Computation of dry-rock VP/VS ratio, fluid property factor, and density estimation from amplitude-variation-with-offset inversion

Geophysics ◽  
2018 ◽  
Vol 83 (6) ◽  
pp. R669-R679 ◽  
Author(s):  
Gang Chen ◽  
Xiaojun Wang ◽  
Baocheng Wu ◽  
Hongyan Qi ◽  
Muming Xia
Keyword(s):  
Reservoir Characterization ◽  
Synthetic Data ◽  
Pore Fluid ◽  
Inversion Method ◽  
S Wave ◽  
Amplitude Variation ◽  
Amplitude Variation With Offset ◽  
Fluid Identification ◽  
Fluid Property ◽  
Avo Inversion

Estimating the fluid property factor and density from amplitude-variation-with-offset (AVO) inversion is important for fluid identification and reservoir characterization. The fluid property factor can distinguish pore fluid in the reservoir and the density estimate aids in evaluating reservoir characteristics. However, if the scaling factor of the fluid property factor (the dry-rock [Formula: see text] ratio) is chosen inappropriately, the fluid property factor is not only related to the pore fluid, but it also contains a contribution from the rock skeleton. On the other hand, even if the angle gathers include large angles (offsets), a three-parameter AVO inversion struggles to estimate an accurate density term without additional constraints. Thus, we have developed an equation to compute the dry-rock [Formula: see text] ratio using only the P- and S-wave velocities and density of the saturated rock from well-logging data. This decouples the fluid property factor from lithology. We also developed a new inversion method to estimate the fluid property factor and density parameters, which takes full advantage of the high stability of a two-parameter AVO inversion. By testing on a portion of the Marmousi 2 model, we find that the fluid property factor calculated by the dry-rock [Formula: see text] ratio obtained by our method relates to the pore-fluid property. Simultaneously, we test the AVO inversion method for estimating the fluid property factor and density parameters on synthetic data and analyze the feasibility and stability of the inversion. A field-data example indicates that the fluid property factor obtained by our method distinguishes the oil-charged sand channels and the water-wet sand channel from the well logs.

Two-step joint PP- and PS-wave three-term amplitude-variation with offset inversion

2016 ◽  
Vol 4 (4) ◽  
pp. T613-T625 ◽  
Author(s):  
Qizhen Du ◽  
Bo Zhang ◽  
Xianjun Meng ◽  
Chengfeng Guo ◽  
Gang Chen ◽  
...  
Keyword(s):  
Reflection Coefficient ◽  
Wave Reflection ◽  
Coefficient Matrix ◽  
P Wave ◽  
Inversion Method ◽  
S Wave ◽  
Amplitude Variation ◽  
Amplitude Variation With Offset ◽  
Avo Inversion ◽  
Pp Wave

Three-term amplitude-variation with offset (AVO) inversion generally suffers from instability when there is limited prior geologic or petrophysical constraints. Two-term AVO inversion shows higher instability compared with three-term AVO inversion. However, density, which is important in the fluid-type estimation, cannot be recovered from two-term AVO inversion. To reliably predict the P- and S-waves and density, we have developed a robust two-step joint PP- and PS-wave three-term AVO-inversion method. Our inversion workflow consists of two steps. The first step is to estimate the P- and S-wave reflectivities using Stewart’s joint two-term PP- and PS-AVO inversion. The second step is to treat the P-wave reflectivity obtained from the first step as the prior constraint to remove the P-wave velocity related-term from the three-term Aki-Richards PP-wave approximated reflection coefficient equation, and then the reduced PP-wave reflection coefficient equation is combined with the PS-wave reflection coefficient equation to estimate the S-wave and density reflectivities. We determined the effectiveness of our method by first applying it to synthetic models and then to field data. We also analyzed the condition number of the coefficient matrix to illustrate the stability of the proposed method. The estimated results using proposed method are superior to those obtained from three-term AVO inversion.

Joint anisotropic amplitude variation with offset inversion of PP and PS seismic data

Geophysics ◽  
2018 ◽  
Vol 83 (2) ◽  
pp. N31-N50 ◽  
Author(s):  
Jun Lu ◽  
Yun Wang ◽  
Jingyi Chen ◽  
Ying An
Keyword(s):  
Seismic Data ◽  
Local Minimum ◽  
Synthetic Data ◽  
Inversion Method ◽  
Amplitude Variation ◽  
Amplitude Variation With Offset ◽  
Avo Inversion ◽  
Time Migration ◽  
Amplitude Variation With Angle ◽  
Two Stages

With the increase in exploration target complexity, more parameters are required to describe subsurface properties, particularly for finely stratified reservoirs with vertical transverse isotropic (VTI) features. We have developed an anisotropic amplitude variation with offset (AVO) inversion method using joint PP and PS seismic data for VTI media. Dealing with local minimum solutions is critical when using anisotropic AVO inversion because more parameters are expected to be derived. To enhance the inversion results, we adopt a hierarchical inversion strategy to solve the local minimum solution problem in the Gauss-Newton method. We perform the isotropic and anisotropic AVO inversions in two stages; however, we only use the inversion results from the first stage to form search windows for constraining the inversion in the second stage. To improve the efficiency of our method, we built stop conditions using Euclidean distance similarities to control iteration of the anisotropic AVO inversion in noisy situations. In addition, we evaluate a time-aligned amplitude variation with angle gather generation approach for our anisotropic AVO inversion using anisotropic prestack time migration. We test the proposed method on synthetic data in ideal and noisy situations, and find that the anisotropic AVO inversion method yields reasonable inversion results. Moreover, we apply our method to field data to show that it can be used to successfully identify complex lithologic and fluid information regarding fine layers in reservoirs.

scholarly journals High-Order AVO Inversion for Effective Pore-Fluid Bulk Modulus Based on Series Reversion and Bayesian Theory

Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1313
Author(s):  
Lei Shi ◽  
Yuhang Sun ◽  
Yang Liu ◽  
David Cova ◽  
Junzhou Liu
Keyword(s):  
Bulk Modulus ◽  
Seismic Data ◽  
Pore Fluid ◽  
High Order ◽  
Bayesian Theory ◽  
P Wave ◽  
Seismic Exploration ◽  
Inversion Method ◽  
Fluid Identification ◽  
Avo Inversion

Pore-fluid identification is one of the key technologies in seismic exploration. Fluid indicators play important roles in pore-fluid identification. For sandstone reservoirs, the effective pore-fluid bulk modulus is more susceptible to pore-fluid than other fluid indicators. AVO (amplitude variation with offset) inversion is an effective way to obtain fluid indicators from seismic data directly. Nevertheless, current methods lack a high-order AVO equation for a direct, effective pore-fluid bulk modulus inversion. Therefore, based on the Zoeppritz equations and Biot–Gassmann theory, we derived a high-order P-wave AVO approximation for an effective pore-fluid bulk modulus. Series reversion and Bayesian theory were introduced to establish a direct non-linear P-wave AVO inversion method. By adopting this method, the effective pore-fluid bulk modulus, porosity, and density can be inverted directly from seismic data. Numerical simulation results demonstrate the precision of our proposed method. Model and field data evaluations show that our method is stable and feasible.

Imperialist competitive algorithm optimization method for nonlinear amplitude variation with angle inversion

Geophysics ◽  
2019 ◽  
Vol 84 (3) ◽  
pp. N81-N92 ◽  
Author(s):  
Amir Mollajan ◽  
Hossein Memarian ◽  
Beatriz Quintal
Keyword(s):  
Synthetic Data ◽  
Imperialist Competitive Algorithm ◽  
Optimization Methods ◽  
Hydrocarbon Exploration ◽  
Inversion Method ◽  
Inversion Problem ◽  
S Wave ◽  
Amplitude Variation ◽  
Competitive Algorithm ◽  
Amplitude Variation With Angle

Amplitude variation with angle (AVA) inversion is one of the most effective techniques in hydrocarbon exploration and estimating subsurface petrophysical properties. The inversion problem as a nonlinear, multiparameter, and multimodal optimization problem is conventionally solved through linearized optimization methods, but with the cost of smoothing important geologic interfaces. In addition, the results obtained by these methods are more possible to be trapped in a local minimum, while global-optimization methods can produce more accurate results and preserve the interfaces of geologic structures. A Bayesian framework is used to formulate the AVA inversion problem, which incorporates a novel prior constraint included by two regularization functions, one for sparsity of the coefficients as well as recovering discontinuities and another one for enhancing the lateral continuity. The imperialist competitive algorithm as an efficient evolutionary algorithm is then used to optimize the resulted objective function, to invert the P-and S-wave velocities as well as the density. We compare our algorithm with a commonly used Bayesian linearized inversion method by applying both methods on synthetic data and real seismic data from Gulf of Mexico. Our results reveal the practicability and stability of the presented method for the AVA inversion problem.

Nonlinear amplitude-variation-with-offset inversion for Lamé parameters using a direct inversion method

2017 ◽  
Vol 5 (3) ◽  
pp. SL57-SL67 ◽  
Author(s):  
Guangsen Cheng ◽  
Xingyao Yin ◽  
Zhaoyun Zong
Keyword(s):  
Nonlinear Equation ◽  
Real Data ◽  
Seismic Inversion ◽  
Inversion Method ◽  
Nonlinear Inversion ◽  
Reservoir Prediction ◽  
Amplitude Variation ◽  
Linear Inversion ◽  
Amplitude Variation With Offset ◽  
Avo Inversion

Prestack seismic inversion is widely used in fluid indication and reservoir prediction. Compared with linear inversion, nonlinear inversion is more precise and can be applied to high-contrast situations. The inversion results can be affected by the parameters’ sensitivity, so the parameterization of nonlinear equations is very significant. Considering the poor nonlinear amplitude-variation-with-offset (AVO) inversion results of impedance and velocity parameters, we adjust the parameters of the nonlinear equation, avoid the inaccuracy caused by parameters sensitivity and get the ideal nonlinear AVO inversion results of the Lamé parameters. The feasibility and stability of the nonlinear equation based on the Lamé parameters and method are verified by the model and the real data examples. The resolution and the lateral continuity of nonlinear inversion results are better compared with the linear inversion results.

A data-driven amplitude variation with offset inversion method via learned dictionaries and sparse representation

Geophysics ◽  
2018 ◽  
Vol 83 (6) ◽  
pp. R725-R748 ◽  
Author(s):  
Bin She ◽  
Yaojun Wang ◽  
Jiandong Liang ◽  
Zhining Liu ◽  
Chengyun Song ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Prior Information ◽  
Data Driven ◽  
Inversion Method ◽  
Model Parameters ◽  
Well Log ◽  
Elastic Parameters ◽  
Amplitude Variation ◽  
Amplitude Variation With Offset ◽  
Avo Inversion

Amplitude variation with offset (AVO) inversion is a typical ill-posed inverse problem. To obtain a stable and unique solution, regularization techniques relying on mathematical models from prior information are commonly used in conventional AVO inversion methods (hence the name model-driven methods). Due to the difference between prior information and the actual geology, these methods often have difficulty achieving satisfactory accuracy and resolution. We have developed a novel data-driven inversion method for the AVO inversion problem. This method can effectively extract useful knowledge from well-log data, including sparse dictionaries of elastic parameters and sparse representation of subsurface model parameters. Lateral continuity of subsurface geology allows for the approximation of model parameters for a work area using the learned dictionaries. Instead of particular mathematical models, a sparse representation is used to constrain the inverse problem. Because no assumption is made about the model parameters, we consider this a data-driven method. The general process of the algorithm is as follows: (1) using well-log data as the training samples to learn the sparse dictionary of each elastic parameter, (2) imposing a sparse representation constraint on the objective function, making the elastic parameters be sparsely represented over the learned dictionary, and (3) solving the objective function by applying a coordinate-descent algorithm. Tests on several synthetic examples and field data demonstrate that our algorithm is effective in improving the resolution and accuracy of solutions and is adaptable to various geologies.

Anisotropic correction of sonic logs in wells with large relative dip

Geophysics ◽  
2008 ◽  
Vol 73 (1) ◽  
pp. E1-E5 ◽  
Author(s):  
Lev Vernik
Keyword(s):  
Reservoir Characterization ◽  
Low Frequency ◽  
P Wave ◽  
Amplitude Variation With Offset ◽  
Seismic Amplitude ◽  
Avo Inversion ◽  
Pore Pressure Prediction ◽  
Siliciclastic Rocks ◽  
Dip Angle ◽  
Sonic Logs

Seismic reservoir characterization and pore-pressure prediction projects rely heavily on the accuracy and consistency of sonic logs. Sonic data acquisition in wells with large relative dip is known to suffer from anisotropic effects related to microanisotropy of shales and thin-bed laminations of sand, silt, and shale. Nonetheless, if anisotropy parameters can be related to shale content [Formula: see text] in siliciclastic rocks, then I show that it is straightforward to compute the anisotropy correction to both compressional and shear logs using [Formula: see text] and the formation relative dip angle. The resulting rotated P-wave sonic logs can be used to enhance time-depth ties, velocity to effective stress transforms, and low-frequency models necessary for prestack seismic amplitude variation with offset (AVO) inversion.

Application of structural interpretation and simultaneous inversion to reservoir characterization of the Duvernay Formation, Fox Creek, Alberta, Canada

2019 ◽  
Vol 38 (2) ◽  
pp. 151-160 ◽  
Author(s):  
Ronald Weir ◽  
Don Lawton ◽  
Laurence Lines ◽  
Thomas Eyre ◽  
David Eaton
Keyword(s):  
Reservoir Characterization ◽  
Lateral Displacement ◽  
Rock Properties ◽  
S Wave ◽  
Amplitude Variation With Offset ◽  
Structural Interpretation ◽  
Simultaneous Inversion ◽  
Prestack Inversion ◽  
Unconventional Resource ◽  
Depth Relationship

Simultaneous prestack inversion of multicomponent 3D seismic data integrated with structural interpretation can provide an effective workflow to maximize value for unconventional plays. We outline an integrated workflow for characterizing the Duvernay play in western Canada, an emerging world-class low-permeability unconventional resource fairway. This workflow includes the determination of a time-depth relationship using synthetic seismograms, generation of seismic-derived time- and depth-converted structural maps, and calculation of inversion-based parameters of density and P- and S-wave velocity. The model-based procedure includes poststack (acoustic) inversion, amplitude variation with offset prestack inversion, and joint PP-PS inversion. With these rock properties determined, calculations are made to determine Young's modulus, Poisson's ratio, and brittleness. Faults are mapped based on time slices, isochrons, and correlatable vertical displacements of stratigraphic marker reflections. Significant strike-slip movements are identified by lateral displacement on interpreted geologic features, such as channels and reef edges. Seismic-derived attributes, combined with structural mapping, highlight zones that are conducive to hydraulic fracturing as well as areas unfavorable for development. Mapping of structural discontinuities provides a framework for understanding zones of preexisting weakness and induced-seismicity hazards.

Signal leakage in f-x deconvolution algorithms

Geophysics ◽  
2017 ◽  
Vol 82 (5) ◽  
pp. W31-W45 ◽  
Author(s):  
Necati Gülünay
Keyword(s):  
Field Data ◽  
New Technologies ◽  
Filter Design ◽  
Synthetic Data ◽  
Data Sets ◽  
Amplitude Variation ◽  
Amplitude Variation With Offset ◽  
Backward Design ◽  
Use Of Data ◽  
Domain Prediction

The old technology [Formula: see text]-[Formula: see text] deconvolution stands for [Formula: see text]-[Formula: see text] domain prediction filtering. Early versions of it are known to create signal leakage during their application. There have been recent papers in geophysical publications comparing [Formula: see text]-[Formula: see text] deconvolution results with the new technologies being proposed. These comparisons will be most effective if the best existing [Formula: see text]-[Formula: see text] deconvolution algorithms are used. This paper describes common [Formula: see text]-[Formula: see text] deconvolution algorithms and studies signal leakage occurring during their application on simple models, which will hopefully provide a benchmark for the readers in choosing [Formula: see text]-[Formula: see text] algorithms for comparison. The [Formula: see text]-[Formula: see text] deconvolution algorithms can be classified by their use of data which lead to transient or transient-free matrices and hence windowed or nonwindowed autocorrelations, respectively. They can also be classified by the direction they are predicting: forward design and apply; forward design and apply followed by backward design and apply; forward design and apply followed by application of a conjugated forward filter in the backward direction; and simultaneously forward and backward design and apply, which is known as noncausal filter design. All of the algorithm types mentioned above are tested, and the results of their analysis are provided in this paper on noise free and noisy synthetic data sets: a single dipping event, a single dipping event with a simple amplitude variation with offset, and three dipping events. Finally, the results of applying the selected algorithms on field data are provided.

Linearized amplitude variation with offset (AVO) inversion with supercritical angles

Geophysics ◽  
2006 ◽  
Vol 71 (5) ◽  
pp. E49-E55 ◽  
Author(s):  
Jonathan E. Downton ◽  
Charles Ursenbach
Keyword(s):  
Reflection Coefficient ◽  
Critical Angle ◽  
Waveform Inversion ◽  
Phase Variation ◽  
Reflection Coefficients ◽  
Amplitude Variation ◽  
Amplitude Variation With Offset ◽  
Zoeppritz Equations ◽  
Avo Inversion ◽  
Angle Data

Contrary to popular belief, a linearized approximation of the Zoeppritz equations may be used to estimate the reflection coefficient for angles of incidence up to and beyond the critical angle. These supercritical reflection coefficients are complex, implying a phase variation with offset in addition to amplitude variation with offset (AVO). This linearized approximation is then used as the basis for an AVO waveform inversion. By incorporating this new approximation, wider offset and angle data may be incorporated in the AVO inversion, helping to stabilize the problem and leading to more accurate estimates of reflectivity, including density reflectivity.

Sign in / Sign up

Export Citation Format

Share Document