Estimating elastic properties and attenuation factor from different frequency components of observed seismic data

2019 ◽  
Vol 220 (2) ◽  
pp. 794-805
Author(s):  
Huaizhen Chen
Keyword(s):  
Reflection Coefficient ◽  
Elastic Properties ◽  
Seismic Data ◽  
Wave Attenuation ◽  
Attenuation Factor ◽  
P Wave ◽  
S Wave ◽  
Data Set ◽  
Frequency Dependent ◽  
Frequency Components

SUMMARY Based on an attenuation model, we first express frequency-dependent P- and S-wave attenuation factors as a function of P-wave maximum attenuation factor, and then we re-express P- and S-wave velocities in anelastic media and derive frequency-dependent stiffness parameters in terms of P-wave maximum attenuation factor. Using the derived stiffness parameters, we propose frequency-dependent reflection coefficient in terms of P- and S-wave moduli at critical frequency and P-wave maximum attenuation factor for the case of an interface separating two attenuating media. Based on the derived reflection coefficient, we establish an approach to utilize different frequency components of observed seismic data to estimate elastic properties (P- and S-wave moduli and density) and attenuation factor, and following a Bayesian framework, we construct the objective function and an iterative method is employed to solve the inversion problem. Tests on synthetic data confirm that the proposed approach makes a stable and robust estimation of unknown parameters in the case of seismic data containing a moderate noise/error. Applying the proposed approach to a real data set illustrates that a reliable attenuation factor is obtained from observed seismic data, and the ability of distinguishing oil-bearing reservoirs is improved combining the estimated elastic properties and P-wave attenuation factor.

Inversion of differences in frequency components of azimuthal seismic data for indicators of oil-bearing fractured reservoirs based on an attenuative cracked model

Geophysics ◽  
2020 ◽  
Vol 85 (3) ◽  
pp. R163-R175
Author(s):  
Huaizhen Chen ◽  
Junxiao Li ◽  
Kristopher A. Innanen
Keyword(s):  
Reflection Coefficient ◽  
Stiffness Matrix ◽  
Fractured Reservoirs ◽  
P Wave ◽  
Parameter Estimates ◽  
Fluid Viscosity ◽  
Mathematical Form ◽  
Data Set ◽  
Frequency Dependent ◽  
Frequency Components

Based on a model of attenuative cracked rock, we have derived a simplified and frequency-dependent stiffness matrix associated with (1) a rock volume containing aligned and partially saturated cracks and (2) a new indicator of oil-bearing fractured reservoirs, which is related to pressure relaxation in cracked rocks and influenced by fluid viscosity and saturation. Starting from the mathematical form of a perturbation in this stiffness matrix across a reflecting interface separating two attenuative cracked media, we set up a linearized P-wave to P-wave reflection coefficient as an azimuthally and frequency-dependent function of dry rock elastic properties, dry fracture weaknesses, and the new indicator. By varying this reflection coefficient with azimuthal angle, we derive a further expression referred to as the quasidifference in elastic impedance, or [Formula: see text], which is primarily affected by the dry fracture weaknesses and the new indicator. An inversion approach is established to use differences in frequency components of seismic amplitudes to estimate these weaknesses and the indicator based on the derived [Formula: see text]. In synthetic inversion tests, we determine that the approach produces interpretable parameter estimates in the presence of data with a moderate signal-to-noise ratio (S/N). Testing on a real data set suggests that reliable fracture weakness and indicator are generated by the approach; fractured and oil-bearing reservoirs are identified through a combination of the dry fracture weakness and the new indicator.

Elastic reflectivity vectors and the geometry of intercept-gradient crossplots

2019 ◽  
Vol 38 (10) ◽  
pp. 762-769
Author(s):  
Patrick Connolly
Keyword(s):  
Elastic Property ◽  
Elastic Properties ◽  
Wave Velocity ◽  
Seismic Data ◽  
Measurement Errors ◽  
Rock Physics ◽  
P Wave ◽  
S Wave ◽  
P Wave Velocity ◽  
Well Log Analysis

Reflectivities of elastic properties can be expressed as a sum of the reflectivities of P-wave velocity, S-wave velocity, and density, as can the amplitude-variation-with-offset (AVO) parameters, intercept, gradient, and curvature. This common format allows elastic property reflectivities to be expressed as a sum of AVO parameters. Most AVO studies are conducted using a two-term approximation, so it is helpful to reduce the three-term expressions for elastic reflectivities to two by assuming a relationship between P-wave velocity and density. Reduced to two AVO components, elastic property reflectivities can be represented as vectors on intercept-gradient crossplots. Normalizing the lengths of the vectors allows them to serve as basis vectors such that the position of any point in intercept-gradient space can be inferred directly from changes in elastic properties. This provides a direct link between properties commonly used in rock physics and attributes that can be measured from seismic data. The theory is best exploited by constructing new seismic data sets from combinations of intercept and gradient data at various projection angles. Elastic property reflectivity theory can be transferred to the impedance domain to aid in the analysis of well data to help inform the choice of projection angles. Because of the effects of gradient measurement errors, seismic projection angles are unlikely to be the same as theoretical angles or angles derived from well-log analysis, so seismic data will need to be scanned through a range of angles to find the optimum.

Simultaneous inversion of PP and PS seismic data

Geophysics ◽  
2006 ◽  
Vol 71 (3) ◽  
pp. R1-R10 ◽  
Author(s):  
Helene Hafslund Veire ◽  
Martin Landrø
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Model Building ◽  
Joint Inversion ◽  
P Wave ◽  
Inversion Method ◽  
System Of Equations ◽  
S Wave ◽  
Data Set ◽  
Simultaneous Inversion

Elastic parameters derived from seismic data are valuable input for reservoir characterization because they can be related to lithology and fluid content of the reservoir through empirical relationships. The relationship between physical properties of rocks and fluids and P-wave seismic data is nonunique. This leads to large uncertainties in reservoir models derived from P-wave seismic data. Because S- waves do not propagate through fluids, the combined use of P-and S-wave seismic data might increase our ability to derive fluid and lithology effects from seismic data, reducing the uncertainty in reservoir characterization and thereby improving 3D reservoir model-building. We present a joint inversion method for PP and PS seismic data by solving approximated linear expressions of PP and PS reflection coefficients simultaneously using a least-squares estimation algorithm. The resulting system of equations is solved by singular-value decomposition (SVD). By combining the two independent measurements (PP and PS seismic data), we stabilize the system of equations for PP and PS seismic data separately, leading to more robust parameter estimation. The method does not require any knowledge of PP and PS wavelets. We tested the stability of this joint inversion method on a 1D synthetic data set. We also applied the methodology to North Sea multicomponent field data to identify sand layers in a shallow formation. The identified sand layers from our inverted sections are consistent with observations from nearby well logs.

AVO inversion and poroelasticity with P- and S-wave moduli

Geophysics ◽  
2012 ◽  
Vol 77 (6) ◽  
pp. N17-N24 ◽  
Author(s):  
Zhaoyun Zong ◽  
Xingyao Yin ◽  
Guochen Wu
Keyword(s):  
Seismic Data ◽  
Real Data ◽  
P Wave ◽  
Model Parameters ◽  
Initial Model ◽  
S Wave ◽  
Data Set ◽  
Statistical Probability ◽  
Avo Inversion ◽  
Poroelasticity Theory

The fluid term in the Biot-Gassmann equation plays an important role in reservoir fluid discrimination. The density term imbedded in the fluid term, however, is difficult to estimate because it is less sensitive to seismic amplitude variations. We combined poroelasticity theory, amplitude variation with offset (AVO) inversion, and identification of P- and S-wave moduli to present a stable and physically meaningful method to estimate the fluid term, with no need for density information from prestack seismic data. We used poroelasticity theory to express the fluid term as a function of P- and S-wave moduli. The use of P- and S-wave moduli made the derivation physically meaningful and natural. Then we derived an AVO approximation in terms of these moduli, which can then be directly inverted from seismic data. Furthermore, this practical and robust AVO-inversion technique was developed in a Bayesian framework. The objective was to obtain the maximum a posteriori solution for the P-wave modulus, S-wave modulus, and density. Gaussian and Cauchy distributions were used for the likelihood and a priori probability distributions, respectively. The introduction of a low-frequency constraint and statistical probability information to the objective function rendered the inversion more stable and less sensitive to the initial model. Tests on synthetic data showed that all the parameters can be estimated well when no noise is present and the estimated P- and S-wave moduli were still reasonable with moderate noise and rather smooth initial model parameters. A test on a real data set showed that the estimated fluid term was in good agreement with the results of drilling.

Inversion and interpretation of a 3D seismic data set from the Ouachita Mountains, Oklahoma

Geophysics ◽  
2009 ◽  
Vol 74 (2) ◽  
pp. B37-B45 ◽  
Author(s):  
Abuduwali Aibaidula ◽  
George McMechan
Keyword(s):  
Seismic Data ◽  
Acoustic Impedance ◽  
3D Visualization ◽  
Wave Impedance ◽  
P Wave ◽  
3D Seismic ◽  
S Wave ◽  
Ouachita Mountains ◽  
Data Set ◽  
3D Seismic Data

Acoustic impedance inversion (AI) and simultaneous angle-dependent inversion (SADI) of a 3D seismic data set characterize reservoirs of Mississippian Morrowan age in the triangle zone of the frontal Ouachita Mountains, Oklahoma. Acoustic impedance of the near-angle seismic data images the 3D spatial distributions of Wapanucka limestone and Cromwell sandstone. Lamé [Formula: see text] ([Formula: see text] and [Formula: see text]) and [Formula: see text] sections are derived from the P-wave and S-wave impedance ([Formula: see text] and [Formula: see text]) sections produced by the SADI. Lithology is identified from the gamma logs and [Formula: see text]. The [Formula: see text], [Formula: see text], and [Formula: see text] are interpreted in terms of a hydrocarbon distribution pattern. The [Formula: see text] is used to identify high [Formula: see text] regions that are consistent with high sand/shale ratio. The estimated impedances and derived Lamé parameter sections are consistent with the interpretation that parts of the Wapanucka limestone and Cromwell sandstone contain potential gas reservoirs in fault-bounded compartments. The Cromwell sandstone contains the main inferred reservoirs; the two largest of these are each [Formula: see text] in pore volume. The inversion results also explain the observed low production in previous wells because those did not sample the best compartments. We propose a single new well location that would penetrate both reservoirs; 3D visualization facilitates this recommendation.

Prestack and poststack inversion using a physics-guided convolutional neural network

2019 ◽  
Vol 7 (3) ◽  
pp. SE161-SE174 ◽  
Author(s):  
Reetam Biswas ◽  
Mrinal K. Sen ◽  
Vishal Das ◽  
Tapan Mukerji
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Elastic Properties ◽  
Wave Velocity ◽  
Seismic Data ◽  
Synthetic Data ◽  
P Wave ◽  
Distinct Advantage ◽  
Elastic Parameters ◽  
Data Set

An inversion algorithm is commonly used to estimate the elastic properties, such as P-wave velocity ([Formula: see text]), S-wave velocity ([Formula: see text]), and density ([Formula: see text]) of the earth’s subsurface. Generally, the seismic inversion problem is solved using one of the traditional optimization algorithms. These algorithms start with a given model and update the model at each iteration, following a physics-based rule. The algorithm is applied at each common depth point (CDP) independently to estimate the elastic parameters. Here, we have developed a technique using the convolutional neural network (CNN) to solve the same problem. We perform two critical steps to take advantage of the generalization capability of CNN and the physics to generate synthetic data for a meaningful representation of the subsurface. First, rather than using CNN as in a classification type of problem, which is the standard approach, we modified the CNN to solve a regression problem to estimate the elastic properties. Second, again unlike the conventional CNN, which is trained by supervised learning with predetermined label (elastic parameter) values, we use the physics of our forward problem to train the weights. There are two parts of the network: The first is the convolution network, which takes the input as seismic data to predict the elastic parameters, which is the desired intermediate result. In the second part of the network, we use wave-propagation physics and we use the output of the CNN to generate the predicted seismic data for comparison with the actual data and calculation of the error. This error between the true and predicted seismograms is then used to calculate gradients, and update the weights in the CNN. After the network is trained, only the first part of the network can be used to estimate elastic properties at remaining CDPs directly. We determine the application of physics-guided CNN on prestack and poststack inversion problems. To explain how the algorithm works, we examine it using a conventional CNN workflow without any physics guidance. We first implement the algorithm on a synthetic data set for prestack and poststack data and then apply it to a real data set from the Cana field. In all the training examples, we use a maximum of 20% of data. Our approach offers a distinct advantage over a conventional machine-learning approach in that we circumvent the need for labeled data sets for training.

Estimating P- and S-wave inverse quality factors from observed seismic data using an attenuative elastic impedance

Geophysics ◽  
2018 ◽  
Vol 83 (2) ◽  
pp. R173-R187 ◽  
Author(s):  
Huaizhen Chen ◽  
Kristopher A. Innanen ◽  
Tiansheng Chen
Keyword(s):  
Seismic Data ◽  
Wave Attenuation ◽  
Synthetic Data ◽  
Real Data ◽  
Quality Factors ◽  
S Wave ◽  
Reservoir Prediction ◽  
Data Set ◽  
Elastic Impedance ◽  
Gas Bearing

P- and S-wave inverse quality factors quantify seismic wave attenuation, which is related to several key reservoir parameters (porosity, saturation, and viscosity). Estimating the inverse quality factors from observed seismic data provides additional and useful information during gas-bearing reservoir prediction. First, we have developed an approximate reflection coefficient and attenuative elastic impedance (QEI) in terms of the inverse quality factors, and then we established an approach to estimate elastic properties (P- and S-wave impedances, and density) and attenuation (P- and S-wave inverse quality factors) from seismic data at different incidence angles and frequencies. The approach is implemented as a two-step inversion: a model-based and damped least-squares inversion for QEI, and a Bayesian Markov chain Monte Carlo inversion for the inverse quality factors. Synthetic data tests confirm that P- and S-wave impedances and inverse quality factors are reasonably estimated in the case of moderate data error or noise. Applying the established approach to a real data set is suggestive of the robustness of the approach, and furthermore that physically meaningful inverse quality factors can be estimated from seismic data acquired over a gas-bearing reservoir.

The matter of size: On the moment magnitude of microseismic events

Geophysics ◽  
2014 ◽  
Vol 79 (3) ◽  
pp. KS31-KS41 ◽  
Author(s):  
Wenjie Jiao ◽  
Michael Davidson ◽  
Arcangelo Sena ◽  
Bradley L. Bankhead ◽  
Yu Xia ◽  
...  
Keyword(s):  
Seismic Moment ◽  
Body Wave ◽  
Attenuation Factor ◽  
Moment Magnitude ◽  
P Wave ◽  
S Wave ◽  
Data Set ◽  
Microseismic Events ◽  
The Moment ◽  
Scalar Moment

We investigated the method of estimating seismic moment and moment magnitude for microseismic events. We determined that the [Formula: see text] defined by Bowers and Hudson is the proper scalar moment to be used in microseismic studies for characterizing the size of an event and calculating its moment magnitude. For non-double-couple sources, the proportional relationship between body-wave amplitude and seismic moment in the Brune model breaks down. So under such situations, the Brune model is not an appropriate way to estimate the seismic moment and magnitude. Moreover, the S-wave alone is not sufficient for determining the total seismic moment. Instead, the P-wave must be analyzed. An example Barnett Shale data set was studied, and the results concluded that the magnitudes estimated with the Brune model could be off by as much as 1.92, with an absolute average of 0.35. The moment magnitudes based on the scalar moment [Formula: see text] also gave a significantly different event size distribution and b-value estimation. Finally, attenuation also played a role in estimating the moment magnitude. With a typical average attenuation factor of [Formula: see text], the average magnitude correction for our field data set was on the order of 0.15. However, it could reach 0.3 for events far away from the monitoring well.

P- and S-wave attenuation logs from monopole sonic data

Geophysics ◽  
2000 ◽  
Vol 65 (3) ◽  
pp. 755-765 ◽  
Author(s):  
Xinhua Sun ◽  
Xiaoming Tang ◽  
C. H. (Arthur) Cheng ◽  
L. Neil Frazer
Keyword(s):  
Wave Attenuation ◽  
Fluid Velocity ◽  
P Wave ◽  
Reservoir Rock ◽  
Rock Properties ◽  
S Wave ◽  
Intrinsic Attenuation ◽  
Modified Method ◽  
Receiver Array ◽  
Attenuation Profiles

In this paper, a modification of an existing method for estimating relative P-wave attenuation is proposed. By generating synthetic waveforms without attenuation, the variation of geometrical spreading related to changes in formation properties with depth can be accounted for. With the modified method, reliable P- and S-wave attenuation logs can be extracted from monopole array acoustic waveform log data. Synthetic tests show that the P- and S-wave attenuation values estimated from synthetic waveforms agree well with their respective model values. In‐situ P- and S-wave attenuation profiles provide valuable information about reservoir rock properties. Field data processing results show that this method gives robust estimates of intrinsic attenuation. The attenuation profiles calculated independently from each waveform of an eight‐receiver array are consistent with one another. In fast formations where S-wave velocity exceeds the borehole fluid velocity, both P-wave attenuation ([Formula: see text]) and S-wave attenuation ([Formula: see text]) profiles can be obtained. P- and S-wave attenuation profiles and their comparisons are presented for three reservoirs. Their correlations with formation lithology, permeability, and fractures are also presented.

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