3D structural-orientation vector guided autotracking for weak seismic reflections: A new tool for shale reservoir visualization and interpretation

2018 ◽  
Vol 6 (4) ◽  
pp. SN47-SN56 ◽  
Author(s):  
Haibin Di ◽  
Dengliang Gao ◽  
Ghassan AlRegib
Keyword(s):  
Oil And Gas ◽  
Reservoir Characterization ◽  
Signal To Noise Ratio ◽  
Estimation Algorithm ◽  
3D Seismic ◽  
Data Set ◽  
Structural Orientation ◽  
Weak Reflection ◽  
Orientation Vector ◽  
Shale Reservoirs

Recognizing and tracking weak reflections, which are characterized by low amplitude, low signal-to-noise ratio, and low degree of lateral continuity, is a long-time issue in 3D seismic interpretation and reservoir characterization. The problem is particularly acute with unconventional, fractured shale reservoirs, in which the impedance contrast is low and/or reservoir beds are below the tuning thickness. To improve the performance of interpreting weak reflections associated with shale reservoirs, we have developed a new workflow for weak-reflection tracking guided by a robust structural-orientation vector (SOV) estimation algorithm. The new SOV-guided auto-tracking workflow first uses the reflection orientation at the seed location as a constraint to project the most-likely locations in the neighboring traces, and then locally adjust them to maximally match the target reflection. We verify our workflow through application to a test seismic data set that is typical of routine 3D seismic surveys over shale oil and gas fields. The results demonstrate the improved quality of the resulting horizons compared with the traditional autotracking algorithms. We conclude that this new SOV-guided autotracking workflow can be used to enhance the performance and effectiveness of weak reflection mapping, which should have important implications for improved shale reservoirs visualization and characterization.

Robust vector median filtering with a structure-adaptive implementation

Geophysics ◽  
2020 ◽  
Vol 85 (5) ◽  
pp. V407-V414
Author(s):  
Yanghua Wang ◽  
Xiwu Liu ◽  
Fengxia Gao ◽  
Ying Rao
Keyword(s):  
Seismic Data ◽  
Impulse Noise ◽  
Reservoir Characterization ◽  
Median Filter ◽  
Original Data ◽  
Thin Layers ◽  
3D Seismic ◽  
Data Set ◽  
Vector Median ◽  
3D Seismic Data

The 3D seismic data in the prestack domain are contaminated by impulse noise. We have adopted a robust vector median filter (VMF) for attenuating the impulse noise from 3D seismic data cubes. The proposed filter has two attractive features. First, it is robust; the vector median that is the output of the filter not only has a minimum distance to all input data vectors, but it also has a high similarity to the original data vector. Second, it is structure adaptive; the filter is implemented following the local structure of coherent seismic events. The application of the robust and structure-adaptive VMF is demonstrated using an example data set acquired from an area with strong sedimentary rhythmites composed of steep-dipping thin layers. This robust filter significantly improves the signal-to-noise ratio of seismic data while preserving any discontinuity of reflections and maintaining the fidelity of amplitudes, which will facilitate the reservoir characterization that follows.

Development of a Powerful Data-Analysis Tool Using Nonparametric Smoothing Models To Identify Drillsites in Tight Shale Reservoirs With High Economic Potential

SPE Journal ◽  
2017 ◽  
Vol 23 (03) ◽  
pp. 719-736 ◽  
Author(s):  
Quan Cai ◽  
Wei Yu ◽  
Hwa Chi Liang ◽  
Jenn-Tai Liang ◽  
Suojin Wang ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Predictive Power ◽  
Oil And Gas ◽  
Predictor Variables ◽  
Data Sets ◽  
Analysis Tool ◽  
Data Set ◽  
Data Analysis Tool ◽  
Shale Reservoirs

Summary The oil-and-gas industry is entering an era of “big data” because of the huge number of wells drilled with the rapid development of unconventional oil-and-gas reservoirs during the past decade. The massive amount of data generated presents a great opportunity for the industry to use data-analysis tools to help make informed decisions. The main challenge is the lack of the application of effective and efficient data-analysis tools to analyze and extract useful information for the decision-making process from the enormous amount of data available. In developing tight shale reservoirs, it is critical to have an optimal drilling strategy, thereby minimizing the risk of drilling in areas that would result in low-yield wells. The objective of this study is to develop an effective data-analysis tool capable of dealing with big and complicated data sets to identify hot zones in tight shale reservoirs with the potential to yield highly productive wells. The proposed tool is developed on the basis of nonparametric smoothing models, which are superior to the traditional multiple-linear-regression (MLR) models in both the predictive power and the ability to deal with nonlinear, higher-order variable interactions. This data-analysis tool is capable of handling one response variable and multiple predictor variables. To validate our tool, we used two real data sets—one with 249 tight oil horizontal wells from the Middle Bakken and the other with 2,064 shale gas horizontal wells from the Marcellus Shale. Results from the two case studies revealed that our tool not only can achieve much better predictive power than the traditional MLR models on identifying hot zones in the tight shale reservoirs but also can provide guidance on developing the optimal drilling and completion strategies (e.g., well length and depth, amount of proppant and water injected). By comparing results from the two data sets, we found that our tool can achieve model performance with the big data set (2,064 Marcellus wells) with only four predictor variables that is similar to that with the small data set (249 Bakken wells) with six predictor variables. This implies that, for big data sets, even with a limited number of available predictor variables, our tool can still be very effective in identifying hot zones that would yield highly productive wells. The data sets that we have access to in this study contain very limited completion, geological, and petrophysical information. Results from this study clearly demonstrated that the data-analysis tool is certainly powerful and flexible enough to take advantage of any additional engineering and geology data to allow the operators to gain insights on the impact of these factors on well performance.

AVO inversion using pseudoisotropic elastic properties

2021 ◽  
Vol 40 (1) ◽  
pp. 52-59
Author(s):  
Michinori Asaka
Keyword(s):  
Elastic Properties ◽  
Oil And Gas ◽  
Reservoir Characterization ◽  
Elastic Anisotropy ◽  
Data Set ◽  
Amplitude Variation With Offset ◽  
Avo Inversion ◽  
Characterization Study ◽  
Anisotropy Parameters ◽  
Using Data

Amplitude variation with offset (AVO) inversion of an anisotropic data set is a challenging task. Nonnegligible differences in the anisotropy parameters between the various lithologies make the seismic data AVO response completely different from the isotropic synthetic seismogram. In this case, it is difficult to invert for VP/VS and density consistent with well-log data. AVO inversion using pseudoisotropic elastic properties is a practical solution to this problem. Verification of this method was performed using data from an offshore Western Australia field. It was found that wavelet extraction and density inversion are improved significantly by replacing the isotropic elastic properties with the pseudoisotropic properties. Inverted density shows reasonable quality and therefore can be included in the reservoir characterization study. Postinversion analyses can be performed effectively on the pseudoisotropic elastic properties because crossplot analysis shows the increased separation of different lithofacies due to contrasts in anisotropy parameters. This result could have significant implications for other fields, as shale constitutes most of the overburden in conventional oil and gas fields and often shows strong elastic anisotropy.

scholarly journals Porosity estimation in the Fort Worth Basin constrained by 3D seismic attributes integrated in a sequential Bayesian simulation framework

Geophysics ◽  
2017 ◽  
Vol 82 (4) ◽  
pp. M67-M80 ◽  
Author(s):  
Martin Blouin ◽  
Mickaele Le Ravalec ◽  
Erwan Gloaguen ◽  
Mathilde Adelinet
Keyword(s):  
Oil And Gas ◽  
Reservoir Characterization ◽  
Geophysical Methods ◽  
Seismic Attributes ◽  
Seismic Inversion ◽  
Fort Worth ◽  
3D Seismic ◽  
Reservoir Properties ◽  
Fort Worth Basin ◽  
Porosity Estimation

The accurate inference of reservoir properties such as porosity and permeability is crucial in reservoir characterization for oil and gas exploration and production as well as for other geologic applications. In most cases, direct measurements of those properties are done in wells that provide high vertical resolution but limited lateral coverage. To fill this gap, geophysical methods can often offer data with dense 3D coverage that can serve as proxy for the variable of interest. All the information available can then be integrated using multivariate geostatistical methods to provide stochastic or deterministic estimate of the reservoir properties. Our objective is to generate multiple scenarios of porosity at different scales, considering four formations of the Fort Worth Basin altogether and then restricting the process to the Marble Falls limestones. Under the hypothesis that a statistical relation between 3D seismic attributes and porosity can be inferred from well logs, a Bayesian sequential simulation (BSS) framework proved to be an efficient approach to infer reservoir porosity from an acoustic impedance cube. However, previous BBS approaches only took two variables upscaled at the resolution of the seismic data, which is not suitable for thin-bed reservoirs. We have developed three modified BSS algorithms that better adapt the BSS approach for unconventional reservoir petrophysical properties estimation from deterministic prestack seismic inversion. A methodology that includes a stochastic downscaling procedure is built and one that integrates two secondary downscaled constraints to the porosity estimation process. Results suggest that when working at resolution higher than surface seismic, it is better to execute the workflow for each geologic formation separately.

Research on LIFT Technology for 3D Seismic Denoising in Coalfield

2014 ◽  
Vol 556-562 ◽  
pp. 6328-6331
Author(s):  
Su Zhen Shi ◽  
Yi Chen Zhao ◽  
Li Biao Yang ◽  
Yao Tang ◽  
Juan Li
Keyword(s):  
Signal To Noise Ratio ◽  
High Signal ◽  
3D Seismic ◽  
Signal To Noise ◽  
Denoising Method ◽  
Seismic Processing ◽  
Work Area ◽  
Noise Data ◽  
Noise Ratio ◽  
Effective Signal

The LIFT technology has applied in process of denoising to ensure the imaging precision of minor faults and structure in 3D coalfield seismic processing. The paper focused on the denoising process in two study areas where the LIFT technology is used. The separation of signal and noise is done firstly. Then denoising would be done in the noise data. The Data of weak effective signal that is from the noise data could be blended with the original effective signal to reconstruct the denoising data, so the result which has high signal-to-noise ratio and preserved amplitude is acquired. Thus the fact shows that LIFT is an effective denoising method for 3D seismic in coalfield and could be used widely in other work area.

A Novel Symbol Rate Estimation Algorithm for Phase Modulating Signals in Wireless Communications

2013 ◽  
Vol 443 ◽  
pp. 392-396
Author(s):  
Peng Zhou ◽  
Chi Sheng Li
Keyword(s):  
Signal To Noise Ratio ◽  
Estimation Algorithm ◽  
Frequency Resolution ◽  
Phase Shift Keying ◽  
Phase Jump ◽  
Rate Estimation ◽  
Awgn Channel ◽  
Symbol Rate ◽  
Shift Keying ◽  
Simulation Results

In this paper, we proposed a new symbol rate estimation algorithm for phase shift keying (PSK) and qua drawtube amplitude modulation (QAM) signals in AWGN channel First we constructe a delay-multiplied signal, from which we obtaine the modulated information. Then we calculated the instantaneous autocorrelation of the delay-multiplied signal to pick out the phase jump. To eliminate the restriction of frequency resolution in fast Fourier transform, we performed a Chirp-Z transform to find out the exact spectral line which represente the symbol rate of the signal to be analyzed. Compared with the existing algorithms, it is a simple solution that has a better performance and accuracy in low signal-to-noise-ratio channel conditions. Simulation results show that the probability of relative estimating deviation below 0.1% reaches 100% and the average and standard variance of absolute estimation deviation are at the magnitude of 10-2 when SNR is over 2dB.

scholarly journals A search for transiting planets in the β Pictoris system

2018 ◽  
Vol 615 ◽  
pp. A145 ◽  
Author(s):  
M. Mol Lous ◽  
E. Weenk ◽  
M. A. Kenworthy ◽  
K. Zwintz ◽  
R. Kuschnig
Keyword(s):  
Signal To Noise Ratio ◽  
Large Fraction ◽  
Young Star ◽  
High Signal ◽  
Data Set ◽  
Transiting Planets ◽  
Using Data ◽  
Exoplanet Systems ◽  
Transit Signals

Context. Transiting exoplanets provide an opportunity for the characterization of their atmospheres, and finding the brightest star in the sky with a transiting planet enables high signal-to-noise ratio observations. The Kepler satellite has detected over 365 multiple transiting exoplanet systems, a large fraction of which have nearly coplanar orbits. If one planet is seen to transit the star, then it is likely that other planets in the system will transit the star too. The bright (V = 3.86) star β Pictoris is a nearby young star with a debris disk and gas giant exoplanet, β Pictoris b, in a multi-decade orbit around it. Both the planet’s orbit and disk are almost edge-on to our line of sight. Aims. We carry out a search for any transiting planets in the β Pictoris system with orbits of less than 30 days that are coplanar with the planet β Pictoris b. Methods. We search for a planetary transit using data from the BRITE-Constellation nanosatellite BRITE-Heweliusz, analyzing the photometry using the Box-Fitting Least Squares Algorithm (BLS). The sensitivity of the method is verified by injection of artificial planetary transit signals using the Bad-Ass Transit Model cAlculatioN (BATMAN) code. Results. No planet was found in the BRITE-Constellation data set. We rule out planets larger than 0.6 RJ for periods of less than 5 days, larger than 0.75 RJ for periods of less than 10 days, and larger than 1.05 RJ for periods of less than 20 days.

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