Seismic amplitude anomalies and AVO analyses at Mestena Grande Field, Jim Hogg Co., Texas

Geophysics ◽  
1990 ◽  
Vol 55 (8) ◽  
pp. 1015-1025 ◽  
Author(s):  
R. C. Burnett
Keyword(s):  
Middle Eocene ◽  
Real Data ◽  
Field Development ◽  
City Formation ◽  
Seismic Amplitude ◽  
Shear Wave Velocities ◽  
High Impedance ◽  
Amplitude Versus Offset ◽  
Seismic Amplitudes ◽  
Middle Member

Mestena Grande field is located in northeast Jim Hogg Co., Texas. It produces gas and condensate, primarily from the middle member of the Middle Eocene Queen City formation. The producing zone is a deep, thin, high impedance sandstone which generates amplitude anomalies on the stacked data. AVO (amplitude versus offset) analyses were performed to investigate those anomalies and determine if they could aid in field development or exploration along the trend. Modeling the AVO response of a productive well has predicted an amplitude decrease with offset from a high impedance sandstone. However, amplitudes increase with offset on the crest of the field. At Mestena Grande field, three categories of seismic amplitudes correspond with production with only one exception. The first category exhibits strong amplitudes on the stacked data and amplitudes increase with offset. This amplitude category is seen around the best wells in the field. Second are the moderate amplitudes which do not increase with offset that surround the wells producing at moderate rates. The third category is characterized by very weak amplitudes which decrease with offset, occurring near all but one of the dry holes. The disagreement between the results of the modelling and the real data is attributed to the lack of accurate shear wave velocities and the presence of very thin beds.

Exploration application of seismic amplitude analysis in the Krishna-Godavari Basin, east coast of India

2014 ◽  
Vol 2 (4) ◽  
pp. SP5-SP20 ◽  
Author(s):  
Ram Janma Singh
Keyword(s):  
Amplitude Analysis ◽  
Seismic Amplitude ◽  
High Impedance ◽  
Data Phase ◽  
Amplitude Versus Offset ◽  
Bright Spots ◽  
Strong Amplitude ◽  
Very High ◽  
Amplitude Versus ◽  
Godavari Basin

Seismic amplitude anomalies are attractive exploration targets in the Krishna-Godavari Basin offshore India. These bright spots mostly have very high amplitudes, so confident interpretations have been possible. We distinguished between hydrocarbon-bearing sands, water-bearing sands, and high-impedance nonreservoir bodies. Also, we mapped channel architecture and accurately predicted reservoir thickness. Strong amplitude anomalies, prospective seismic character based on an understanding of data phase and polarity, flat spots, and amplitude versus offset have all provided valuable evidence.

Using relative geologic time to constrain CNN-based seismic interpretation and property estimation

Geophysics ◽  
2021 ◽  
pp. 1-44
Author(s):  
Aria Abubakar ◽  
Haibin Di ◽  
Zhun Li
Keyword(s):  
Large Scale ◽  
Three Dimensional ◽  
Seismic Interpretation ◽  
Seismic Survey ◽  
Geologic Time ◽  
Seismic Amplitude ◽  
Property Estimation ◽  
Constrained Learning ◽  
Seismic Amplitudes ◽  
Multiplicative Regularization

Three-dimensional seismic interpretation and property estimation is essential to subsurface mapping and characterization, in which machine learning, particularly supervised convolutional neural network (CNN) has been extensively implemented for improved efficiency and accuracy in the past years. In most seismic applications, however, the amount of available expert annotations is often limited, which raises the risk of overfitting a CNN particularly when only seismic amplitudes are used for learning. In such a case, the trained CNN would have poor generalization capability, causing the interpretation and property results of obvious artifacts, limited lateral consistency and thus restricted application to following interpretation/modeling procedures. This study proposes addressing such an issue by using relative geologic time (RGT), which explicitly preserves the large-scale continuity of seismic patterns, to constrain a seismic interpretation and/or property estimation CNN. Such constrained learning is enforced in twofold: (1) from the perspective of input, the RGT is used as an additional feature channel besides seismic amplitude; and more innovatively (2) the CNN has two output branches, with one for matching the target interpretation or properties and the other for reconstructing the RGT. In addition is the use of multiplicative regularization to facilitate the simultaneous minimization of the target-matching loss and the RGT-reconstruction loss. The performance of such an RGT-constrained CNN is validated by two examples, including facies identification in the Parihaka dataset and property estimation in the F3 Netherlands dataset. Compared to those purely from seismic amplitudes, both the facies and property predictions with using the proposed RGT constraint demonstrate significantly reduced artifacts and improved lateral consistency throughout a seismic survey.

scholarly journals Crosswell Seismic Amplitude-Versus-Offset for Detailed Imaging of Facies and Fluid Distribution within Carbonate Oil Reservoirs

2008 ◽  
Author(s):  
Wayne Pennington ◽  
Mohamed Ibrahim ◽  
Roger Turpening ◽  
Sean Trisch ◽  
Josh Richardson ◽  
...  
Keyword(s):  
Oil Reservoirs ◽  
Fluid Distribution ◽  
Seismic Amplitude ◽  
Amplitude Versus Offset ◽  
Detailed Imaging ◽  
Amplitude Versus

scholarly journals Stochastic reservoir characterization using prestack seismic data

Geophysics ◽  
2004 ◽  
Vol 69 (4) ◽  
pp. 978-993 ◽  
Author(s):  
Jo Eidsvik ◽  
Per Avseth ◽  
Henning Omre ◽  
Tapan Mukerji ◽  
Gary Mavko
Keyword(s):  
Reservoir Characterization ◽  
A Priori ◽  
Rock Physics ◽  
Real Data ◽  
Spatial Coupling ◽  
The North ◽  
Amplitude Versus Offset ◽  
Likelihood Model ◽  
Avo Attributes ◽  
The Impact

Reservoir characterization must be based on information from various sources. Well observations, seismic reflection times, and seismic amplitude versus offset (AVO) attributes are integrated in this study to predict the distribution of the reservoir variables, i.e., facies and fluid filling. The prediction problem is cast in a Bayesian setting. The a priori model includes spatial coupling through Markov random field assumptions and intervariable dependencies through nonlinear relations based on rock physics theory, including Gassmann's relation. The likelihood model relating observations to reservoir variables (including lithology facies and pore fluids) is based on approximations to Zoeppritz equations. The model assumptions are summarized in a Bayesian network illustrating the dependencies between the reservoir variables. The posterior model for the reservoir variables conditioned on the available observations is defined by the a priori and likelihood models. This posterior model is not analytically tractable but can be explored by Markov chain Monte Carlo (MCMC) sampling. Realizations of reservoir variables from the posterior model are used to predict the facies and fluid‐filling distribution in the reservoir. A maximum a posteriori (MAP) criterion is used in this study to predict facies and pore‐fluid distributions. The realizations are also used to present probability maps for the favorable (sand, oil) occurrence in the reservoir. Finally, the impact of seismic AVO attributes—AVO gradient, in particular—is studied. The approach is demonstrated on real data from a turbidite sedimentary system in the North Sea. AVO attributes on the interface between reservoir and cap rock are extracted from 3D seismic AVO data. The AVO gradient is shown to be valuable in reducing the ambiguity between facies and fluids in the prediction.

REDUCING RESOURCE UNCERTAINTY USING SEISMIC AMPLITUDE ANALYSIS ON THE SOUTHERN RANKIN TREND, NORTHWEST AUSTRALIA

1999 ◽  
Vol 39 (1) ◽  
pp. 128 ◽  
Author(s):  
D. Sibley ◽  
F. Herkenhoff ◽  
D. Criddle ◽  
M. McLerie
Keyword(s):  
Seismic Attributes ◽  
3D Seismic ◽  
Amplitude Analysis ◽  
Seismic Amplitude ◽  
Amplitude Versus Offset ◽  
Minimum Number ◽  
Gas Fields ◽  
Resource Uncertainty

Between 1973 and 1996 West Australian Petroleum Pty Limited (WAPET) discovered five major gas fields on the southern Rankin Trend including Spar, West Tryal Rocks, Gorgon, Chrysaor, and Dionysus (collectively termed the Greater Gorgon Resource). Recent discoveries at Chrysaor and Dionysus emphasise the role of subtle 3D seismic attributes in finding hydrocarbons and defining reserves with a minimum number of wells.The Gorgon, Chrysaor, and Dionysus fields were covered by 3D seismic data shot in 1991 and 1995, which led WAPET to discover Chrysaor and later Dionysus. Subsequent to the 3D acquisitions, field reservoirs have been correlated with anomalous seismic events (seismic amplitude and amplitude versus offset) that conform to depth structure. Follow-up work has shown that combining these 3D seismic attributes improves the prediction of wet sands, gas sands, and other lithologies.The resulting understanding and confidence provided by this 3D seismic has driven an aggressive exploration program and defined field reserves at a high confidence level. Results include the recent award of permit area WA-267-P to WAPET and the ongoing studies to begin development of the Greater Gorgon Resource.

A Strategic Gas Field Development Case in Sandstones Using Seismic Amplitudes and Dynamic Characterization

2006 ◽  
Vol 45 (03) ◽  
Author(s):  
J.A. Arevalo-Villagran ◽  
H. Cinco-Ley ◽  
T. Gutierrez-Acosta ◽  
N. Martinez-Romero ◽  
F. Garcia-Hernandez ◽  
...  
Keyword(s):  
Dynamic Characterization ◽  
Gas Field ◽  
Field Development ◽  
Seismic Amplitudes

Ship Shoal Block 91 field, offshore Louisiana: Discovery and development of a stratigraphic field

Geophysics ◽  
1987 ◽  
Vol 52 (11) ◽  
pp. 1466-1472
Author(s):  
Rosanne M. Roux
Keyword(s):  
Development Strategy ◽  
Current Model ◽  
Integrated Approach ◽  
Seismic Amplitude ◽  
Attribute Analysis ◽  
Initial Strategy ◽  
The Development Strategy ◽  
Seismic Amplitudes ◽  
Self Potential ◽  
Complex Channel

The Ship Shoal 91 field is a subtle stratigraphic play which consists of a seismic amplitude whose updip limits do not fit structure. The amplitude corresponds to both gas and oil in the Upper Miocene sand. To attempt to predict pay sand distribution accurately, several geologic models were defined as development drilling progressed. The models utilized self‐potential curve shape, core, and seismic character analyses, combined with computer modeling studies, engineering data, and scanning electron microscope studies. Four geologic models evolved, ranging from a structural trap formed by small faults, to a stratigraphic pinchout of the pay sand, to a channel sand, and finally to a complex channel sand, resulting in the current model: a faulted stratigraphic trap. The development strategy for determining well locations also evolved, due to unexpected well results. The initial strategy was based on the presence of seismic amplitudes, until it became apparent that the amplitude alone is not a reliable hydrocarbon indicator. The revised strategy is based on complex attribute analysis, including the amplitude envelope and the weighted frequency. The discovery and development of this stratigraphic field required thorough integration of geology, geophysics, and engineering disciplines. The Ship Shoal 91 field demonstrates that complex stratigraphic fields are still of exploration interest and can be successfully developed by using an integrated approach.

Bayesian closed-skew Gaussian inversion of seismic AVO data for elastic material properties

Geophysics ◽  
2010 ◽  
Vol 75 (1) ◽  
pp. R1-R11 ◽  
Author(s):  
Omid Karimi ◽  
Henning Omre ◽  
Mohsen Mohammadzadeh
Keyword(s):  
Elastic Material ◽  
Material Properties ◽  
P Wave ◽  
S Wave ◽  
High Dimensions ◽  
Seismic Amplitude ◽  
The North ◽  
Avo Inversion ◽  
Amplitude Versus Offset ◽  
The North Sea

Bayesian closed-skew Gaussian inversion is defined as a generalization of traditional Bayesian Gaussian inversion, which is used frequently in seismic amplitude-versus-offset (AVO) inversion. The new model captures skewness in the variables of interest; hence, the posterior model for log-transformed elastic material properties given seismic AVO data might be a skew probability density function. The model is analytically tractable, and this makes it applicable in high-dimensional 3D inversion problems. Assessment of the posterior models in high dimensions requires numerical approximations, however. The Bayesian closed-skew Gaussian inversion approach has been applied on real elastic material properties from a well in the Sleipner field in the North Sea. A comparison with results from traditional Bayesian Gaussian inversion shows that the mean square error of predictions of P-wave and S-wave velocities are reduced by a factor of two, although somewhat less for density predictions.

Robust elastic nonlinear waveform inversion: Application to real data

Geophysics ◽  
1990 ◽  
Vol 55 (5) ◽  
pp. 527-538 ◽  
Author(s):  
E. Crase ◽  
A. Pica ◽  
M. Noble ◽  
J. McDonald ◽  
A. Tarantola
Keyword(s):  
Waveform Inversion ◽  
Numerical Data ◽  
Real Data ◽  
Gradient Algorithm ◽  
P Waves ◽  
Data Set ◽  
The Real ◽  
Data Inversion ◽  
Avo Inversion ◽  
Amplitude Versus Offset

Nonlinear elastic waveform inversion has advanced to the point where it is now possible to invert real multiple‐shot seismic data. The iterative gradient algorithm that we employ can readily accommodate robust minimization criteria which tend to handle many types of seismic noise (noise bursts, missing traces, etc.) better than the commonly used least‐squares minimization criteria. Although there are many robust criteria from which to choose, we have tested only a few. In particular, the Cauchy criterion and the hyperbolic secant criterion perform very well in both noise‐free and noise‐added inversions of numerical data. Although the real data set, which we invert using the sech criterion, is marine (pressure sources and receivers) and is very much dominated by unconverted P waves, we can, for the most part, resolve the short wavelengths of both P impedance and S impedance. The long wavelengths of velocity (the background) are assumed known. Because we are deriving nearly all impedance information from unconverted P waves in this inversion, data acquisition geometry must have sufficient multiplicity in subsurface coverage and a sufficient range of offsets, just as in amplitude‐versus‐offset (AVO) inversion. However, AVO analysis is implicitly contained in elastic waveform inversion algorithms as part of the elastic wave equation upon which the algorithms are based. Because the real‐data inversion is so large—over 230,000 unknowns (340,000 when density is included) and over 600,000 data values—most statistical analyses of parameter resolution are not feasible. We qualitatively verify the resolution of our results by inverting a numerical data set which has the same acquisition geometry and corresponding long wavelengths of velocity as the real data, but has semirandom perturbations in the short wavelengths of P and S impedance.

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