Nonbright‐spot AVO: Two examples

Geophysics ◽  
1995 ◽  
Vol 60 (5) ◽  
pp. 1398-1408 ◽  
Author(s):  
Christopher P. Ross ◽  
Daniel L. Kinman
Keyword(s):  
Seismic Data ◽  
Acoustic Impedance ◽  
Normal Incidence ◽  
Bright Spot ◽  
Seismic Section ◽  
Amplitude Variation With Offset ◽  
Avo Analysis ◽  
Impedance Contrast ◽  
Seismic Anomalies ◽  
Low Amplitude

The use of amplitude variation with offset (AVO) attribute sections such as the product of the normal incidence trace (A) and the gradient trace (B) have been used extensively in bright spot AVO analysis and interpretation. However, while these sections have often worked well with low acoustic impedance bright spot responses, they are not reliable indicators of nonbright‐spot seismic anomalies. Analyzing nonbright‐spot seismic data with common AVO attribute sections will: (1) not detect the gas‐charged reservoir because of near‐zero acoustic impedance contrast between the sands and encasing shales, or (2) yield an incorrect (negative) AVO product if the normal incidence and gradient values are opposite in sign. We divide nonbright‐spot AVO offset responses into two subcategories: those with phase reversals and those without. An AVO analysis procedure for these anomalies is presented through two examples. The procedure exploits the nature of the prestack response, yielding a more definitive AVO attribute section, and this technique is adaptive to both subcategories of nonbright‐spot AVO responses. This technique identifies the presence of gas‐charged pore fluids within the reservoir when compared to a conventionally processed, relative amplitude seismic section with characteristically low amplitude responses for near‐zero acoustic impedance contrast sands.

DIRECT SEISMIC DETECTION OF HYDROCARBONS

1975 ◽  
Vol 15 (1) ◽  
pp. 81
Author(s):  
W. Pailthorpe ◽  
J. Wardell
Keyword(s):  
Seismic Data ◽  
Random Noise ◽  
Lower Boundary ◽  
The United States ◽  
Bright Spot ◽  
Limiting Factor ◽  
Fluid Interface ◽  
Seismic Section ◽  
Wide Range ◽  
Very High

During the past two years, much publicity has been given to the direct indication of hydrocarbon accumulations by "Bright Spot" reflections: the very high amplitude reflections from a shale to gas-sand or gas-sand to water-sand interface. It was soon generally realised, however, that this phenomenon was of limited occurrence, being mostly restricted to young, shallow, sand and shale sequences such as the United States Gulf Coast. A more widely detectable indication of hydrocarbons was found to be the reflection from a fluid interface, such as the gas to water interface, within the reservoir. This reflection is characterised by its flatness, being a fluid interface, and is often called the "Flat Spot".Model studies show that the flat spots have a wide range of amplitudes, from very high for shallow gas to water contacts, to very low for deep oil to water contacts. However, many of the weaker flat spots on good recent marine seismic data have an adequate signal to random noise ratio for detection, and the problem is to separate and distinguish them from the other stronger reflections close by. In this respect the unique flatness of the fluid contact reflection can be exploited by dip discriminant processes, such as velocity filtering, to separate it from the generally dipping reflectors at its boundaries. A limiting factor in the detection of the deeper flat spots is the frequency bandwidth of the seismic data. Since the separation between the flat spot reflection and the upper and lower boundary reflections of the reservoir is often small, relatively high frequency data are needed to resolve these separate reflections. Correct display of the seismic data can be critical to flat spot detection, and some degree of vertical exaggeration of the seismic section is often required to increase apparent dips, and thus make the flat spots more noticeable.The flat spot is generally a smaller target than the structural features that conventional seismic surveys are designed to find and map, and so a denser than normal grid of seismic lines is required adequately to map most flat spots.

Processing, inversion, and interpretation of a 2D seismic data set from the North Viking Graben, North Sea

Geophysics ◽  
2003 ◽  
Vol 68 (3) ◽  
pp. 837-848 ◽  
Author(s):  
Gislain B. Madiba ◽  
George A. McMechan
Keyword(s):  
Seismic Data ◽  
Dynamic Range ◽  
Velocity Ratio ◽  
Total Porosity ◽  
Shear Velocity ◽  
Seismic Section ◽  
Data Set ◽  
Amplitude Variation With Offset ◽  
Fluid Content ◽  
The North

Simultaneous elastic impedance inversion is performed on the 2D North Viking Graben seismic data set used at the 1994 SEG workshop on amplitude variation with offset and inversion. P‐velocity (Vp), S‐velocity (Vs), density logs, and seismic data are input to the inversion. The inverted P‐impedance and S‐impedance sections are used to generate an approximate compressional‐to‐shear velocity ratio (Vp/Vs) section which, in turn, is used along with water‐filled porosity (Swv) derived from the logs from two wells, to generate fluid estimate sections. This is possible as the reservoir sands have fairly constant total porosity of approximately 28 ± 4%, so the hydrocarbon filled porosity is the total porosity minus the water‐filled porosity. To enhance the separation of lithologies and of fluid content, we map Vp/Vs into Swv using an empirical crossplot‐derived relation. This mapping expands the dynamic range of the low end of the Vp/Vs values. The different lithologies and fluids are generally well separated in the Vp/Vs–Swv domain. Potential hydrocarbon reservoirs (as calibrated by the well data) are identified throughout the seismic section and are consistent with the fluid content estimations obtained from alternative computations. The Vp/Vs–Swv plane still does not produce unique interpretation in many situations. However, the critical distinction, which is between hydrocarbon‐bearing sands and all other geologic/reservoir configurations, is defined. Swv ≤ 0.17 and Vp/Vs ≤ 1.8 are the criteria that delineate potential reservoirs in this area, with decreasing Swv indicating a higher gas/oil ratio, and decreasing Vp/Vs indicating a higher sand/shale ratio. As these criteria are locally calibrated, they appear to be valid locally; they should not be applied to other data sets, which may exhibit significantly different relationships. However, the overall procedure should be generally applicable.

scholarly journals AVO analysis for high amplitude anomalies using 2D pre-stack seismic data

2022 ◽  
Author(s):  
Lamees N. Abdulkareem ◽  
Keyword(s):  
Seismic Data ◽  
High Amplitude ◽  
Rock Properties ◽  
Fluid Substitution ◽  
Amplitude Variation ◽  
Amplitude Variation With Offset ◽  
The Real ◽  
North West ◽  
Avo Analysis ◽  
Controlling Parameter

Amplitude variation with offset (AVO) analysis is an 1 efficient tool for hydrocarbon detection and identification of elastic rock properties and fluid types. It has been applied in the present study using reprocessed pre-stack 2D seismic data (1992, Caulerpa) from north-west of the Bonaparte Basin, Australia. The AVO response along the 2D pre-stack seismic data in the Laminaria High NW shelf of Australia was also investigated. Three hypotheses were suggested to investigate the AVO behaviour of the amplitude anomalies in which three different factors; fluid substitution, porosity and thickness (Wedge model) were tested. The AVO models with the synthetic gathers were analysed using log information to find which of these is the controlling parameter on the AVO analysis. AVO cross plots from the real pre-stack seismic data reveal AVO class IV (showing a negative intercept decreasing with offset). This result matches our modelled result of fluid substitution for the seismic synthetics. It is concluded that fluid substitution is the controlling parameter on the AVO analysis and therefore, the high amplitude anomaly on the seabed and the target horizon 9 is the result of changing the fluid content and the lithology along the target horizons. While changing the porosity has little effect on the amplitude variation with offset within the AVO cross plot. Finally, results from the wedge models show that a small change of thickness causes a change in the amplitude; however, this change in thickness gives a different AVO characteristic and a mismatch with the AVO result of the real 2D pre-stack seismic data. Therefore, a constant thin layer with changing fluids is more likely to be the cause of the high amplitude anomalies.

Mapping lithofacies and pore‐fluid probabilities in a North Sea reservoir: Seismic inversions and statistical rock physics

Geophysics ◽  
2001 ◽  
Vol 66 (4) ◽  
pp. 988-1001 ◽  
Author(s):  
T. Mukerji ◽  
A. Jørstad ◽  
P. Avseth ◽  
G. Mavko ◽  
J. R. Granli
Keyword(s):  
North Sea ◽  
Seismic Data ◽  
Acoustic Impedance ◽  
Probability Distributions ◽  
Rock Physics ◽  
Normal Incidence ◽  
Seismic Inversion ◽  
Training Data ◽  
Well Log ◽  
Statistical Rock Physics

Reliably predicting lithologic and saturation heterogeneities is one of the key problems in reservoir characterization. In this study, we show how statistical rock physics techniques combined with seismic information can be used to classify reservoir lithologies and pore fluids. One of the innovations was to use a seismic impedance attribute (related to the [Formula: see text] ratio) that incorporates far‐offset data, but at the same time can be practically obtained using normal incidence inversion algorithms. The methods were applied to a North Sea turbidite system. We incorporated well log measurements with calibration from core data to estimate the near‐offset and far‐offset reflectivity and impedance attributes. Multivariate probability distributions were estimated from the data to identify the attribute clusters and their separability for different facies and fluid saturations. A training data was set up using Monte Carlo simulations based on the well log—derived probability distributions. Fluid substitution by Gassmann’s equation was used to extend the training data, thus accounting for pore fluid conditions not encountered in the well. Seismic inversion of near‐offset and far‐offset stacks gave us two 3‐D cubes of impedance attributes in the interwell region. The near‐offset stack approximates a zero‐offset section, giving an estimate of the normal incidence acoustic impedance. The far offset stack gives an estimate of a [Formula: see text]‐related elastic impedance attribute that is equivalent to the acoustic impedance for non‐normal incidence. These impedance attributes obtained from seismic inversion were then used with the training probability distribution functions to predict the probability of occurrence of the different lithofacies in the interwell region. Statistical classification techniques, as well as geostatistical indicator simulations were applied on the 3‐D seismic data cube. A Markov‐Bayes technique was used to update the probabilities obtained from the seismic data by taking into account the spatial correlation as estimated from the facies indicator variograms. The final results are spatial 3‐D maps of not only the most likely facies and pore fluids, but also their occurrence probabilities. A key ingredient in this study was the exploitation of physically based seismic‐to‐reservoir property transforms optimally combined with statistical techniques.

Framework for AVO gradient and intercept interpretation

Geophysics ◽  
1998 ◽  
Vol 63 (3) ◽  
pp. 948-956 ◽  
Author(s):  
John P. Castagna ◽  
Herbert W. Swan ◽  
Douglas J. Foster
Keyword(s):  
Reflection Coefficient ◽  
Normal Incidence ◽  
Bright Spot ◽  
Amplitude Variation With Offset ◽  
High Impedance ◽  
Bright Spots ◽  
Fluid Factor ◽  
Amplitude Increase ◽  
Nonuniform Gas ◽  
Background Trend

Amplitude variation with offset (AVO) interpretation may be facilitated by crossplotting the AVO intercept (A) and gradient (B). Under a variety of reasonable petrophysical assumptions, brine‐saturated sandstones and shales follow a well‐defined “background” trend in the A-B plane. Generally, A and B are negatively correlated for “background” rocks, but they may be positively correlated at very high [Formula: see text] ratios, such as may occur in very soft shallow sediments. Thus, even fully brine‐saturated shallow events with large reflection coefficients may exhibit large increases in AVO. Deviations from the background trend may be indicative of hydrocarbons or lithologies with anomalous elastic properties. However, in contrast to the common assumptions that gas‐sand amplitude increases with offset, or that the reflection coefficient becomes more negative with increasing offset, gas sands may exhibit a variety of AVO behaviors. A classification of gas sands based on location in the A-B plane, rather than on normal‐incidence reflection coefficient, is proposed. According to this classification, bright‐spot gas sands fall in quadrant III and have negative AVO intercept and gradient. These sands exhibit the amplitude increase versus offset which has commonly been used as a gas indicator. High‐impedance gas sands fall in quadrant IV and have positive AVO intercept and negative gradient. Consequently, these sands initially exhibit decreasing AVO and may reverse polarity. These behaviors have been previously reported and are addressed adequately by existing classification schemes. However, quadrant II gas sands have negative intercept and positive gradient. Certain “classical” bright spots fall in quadrant II and exhibit decreasing AVO. Examples show that this may occur when the gas‐sand shear‐wave velocity is lower than that of the overlying formation. Common AVO analysis methods such as partial stacks and product (A × B) indicators are complicated by this nonuniform gas‐sand behavior and require prior knowledge of the expected gas‐sand AVO response. However, Smith and Gidlow’s (1987) fluid factor, and related indicators, will theoretically work for gas sands in any quadrant of the A-B plane.

scholarly journals Use of Attributes on 3D Seismic Data for Studying Mishrif Formation in East Abu-Amoud Field, South-Eastern Iraq

2021 ◽  
pp. 4802-4809
Author(s):  
Mohammed H. Al-Aaraji ◽  
Hussein H. Karim
Keyword(s):  
Seismic Data ◽  
Acoustic Impedance ◽  
Seismic Attributes ◽  
3D Seismic ◽  
Seismic Section ◽  
Reflected Waves ◽  
South Eastern ◽  
Seismic Sections ◽  
Sonic Logs ◽  
3D Seismic Data

      The seismic method depends on the nature of the reflected waves from the interfaces between layers, which in turn depends on the density and velocity of the layer, and this is called acoustic impedance. The seismic sections of the East Abu-Amoud field that is located in Missan Province, south-eastern Iraq, were studied and interpreted for updating the structural picture of the major Mishrif Formation for the reservoir in the field. The Mishrif Formation is rich in petroleum in this area, with an area covering about 820 km2. The horizon was calibrated and defined on the seismic section with well logs data (well tops, check shot, sonic logs, and density logs) in the interpretation process to identify the upper and lower boundaries of the Formation.  Seismic attributes were used to study the formation, including instantaneous phase attributes and relative acoustic impedance on time slice of 3D seismic data . Also, relative acoustic impedance was utilized to study the top of the Mishrif Formation. Based on these seismic attributes, karst features of the formation were identified. In addition, the nature of the lithology in the study area and the change in porosity were determined through the relative acoustic impedance The overlap of the top of the Mishrif Formation with the bottom of the Khasib Formation was determined because the Mishrif Formation is considered as an unconformity surface.

A case study showing the value of multioffset synthetic seismograms in seismic data interpretation

2016 ◽  
Vol 4 (4) ◽  
pp. T455-T459 ◽  
Author(s):  
J. Helen Isaac ◽  
Don C. Lawton
Keyword(s):  
Seismic Data ◽  
Upper Cretaceous ◽  
Normal Incidence ◽  
High Amplitude ◽  
Synthetic Seismogram ◽  
Synthetic Seismograms ◽  
Seismic Survey ◽  
Research Station ◽  
S Wave ◽  
Low Amplitude

A baseline 3D3C seismic survey was acquired in May 2014 at a Field Research Station in Southern Alberta, Canada, which is the site of experimental [Formula: see text] injection into an Upper Cretaceous sandstone at approximately 300 m depth. We have created synthetic seismograms from sonic and density logs to identify reflectors seen on the processed seismic data. The high-amplitude positive response (peak) at the top of the Upper Cretaceous Milk River Formation sandstone on the normal incidence PP synthetic seismogram does not match the response seen on the migrated PP seismic data, which is a very low amplitude peak. For such a high impedance, low Poisson’s ratio sandstone, the Zoeppritz equations predict a high-amplitude reflection coefficient at zero offset, then a decrease in amplitude, and even a change in polarity with increasing source-receiver offset. To match the stacked seismic data better, we have created offset synthetic seismograms using P- and S-wave sonic logs and density logs. The character of the top Milk River reflection on the seismic data stacked using all offset traces resembles that observed on the stacked offset synthetic seismogram, which is a similar low-amplitude peak. The character of the top Milk River reflection on the seismic data stacked using only near-offset traces to 250 m looks like that seen on the normal incidence synthetic seismogram.

THE G-LOG* SEISMIC INVERSION PROCESS

1981 ◽  
Vol 21 (1) ◽  
pp. 155
Author(s):  
D. B. Hays ◽  
J. Wardell
Keyword(s):  
Wave Equation ◽  
Seismic Data ◽  
Acoustic Impedance ◽  
Mean Squared Error ◽  
Seismic Inversion ◽  
Synthetic Seismogram ◽  
Inversion Method ◽  
Multiple Reflections ◽  
Seismic Section ◽  
Impedance Model

The G-LOG process is a method of seismic inversion which provides direct estimates of subsurface acoustic impedance from wavelet process stacked or migrated data. The fundamentals and characteristics of the inversion method will be discussed and examples of its use on Australian seismic data will be presented.G-LOG functions are derived by an iterative subsurface modelling technique based on a rigorous inversion of one- dimensional wave equation. This process finds the acoustic impedance model, or log, whose resulting wave-equation- consistent synthetic seismogram best matches the input seismic data in a least mean squared error sense. Multiple reflections are included in the synthetic seismogram, so that they become useful information in the determination of the log.Interval velocity logs are derived from the acoustic impedance logs. The results can be displayed in various forms, including detailed velocity logs, and colour-coded log 'sections' to match with the seismic section. Several examples of such results are presented.The G-LOG process is a revolutionary technique of subsurface modelling, and the logs it provides are strong indicators of subsurface lithology and will be an important tool in the evaluation and re-evaluation of potential hydrocarbon-bearing prospects.*Trademark of G.S.I.

Amplitude variation with offset analysis of nonbright spots for gas channel identification: A case study from the western Sichuan Basin, China

2021 ◽  
Vol 9 (4) ◽  
pp. T1133-T1141
Author(s):  
Feng Tan ◽  
Jun-Xing Cao ◽  
Xing-Jian Wang ◽  
Peng Bai ◽  
Jun Liu ◽  
...  
Keyword(s):  
Seismic Data ◽  
Sichuan Basin ◽  
Bright Spot ◽  
Amplitude Variation ◽  
Gas Field ◽  
Amplitude Variation With Offset ◽  
Western Sichuan ◽  
High Productivity ◽  
Gas Channel ◽  
Wave Trough

The Shaximiao Formation in the Zhongjiang Gas Field of the Sichuan Basin was initially a high-productivity gas field with the bright spot channel as the vital exploration target. With further development, gas wells were obtained in some nonbright spot areas, which caused interpreters to pay great attention to the channels with nonbright spot abnormal amplitudes. We have developed a method to delineate nonbright spot channels from the complicated sand-mudstone contact relationship. First, we classified sandstone into types I, IIa, IIb, and III, depending on the responses of the amplitude variation with offset from the drilled data, to produce a forward model. We the explain why the hidden channel cannot be identified using the full-angle stack seismic data based on this model. Afterward, we put forward a difference, between the synthetic seismogram responses of bright and nonbright channels, in creating seismic-to-well ties for nonbright channels. This difference from bright channels is that the synthetic data’s wave peak is not corresponding to the peak of the real seismic data. The wave trough has the same situation. Finally, we used far-angle stack seismic data to calculate coherent energy and instantaneous spectral attributes (the latter produced for red-green-blue blending) to identify the hidden channel. We observed that parts of the channel are more clearly visible in the far-angle stack than in the full-angle stack data. In the latter situation, we cannot describe the geometric shape of the channel elaborately. The Shaximiao Formation example is a relatively effective analog for nonbright spot plays compared with elsewhere.

Poroelastic analysis of amplitude-versus-frequency variations

Geophysics ◽  
2009 ◽  
Vol 74 (6) ◽  
pp. N41-N48 ◽  
Author(s):  
Haitao Ren ◽  
Gennady Goloshubin ◽  
Fred J. Hilterman
Keyword(s):  
Low Frequency ◽  
Bright Spot ◽  
Frequency Dependent ◽  
Low Frequencies ◽  
Amplitude Variation With Offset ◽  
Seismic Amplitude ◽  
Nondispersive Medium ◽  
Seismic Anomalies ◽  
Frequency Variations ◽  
Lower Frequencies

Although significant advancement has occurred in the interpretation of seismic amplitude-variation-with-offset (AVO) anomalies, a theory is lacking to guide the interpretation of frequency-dependent seismic anomalies. Using analytic equations and numerical modeling, we have investigated characteristics of the normal-incident reflection coefficient (NI) as a function of frequency at an interface between a nondispersive medium and a patchy-saturated dispersive medium. Because of velocity dispersion, the variation of NI magnitude is divided into three general classes. These classes are (1) low-frequency dim-out reservoirs, in which NI magnitude decreases toward lower frequencies; (2) phase-shift reservoirs, in which NI is a small negative value at low frequencies but becomes positive at higher frequencies; and (3) low-frequency bright-spot reservoirs, in which NI magnitude increases toward lower frequencies. This classification could provide insight for frequency-dependent seismic interpre-tation.

Sign in / Sign up

Export Citation Format

Share Document