other facies

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