scholarly journals Fluid‐property discrimination with AVO: A Biot‐Gassmann perspective

Geophysics ◽  
2003 ◽  
Vol 68 (1) ◽  
pp. 29-39 ◽  
Author(s):  
Brian H. Russell ◽  
Ken Hedlin ◽  
Fred J. Hilterman ◽  
Lawrence R. Lines
Keyword(s):  
Seismic Data ◽  
Rock Physics ◽  
Well Log ◽  
Eastern Canada ◽  
Shallow Gas ◽  
Shear Moduli ◽  
Seismic Amplitude ◽  
Amplitude Inversion ◽  
Avo Inversion ◽  
Fluid Factor

This analysis draws together basic rock physics, amplitude variations with offset (AVO), and seismic amplitude inversion to discuss how fluid‐factor discrimination can be performed using prestack seismic data. From both Biot and Gassmann theories for porous, fluid‐saturated rocks, a general formula is first derived for fluid‐factor discrimination given that both the P and S impedances are available. In essence, the two impedances are transformed so that they better differentiate between the fluid and rock matrix of the porous medium. This formula provides a more sensitive discriminator of the pore‐fluid saturant than the acoustic impedance and is especially applicable in hard‐rock environments. The formulation can be expressed with either the Lamé constants and density, or the bulk and shear moduli and density. Numerical and well‐log examples illustrate the applicability of this approach. AVO inversion results are then incorporated to show how this method can be implemented using prestack seismic data. Finally, a shallow gas‐sand example from Alberta and a well‐log example from eastern Canada are shown to illustrate the technique.

Colored and linear inversions to relative acoustic impedance

Geophysics ◽  
2019 ◽  
Vol 84 (2) ◽  
pp. N15-N27 ◽  
Author(s):  
Carlos A. M. Assis ◽  
Henrique B. Santos ◽  
Jörg Schleicher
Keyword(s):  
Seismic Data ◽  
Acoustic Impedance ◽  
Computational Cost ◽  
Synthetic Data ◽  
Seismic Source ◽  
Alternative Methods ◽  
Well Log ◽  
Linear Inversion ◽  
Amplitude Inversion ◽  
Band Limited

Acoustic impedance (AI) is a widely used seismic attribute in stratigraphic interpretation. Because of the frequency-band-limited nature of seismic data, seismic amplitude inversion cannot determine AI itself, but it can only provide an estimate of its variations, the relative AI (RAI). We have revisited and compared two alternative methods to transform stacked seismic data into RAI. One is colored inversion (CI), which requires well-log information, and the other is linear inversion (LI), which requires knowledge of the seismic source wavelet. We start by formulating the two approaches in a theoretically comparable manner. This allows us to conclude that both procedures are theoretically equivalent. We proceed to check whether the use of the CI results as the initial solution for LI can improve the RAI estimation. In our experiments, combining CI and LI cannot provide superior RAI results to those produced by each approach applied individually. Then, we analyze the LI performance with two distinct solvers for the associated linear system. Moreover, we investigate the sensitivity of both methods regarding the frequency content present in synthetic data. The numerical tests using the Marmousi2 model demonstrate that the CI and LI techniques can provide an RAI estimate of similar accuracy. A field-data example confirms the analysis using synthetic-data experiments. Our investigations confirm the theoretical and practical similarities of CI and LI regardless of the numerical strategy used in LI. An important result of our tests is that an increase in the low-frequency gap in the data leads to slightly deteriorated CI quality. In this case, LI required more iterations for the conjugate-gradient least-squares solver, but the final results were not much affected. Both methodologies provided interesting RAI profiles compared with well-log data, at low computational cost and with a simple parameterization.

Well log and seismic data analysis using rock physics templates

First Break ◽  
2004 ◽  
Vol 22 (1017) ◽  
Author(s):  
P. A. Avseth ◽  
E. Odegaard
Keyword(s):  
Data Analysis ◽  
Seismic Data ◽  
Rock Physics ◽  
Well Log

scholarly journals Analisis AVO untuk Mengetahui Penyebaran Hidrokarbon Berdasarkan Faktor Fluida (Studi Kasus Lapangan “H” Formasi Talang Akar Cekungan Jawa Barat Utara)

2016 ◽  
Vol 3 (02) ◽  
pp. 209
Author(s):  
Muhammad Nur Handoyo ◽  
Agus Setyawan ◽  
Mualimin Muhammad
Keyword(s):  
Seismic Data ◽  
Class Iii ◽  
Avo Inversion ◽  
Amplitude Versus Offset ◽  
Attribute Analysis ◽  
Fluid Factor ◽  
Inversion Analysis ◽  
Amplitude Versus

<span>Amplitude versus offset (AVO) inversion analysis can be used to determine the spread of <span>hydrocarbons on seismic data. In this study we conducted AVO on reservoir layer Talang <span>Akar’s formation (TAF). AVO inversion results are angle stack, normal incident reflectivity <span>(intercept), gradient and fluid factor. Angle stack attribute analysis showed an AVO anomaly <span>in the reservoir TAF layer, amplitude has increased negative value from near angle stack to far <span>angle stack. The result of crossplot normal incident reflectivity (intercept) with gradient <span>indicates reservoir TAF layer including Class III AVO anomaly. While the analysis of fluid <span>factor attribute has a negative value thus reservoir TAF layer indicates a potential <span>hydrocarbon.</span></span></span></span></span></span></span></span><br /></span>

Seismic reservoir characterization of the Bone Spring and Wolfcamp Formations in the Delaware Basin: Challenges and uncertainty in characterization using rock physics — A case study: Part 2

2020 ◽  
Vol 8 (4) ◽  
pp. T1057-T1069
Author(s):  
Ritesh Kumar Sharma ◽  
Satinder Chopra ◽  
Larry Lines
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Rock Physics ◽  
Well Log ◽  
Data Set ◽  
Frequency Model ◽  
Log Data ◽  
Delaware Basin ◽  
Seismic Reservoir

The discrimination of fluid content and lithology in a reservoir is important because it has a bearing on reservoir development and its management. Among other things, rock-physics analysis is usually carried out to distinguish between the lithology and fluid components of a reservoir by way of estimating the volume of clay, water saturation, and porosity using seismic data. Although these rock-physics parameters are easy to compute for conventional plays, there are many uncertainties in their estimation for unconventional plays, especially where multiple zones need to be characterized simultaneously. We have evaluated such uncertainties with reference to a data set from the Delaware Basin where the Bone Spring, Wolfcamp, Barnett, and Mississippian Formations are the prospective zones. Attempts at seismic reservoir characterization of these formations have been developed in Part 1 of this paper, where the geologic background of the area of study, the preconditioning of prestack seismic data, well-log correlation, accounting for the temporal and lateral variation in the seismic wavelets, and building of robust low-frequency model for prestack simultaneous impedance inversion were determined. We determine the challenges and the uncertainty in the characterization of the Bone Spring, Wolfcamp, Barnett, and Mississippian sections and explain how we overcame those. In the light of these uncertainties, we decide that any deterministic approach for characterization of the target formations of interest may not be appropriate and we build a case for adopting a robust statistical approach. Making use of neutron porosity and density porosity well-log data in the formations of interest, we determine how the type of shale, volume of shale, effective porosity, and lithoclassification can be carried out. Using the available log data, multimineral analysis was also carried out using a nonlinear optimization approach, which lent support to our facies classification. We then extend this exercise to derived seismic attributes for determination of the lithofacies volumes and their probabilities, together with their correlations with the facies information derived from mud log data.

Seismic integrated study for offshore deep- and thin-stratum characterization: A case study from the Enping sag within the Pearl River Mouth Basin

2020 ◽  
Vol 39 (3) ◽  
pp. 176-181
Author(s):  
Jun Liu ◽  
Donghai Liang ◽  
Guangrong Peng ◽  
Xiaomin Ruan ◽  
Yingwei Li ◽  
...  
Keyword(s):  
Seismic Data ◽  
River Mouth ◽  
Rock Physics ◽  
Pearl River ◽  
Pearl River Mouth Basin ◽  
Avo Inversion ◽  
Lithology Prediction ◽  
The Pearl River ◽  
Well Data

In the Enping 17 sag within the Pearl River Mouth Basin in the South China Sea, one wildcat well has been drilled to the Lower Paleogene Enping Formation (FM EP) and partially into the Wenchang Formation (FM WC) for deep formation hydrocarbon exploration. However, no commercial play was discovered. The reasons for this are clear if the petroleum systems modeling is examined. In FM EP, the main reason for failure is due to poor sealing. In FM WC, the failure is due to the lack of a good reservoir for hydrocarbon accumulation. Encountering a 9 m thick reservoir at a depth of 4650 m indicates that braided fluvial delta and lowstand turbidite sandstone may develop in FM WC. With the objective of establishing cap rock in FM EP and reservoir rock in FM WC, and in the absence of sufficient well data, an integrated framework for 3D seismic reservoir characterization of offshore deep and thin layers was developed. The workflow includes seismic data reprocessing, well-log-based rock-physics analysis, seismic structure interpretation, simultaneous amplitude variation with offset (AVO) inversion, 3D lithology prediction, and geologic integrated analysis. We present four key solutions to address four specific challenges in this case study: (1) the application of adaptive deghosting techniques to remove the source and streamer depth-related ghost notches in the seismic data bandwidth and the relative amplitude-preserved bandwidth extension technique to improve the seismic data resolution; (2) a practical rock-physics modeling approach to consider the formation overpressure for pseudoshear sonic log prediction; (3) interactive and synchronized workflow between prestack 3D AVO inversion and seismic processing to predict a 9 m thick layer in FM WC through more than 60 rounds of cyclic tests; and (4) cross validation between seismic qualitative attributes and quantitative inversion results to verify the lithology prediction result under the condition of insufficient well data.

Resolving subtle stratigraphic features using spectral ridges and phase residues

2013 ◽  
Vol 1 (1) ◽  
pp. SA93-SA108 ◽  
Author(s):  
Oswaldo Davogustto ◽  
Marcílio Castro de Matos ◽  
Carlos Cabarcas ◽  
Toan Dao ◽  
Kurt J. Marfurt
Keyword(s):  
Seismic Data ◽  
Thin Layers ◽  
Earth Model ◽  
Well Log ◽  
Anadarko Basin ◽  
Seismic Amplitude ◽  
West Texas ◽  
Amplitude Data ◽  
Using Data ◽  
Extract Information

Seismic interpretation is dependent on the quality and resolution of seismic data. Unfortunately, seismic amplitude data are often insufficient for detailed sequence stratigraphy interpretation. We reviewed a method to derive high-resolution seismic attributes based upon complex continuous wavelet transform pseudodeconvolution (PD) and phase-residue techniques. The PD method is based upon an assumption of a blocky earth model that allowed us to increase the frequency content of seismic data that, for our data, better matched the well log control. The phase-residue technique allowed us to extract information not only from thin layers but also from interference patterns such as unconformities from the seismic amplitude data. Using data from a West Texas carbonate environment, we found out how PD can be used not only to improve the seismic well ties but also to provide sharper sequence terminations. Using data from an Anadarko Basin clastic environment, we discovered how phase residues delineate incised valleys seen on the well logs that are difficult to see on vertical slices through the original seismic amplitude.

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.

Broadband seismic amplitude variation with offset inversion

Geophysics ◽  
2017 ◽  
Vol 82 (3) ◽  
pp. M43-M53 ◽  
Author(s):  
Zhaoyun Zong ◽  
Kun Li ◽  
Xingyao Yin ◽  
Ming Zhu ◽  
Jiayuan Du ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Frequency Domain ◽  
Seismic Data ◽  
Low Frequency ◽  
Well Log ◽  
Complex Frequency ◽  
Elastic Parameters ◽  
Log Data ◽  
Avo Inversion ◽  
Frequency Components

Seismic amplitude variation with offset (AVO) inversion is well-known as a popular and pragmatic tool used for the prediction of elastic parameters in the geosciences. Low frequencies missing from conventional seismic data are conventionally recovered from other geophysical information, such as well-log data, for estimating the absolute rock properties, which results in biased inversion results in cases of complex heterogeneous geologic targets or plays with sparse well-log data, such as marine or deep stratum. Broadband seismic data bring new opportunities to estimate the low-frequency components of the elastic parameters without well-log data. We have developed a novel AVO inversion approach with the Bayesian inference for broadband seismic data. The low-frequency components of the elastic parameters are initially estimated with the proposed broadband AVO inversion approach with the Bayesian inference in the complex frequency domain because seismic inversion in the complex frequency domain is helpful to recover the long-wavelength structures of the elastic models. Gaussian and Cauchy probability distribution density functions are used for the likelihood function and the prior information of model parameters, respectively. The maximum a posteriori probability solution is resolved to estimate the low-frequency components of the elastic parameters in the complex frequency domain. Furthermore, with those low-frequency components as initial models and constraints, the conventional AVO inversion method with the Bayesian inference in the time domain is further implemented to estimate the final absolute elastic parameters. Synthetic and field data examples demonstrate that the proposed AVO inversion in the complex frequency domain is able to predict the low-frequency components of elastic parameters well, and that those low-frequency components set a good foundation for the final estimation of the absolute elastic parameters.

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