The existence of elastic anisotropy in the reservoir is obtained through the equivalent media theory. An isotropic elastic theory fairly explains the reservoir modeling or characterization but its not well explain anisotropic characteristic fairly for reservoir characterization which is extremely challenging without considering a self-consistent theory of effective equivalent media theory. In this research, equivalent media theory has been explained and implemented on a producing well-log data with consistent Vp, Vs, density and other parameters. Instead of using Voigt averaging, equivalent media theory used to estimate the effective stiffness parameters and compare with Thomsen's parameters and finally used effective anisotropy parameters and compare with gamma log. Result shows the effectiveness of equivalent media theory for future application for developing reservoir modeling and characterization.
Lee, Wei Yi (Centre of Subsurface Seismic Imaging, CSI, Universiti Teknologi PETRONAS) | Hamidi, Rosita (Centre of Subsurface Seismic Imaging, CSI, Universiti Teknologi PETRONAS) | Ghosh, Deva (Centre of Subsurface Seismic Imaging, CSI, Universiti Teknologi PETRONAS) | Musa, Mohd Hafiz (Centre of Subsurface Seismic Imaging, CSI, Universiti Teknologi PETRONAS)
Noise is the unwanted energy in a seismic trace opposed to the signals corresponding to reflected energy from the subsurface features. Since it can overlap with the main signals' energy and conceal the geological information, noise attenuation is one of the most important steps in seismic data processing. The most common method is frequency filtering. However, due to its limitations on separating the noise from signals, this method usually results in hurting the signal. Hence, it is important to develop an alternative method that can attenuate the noise without affecting the signal. Filters based on time-frequency analysis of the data can have a better separation of the noise from signal as they maintain the time localization of events while presenting their frequency content simultaneously. One of the recent approaches to time-frequency analysis of signals is the Empirical Wavelet Transform (EWT) which provides adaptive wavelet filter bank for signal analysis. In this paper, a filter is designed based on EWT for random noise attenuation and is applied on both synthetic and real data.
Liu, Changcheng (Centre of Excellence in Subsurface Seismic Imaging & Hydrocarbon Prediction, Universiti Teknologi PETRONAS) | Ghosh, Deva (Centre of Excellence in Subsurface Seismic Imaging & Hydrocarbon Prediction, Universiti Teknologi PETRONAS) | Salim, Ahmed Mohamed Ahmed (Centre of Excellence in Subsurface Seismic Imaging & Hydrocarbon Prediction, Universiti Teknologi PETRONAS) | Chow, Weng Sum (Centre of Excellence in Subsurface Seismic Imaging & Hydrocarbon Prediction, Universiti Teknologi PETRONAS)
Hydrocarbon prediction using the rock physical parameters is a common technique in the oil and gas industry. However, the rock physical parameters are controlled by porosity, the volume of clay, pore-filled fluid type and lithology simultaneously. Many methods are proposed to predict the existence of hydrocarbon. This paper proposes a new method ΔK which is the difference between the real bulk modulus and the bulk modulus in the brine- substitute case. The algorithm is validated through stochastic numerical modelling. The brines are separated by the ΔK, and the gas can be detected with acceptable accuracy. Furthermore, a model using deep learning approach is trained to predict the ΔK. The trained model is effective that the predicted values using this model have a strong correlation with the original ΔK. The ΔK can be applied to the data which contains Vp, Vs and density using this approach model. In this study, the ΔK is applied to the Marmousi II dataset to examine the performance and yields a good result. The combination of the deep learning and the ΔK improves our ability in hydrocarbon prediction.
The velocity model is of a great importance for geological as well as structural properties of complex structure such as gas cloud. Instead of ray-based techniques, eikonal wavefield tomography can provide a higher resolution velocity model for seismic images. We have implemented first break travel time tomography to enhance the initial velocity model for seismic full waveform inversion (FWI) for better imaging rather than guess initial velocity model for FWI. The First-break travel time concept is based on the eikonal equation, relies on inversion to resolve the complex gas cloud imaging. It allows not only the receivers but the shots to change position along the ray path. Tomography results are useful particularly significant in the presence of noise, scattering in the data. We have implemented this approach on marmousi as well as gas cloud model and output are used as input velocity model for FWI and results of proposed approach is more robust than the traditional with faster convergence.
Hamidi, Rosita (Centre of Seismic Imaging and Hydrocarbon Prediction, Universiti Teknologi Petronas) | Ghosh, Deva (Centre of Seismic Imaging and Hydrocarbon Prediction, Universiti Teknologi Petronas)
Fault and fracture study has a great importance in hydrocarbon prospect exploration and development. Consequently, there have been lots of efforts to analyze the existence and extent of faults in subsurface layers using different methods and tools available to geoscientists; among which the seismic attributes have been proven to be efficient in detecting areas affected by faults and fractures. Seismic attributes help interpreters to highlight details focusing on the geological features of interest in seismic data. However, there are some limitations in the performance of these tools, as the algorithms are dependent on the seismic survey parameters, quality of the data and its existing patterns, and geology of the study area. Consequently, new strategies and algorithms are needed to improve the information obtained from the calculated attributes.
In this study, fault and fracture damage zone analysis is done on three – dimensional seismic data from Sarawak basin in Malaysia. Commonly used seismic attributes to detect such features including variance, dip – magnitude, curvature, and gradient – magnitude are applied. Next, spectral analysis, as a tool to identify events with different frequency content is used which can detect the patterns related to faulting and fracturing of the subsurface layers. The proposed method in this work is to examine the attributes’ performance on spectrally decomposed seismic cubes to unmask the details present at different frequencies. Accordingly, the seismic attributes are applied on the selected cubes, and the color blended cubes of the outputs are evaluated. As the results show, the new strategy reveals more detailed information that already exist in seismic data but cannot be distinguished because they are concealed in the full band seismic cube. Comparing each pair of conventional vs. spectral assisted attributes shows enhancement of the results (more details and better resolution) in all evaluated seismic attributes with the proposed method.
In field development and production, a reservoir model is a key element in the successful performance of an oil and gas field. Well logs and core data have high vertical resolution whereas seismic data has poorer resolution vertically. However, 3D seismic data has very high horizontal resolution. We will take this into account while proposing the new methodology.
Depending on wells only we can have a hi-res model far higher than what we actually need. However, in between the wells any meaningful extrapolation is flawed. Using geo-statistics through kriging algorithm and variography leads to non-geologic reservoir models. One particular process which is highly undesirable is to throw any valid well info and upscale it such as to fit the seismic. This results in a model that is inherently flawed. The important aspect of the new methodology is to follow up the seismic stochastic inversion together with a suitable rock physics modelling to achieve high resolution (reservoir scale) of different litho-facies discrimination.
In this study first a robust rock physics model is performed for not only shear velocity log prediction at not having shear log wells but also to more appropriate litho-facies differentiation at well locations using seismic elastic properties (AI vs. Vp/Vs). Afterwards, seismic pre-stack stochastic inversion is carried out to populate different types of litho-facies between the wells. Eventually, the distribution map of pay zone facies is resulted.
Moussavi Alashloo, S. Y. (Centre of Seismic Imaging, Universiti Teknologi PETRONAS) | Ghosh, Deva (Centre of Seismic Imaging, Universiti Teknologi PETRONAS) | Bashir, Yasir (Centre of Seismic Imaging, Universiti Teknologi PETRONAS)
Reverse time migration (RTM) is a wavefield-continuation method which is accepted as the best migration method currently available for imaging complicated geology. RTM is defined as a reversal procedure of seismic wave propagation, but, conventional RTM does not formulate this reversal procedure as an inverse problem. This problem can be solved using least-squares migration (LSM). This paper presents developing RTM by utilizing least squares inversion process. A matrix-based least squares RTM (LSRTM) algorithm is studied by employing the generalized diffraction-stack migration method. A simple layered model with an anticline structure, and Marmousi model are used to monitor how LSRTM can improve the imaging of dip reflectors, steep dips and the pinch-out. The LSRTM method succeeded to image the flanks, remove noises and improve the resolution. Inversion process of least squares RTM was more efficient than conventional RTM to enhance the resolution of image, remove the artifacts, and correct the amplitude.
Spectral decomposition has been widely used in seismic interpretation. Many methods are proposed, such as S transform and pseudo Wigner-Ville distribution for spectral decomposition. These two methods are insufficient in the time resolution or clarity. A method which combines matching pursuit and pseudo Wigner-Ville distribution is applied to enhance the time resolution and eliminate cross term interferences. This method decomposes the input signal into a series of wavelets belongs to a preset dictionary, then analyze time-frequency character for each wavelets using pseudo Wigner-Ville distribution and stack the results together. It keeps good time resolution in pseudo Wigner-Ville distribution. Simultaneously, cross term interference is eliminated because matching pursuit transforms the input signal into multiple independent wavelet signals.
This method is applied on the synthetic trace and real seismic section. Its result which has higher resolution and elimination of cross terms interference can be used to indicate the existence of hydrocarbon. Matching pursuit-based pseudo Wigner-Ville distribution has a better performance in spectral decomposition, though it is more time consuming
Presentation Date: Thursday, September 28, 2017
Start Time: 10:10 AM
Presentation Type: ORAL
A new approach in Bayesian stochastic seismic inversion has been developed to accurately invert thin pay bed reservoirs below “so called” Seismic Resolution. Sensitive thin bed “attributes”, e.g. wavelet, tuning and spectral analysis, well log, and locally varying anisotropy (LVA), are applied to constrain the proposed Bayesian seismic inversion. The prior model is generated through a perturbation of apparent reflectivity from seismic data. This process guides the perturbation to capture as much as possible detail information of the properties to be inverted. The LVA is incorporated to impose the spatial continuity of the inverted parameters. The low frequency model is built from well log data, and included iteratively to the prior model through a process of frequency matching. The misfit between modelled and observed data is controlled by an energy “spectral attribute” to ensure better resolution. Finally, Markov Chain Monte Carlo method is employed to conduct the simulation. Therefore the minimum inversion biasness is ensured and better uncertainty assessment is provided. This proposed stochastic inversion is applied to an offshore field in Malaysia and successfully inverts thin 5 m gas sand buried at the depth of 1100 m. This thin reservoir was identified on well log but was not detected a) either on the high resolution 3D seismic or b) after deterministic Simultaneous Inversion. This breakthrough will open a new avenue while exploring and developing thin stacked pays very common in the Malaysian and South East Asia offshore Basins. Constraints and high quality in modern seismic data ensure our success.
Presentation Date: Wednesday, September 27, 2017
Start Time: 9:20 AM
Presentation Type: ORAL
Small scale geologic discontinuities are not easy to detect and image in the seismic data, as these features represent themselves as diffracted waves which are different from reflected waves. Using two different data examples, one simple model and one complex model, we illustrate the accuracy of separating diffraction by Plane-wave destruction (PWD) and Dip frequency filtering (DFF) on synthetic data set. In plane-wave destruction, our criteria is to calculate the smoothness and continuity of local events slopes that correspond to a reflection event, and the dip frequency filtering criteria is mainly dependent on the frequency wave number (f-k) of the seismic data. Our example models demonstrate the effectiveness of diffraction separation and possible imaging for high-resolution imaging of minor but significant geologic features.
Presentation Date: Wednesday, September 27, 2017
Start Time: 3:55 PM
Location: Exhibit Hall C, E-P Station 1
Presentation Type: EPOSTER