Post-stack Impedance Inversion Research Articles

Structurally Constrained Initial Impedance Modeling for Poststack Seismic Inversion

The establishment of initial subsurface model is a crucial step for seismic inversion. An accurate and reasonable initial model can mitigate the ill-posedness of seismic inversion and improve the quality of inversion result. A common method for building initial model is the well-log data interpolation. However, the traditional well-log interpolation method ignores the structural information of the subsurface, resulting in the constructed initial model lacking geological meaning. We propose a novel structurally constrained modeling method (SCMM) to obtain a geologically reasonable initial impedance model for poststack seismic inversion. Well-log interpolation can be represented as an inverse problem. SCMM constrains the inversion process by using a regularization operator that forces the well-log data to be extended to the entire seismic working area along the subsurface local structural direction. First, we calculate the seismic dip from the poststack seismic profile. Then, we design the structural operator based on the estimated seismic dip information to constrain the interpolation process. Under the framework of inversion, the interpolation objective function can be established by combining the structural operator with the well-log data misfit term, and it can be solved efficiently by the conjugate gradient algorithm. Synthetic and field data tests show that the initial model built by SCMM is more consistent with the geological rules than that built by traditional method, and the poststack impedance inversion using SCMM is better than that using traditional modeling method in terms of convergence property and accuracy of inversion result.

Broadband Acoustic Impedance Building via the Fusion of Sparsely Promoted Reflectivity and Background Acoustic Impedance

Geophysical exploration is developing from qualitative seismic imaging to quantitative imaging, and broadband acoustic impedance is the core. Directly estimating broadband impedance using Full-Waveform Inversion is a strong nonlinear problem. It is difficult to obtain a reliable result in practice. We proposed an alternative way: Estimate background velocity, density and broadband reflectivity first, then fuse them to be the broadband impedance by information fusion. This paper studies the method of fusing bandlimited reflectivity and background impedance. Due to the observation with bandlimited seismic wavelet, only bandlimited reflectivity can be obtained even after a lot of processing. The bandlimited reflectivity can lead to oscillation error in impedance. Different from the conventional post-stack impedance inversion, this paper introduces an iterative process without the need of wavelet extraction. Start from the broadband reflectivity that has been subjected to fidelity imaging, least-squares migration and magnitude calibration. In order to reduce the oscillation error, reflectivity is sparsely promoted, such that the reflection coefficients from large to small are gradually fused with background impedance. Reflectivity and impedance are mutually constrained and iteratively updated, and lateral continuity is incorporated. Numerical experiment and three-dimensional field data application demonstrate the effectiveness of the method. Impedance shows higher interpretability than reflectivity.

New challenge for the rock physics and post-stack impedance inversion for detecting the erosional surface of Matruh Canyon at Emry Oil Field, Western Desert, Egypt

New challenge for the rock physics and post-stack impedance inversion for detecting the erosional surface of Matruh Canyon at Emry Oil Field, Western Desert, Egypt

Multitrace Impedance Inversion Based on Structure-Oriented Regularization

As an effective approach of reservoir prediction, poststack impedance inversion has been widely used in industry. However, like other inverse problems, poststack impedance inversion is a quintessential ill-conditioned problem. Conventional impedance inversion methods often use regularization techniques such as Tikhonov-type regularization to improve the stability of the inversion solution. Nevertheless, because this type of regularization method constrains the inversion process by applying isotropic smoothness to the impedance, it will result in blurred boundaries and micro-geological structures, thereby reducing the resolution of the inversion result. In order to address this problem, we have introduced a structure-oriented regularization (SOR) method based on the local geological structural direction for impedance inversion. Compared with conventional isotropic smooth constraints, SOR can apply smoothness along the direction of geological structures, so it can effectively protect significant geological information from being blurred. Three steps are required to complete the SOR-based seismic impedance inversion method. To begin with, the structural orientations are estimated from seismic image. Then, the SOR term is constructed and combined with the multitrace seismic data misfit term to formulate the objective function for the inversion of impedance. Finally, the objective function can be easily solved by the conjugate gradient (CG) method. We compare our method with the classic model-based impedance inversion method on synthetic and real seismic data. The inversion results demonstrate the benefit of our method in seismic impedance inversion.

Post-Stack Impedance Inversion Based on Spatio-Temporal Neural Network

Seismic acoustic impedance bridges the gap between post-stack seismic reflection data and reservoir parameters such as lithology and porosity, and hence it plays an important role in stratigraphic interpretation. Due to sedimentation and propagation effects, both seismic records and impedance are a type of spatio-temporal data, i.e., there should be coupling between adjacent data points. However, most deep learning inversion methods only consider local shape information, ignore the time-series characteristics of the data, and are demanding on the training data, which makes inversion more difficult and leads to low inversion accuracy. For this reason, we develop a spatio-temporal neural network (STNN) to perform post-stack impedance inversion. The network consists mainly of a convolutional neural network (CNN) block and a recurrent neural network (RNN) block in series. Thus, the proposed method can take full advantage of CNN and RNN to capture the dynamics and correlations of seismic series in the spatio-temporal levels, yielding more continuous and stable results. We use an overthrust model example and an actual data case to test the performances of the STNN and demonstrate its advantages over traditional deep learning (i.e., CNN) based impedance inversion. Through a series of numerical experiments, we find that STNN produces more geologically reliable results, which not only ensure coupling relationships between adjacent points in the vertical direction, but also reveal well the stratigraphic variations in the lateral direction.

Joint impedance inversion and spectral decomposition for deepwater gas reservoir characterization: A case study in South China Sea

Gas reservoir characterization is one of the frontiers in seismic exploration. Acoustic impedance, one of the most effective seismic attributes, aims to describe the spatial distribution of rock properties. However, using acoustic impedance only is insufficient to describe gas-bearing layers accurately, in the case of rapid lithologic changes and complex geology in the deepwater area. The reflection seismograms indicate an absorption effect when seismic waves propagate through gas-bearing layers. The absorption effect can be used as an additional attribute to help gas reservoir characterization. Therefore, we have developed a new attribute for gas reservoir characterization in this study, which integrates the results of acoustic impedance and absorption coefficient. We estimate the acoustic impedance model by using poststack impedance inversion and then we calculate probability distribution functions. Functions are classified into gas-bearing and nongas layers. We discuss an absorption coefficient and obtain it from the spectrum gradient, in which the gradient is calculated by spectral decomposition using the matching pursuit method. We apply the new attribute to characterize the spatial distribution and thickness of deepwater gas reservoirs in the Pearl River Mouth Basin. Well-log and geologic information indicate that the study area has an enrichment of gas reservoirs. Field data application indicates the explicit distribution of the gas reservoir and in accordance with the well-log information, which indicates that our attribute can improve gas reservoir characterization.

Prestack inversion identification of organic reef gas reservoirs of Permian Changxing Formation in Damaoping area, Sichuan Basin, SW China

Organic reef reservoirs in the platform margin of Kaijiang-Liangping trough in Damaoping area, Sichuan Basin are thin in single layer, fast in lateral variation, and have small P-impedance difference from the surrounding rock, it is difficult to identify and predict the reservoirs and fluid properties by conventional post-stack inversion. Through correlation analysis of core test data and logging P-S wave velocity, this work proposed a formula to calculate the shear wave velocity in different porosity ranges, and solved the issue that some wells in the study area have no S-wave data. AVO forward analysis reveals that formation porosity is the main factor affecting the variation of AVO type, the change of water saturation cannot affect the AVO type, but it has an effect on the change range of AVO. Through cross-plotting analysis of elastic parameters, it is found that fluid factor is a parameter sensitive to gas-bearing property of organic reef reservoir in the study area. By comparing results of post-stack impedance inversion, post-stack high frequency attenuation property, pre-stack simultaneous inversion and AVO anomaly analysis of angle gathers, it is found that the gas-bearing prediction of organic reef reservoirs by using fluid factor derived from simultaneous pre-stack inversion had the highest coincidence rate with actual drilling data. At last, according to the characteristics of fluid factor distribution, the favorable gas-bearing area of the organic reef reservoir in Changxing Formation was predicted, and the organic reef trap at the top of Changxing Formation in Block A of Damaoping area was sorted out as the next exploration target.

Poststack impedance inversion considering the diffractive component of the wavefield

Seismic diffraction waveform energy contains important information about small-scale subsurface elements, and it is complementary to specular reflection information about subsurface properties. Diffraction imaging has been used for fault, pinchout, and fracture detection. Very little research, however, has been carried out taking diffraction into account in the impedance inversion. Usually, in the standard inversion scheme, the input is the migrated data and the assumption is taken that the diffraction energy is optimally focused. This assumption is true only for a perfectly known velocity model and accurate true amplitude migration algorithm, which are rare in practice. We have developed a new approach for impedance inversion, which takes into account diffractive components of the total wavefield and uses the unmigrated input data. Forward modeling, designed for impedance inversion, includes the classical specular reflection plus asymptotic diffraction modeling schemes. The output model is composed of impedance perturbation and the low-frequency model. The impedance perturbation is estimated using the Bayesian approach and remapped to the migrated domain by the kinematic ray tracing. Our method is demonstrated using synthetic and field data in comparison with the standard inversion. Results indicate that inversion with taking into account diffraction can improve the acoustic impedance prediction in the vicinity of local reflector discontinuities.

Impedance inversion by using the low-frequency full-waveform inversion result as an a priori model

Prestack acoustic full-waveform inversion (FWI) can provide long-wavelength components of the P-wave velocity by using low frequencies and long-offset direct/diving/refracted waves, which could be simulated via a large space grid, and it is weakly sensitive to density. Poststack impedance inversion can usually quickly yield high-resolution impedance, and it is sensitive to density. Therefore, we have combined these two methods to develop an FWI-driven impedance inversion. Our method first uses FWI to obtain the long-wavelength velocity with a guaranteed overlap between the high frequencies of the velocity and the low frequencies of the poststack data. Then, the fitting rock-physics relationship between the density and the velocity is adopted to translate the FWI velocity into the low-frequency impedance. Finally, the resulting low-frequency impedance is used to construct an a priori constraint for poststack impedance inversion. The method has the ability to solve the overlap between the FWI-based converted prior impedance model and poststack data, and it can thereby yield a broadband absolute impedance result. We adopt a Marmousi II model example and a real data case to test the performances of the FWI-driven impedance inversion and indicate its advantages compared with the conventional well-driven impedance inversion that uses well logs and interpreted horizons to build the prior impedance model. The synthetic data example demonstrates that well-driven impedance inversion produces a result with a relatively large deviation to the true impedance model at complex structure zones. However, FWI-driven impedance inversion favorably recovers all interesting sediment layers at complex structure zones. The real data example illustrates that well-driven impedance inversion yields a result with a distinct footprint of the prior model created from well logs and horizons. On the other hand, we find that FWI-driven impedance inversion yields a geologically reasonable solution, which not only conforms to the time-space variation trend of the well logs, but it also reveals a basin structural-depositional evolution.

Characterizing a Mississippian tripolitic chert reservoir using 3D unsupervised and supervised multiattribute seismic facies analysis: An example from Osage County, Oklahoma

Seismic interpretation is based on the identification of reflector configuration and continuity, with coherent reflectors having a distinct amplitude, frequency, and phase. Skilled interpreters may classify reflector configurations as parallel, converging, truncated, or hummocky, and use their expertise to identify stratigraphic packages and unconformities. In principal, a given pattern can be explicitly defined as a combination of waveform and reflector configuration properties, although such “clustering” is often done subconsciously. Computer-assisted classification of seismic attribute volumes builds on the same concepts. Seismic attributes not only quantify characteristics of the seismic reflection events, but also measure aspects of reflector configurations. The Mississippi Lime resource play of northern Oklahoma and southern Kansas provides a particularly challenging problem. Instead of defining the facies stratigraphically, we need to define them either diagenetically (tight limestone, stratified limestone and nonporous chert, and highly porous tripolitic chert) or structurally (fractured versus unfractured chert and limestone). Using a 3D seismic survey acquired in Osage County Oklahoma, we use Kohonen self-organizing maps to classify different diagenetically altered facies of the Mississippi Lime play. The 256 prototype vectors (potential clusters) reduce to only three or four distinct “natural” clusters. We use ground truth of seismic facies seen on horizontal image logs to fix three average attribute data vectors near the well locations, resulting in three “known” facies, and do a minimum Euclidean distance supervised classification. The predicted clusters correlate well to the poststack impedance inversion result.