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In seismic data analysis, acoustic impedance may be used for predicting the porosity of a sedimentary layer with known lithology, where the influence on impedance from pore fluid is known. This method typically assumes that the stiffness of the rock frame is solely a function of porosity and that there is no variation in stiffness between rocks with the same composition and porosity. However, contact cementation can cause an increase in rock frame stiffness with no significant reduction in porosity. This means that rocks with similar porosities and varying degrees of cementation would have significantly different elastic moduli; thus, attempting to derive porosity from seismic impedance alone would incur errors. The primary goal of this study is to assess how significant this error is for porosity prediction. The secondary goal is to calculate the dry rock frame stiffness across the field and evaluate to what extent the time of hydrocarbon placement affects the lithification of the rock. To fulfill these objectives, we studied petrophysical logs and core data from the Dan field and derived seismic impedance, porosity and stiffness expressed in terms of Biot’s coefficient for 14 wells across the field. Trends defined between acoustic impedance, porosity, and Biot’s coefficient in the Maastrichtian unit of the Dan field showed that, for a given porosity and water saturation in the oil zone, the chalk located at the crest of the reservoir is softer than the chalk located at the flanks of the field.We also found that chalk is stiffer in the down-faulted north-western part of the field for a given porosity and water saturation in the oil zone. In the water zone, no difference was found. We speculate that contact cementation downflank in the northwest block caused an increase in the chalk stiffness without reducing the porosity, while earlier hydrocarbon filling of the south-eastern block and the crest of the structure delayed contact cementation between the chalk particles. This difference in contact cementation between rocks of the same porosity causes porosity prediction from acoustic impedance to vary by up to six porosity units.

Seismic data reconstruction is an essential and yet fundamental step in seismic data processing workflow, which is of profound significance to improve migration imaging quality, multiple suppression effect, and seismic inversion accuracy. Regularization methods play a central role in solving the under-determined inverse problem of seismic data reconstruction. In this paper, a novel regularization approach is proposed, the low dimensional manifold model (LDMM), for reconstructing the missing seismic data. Our work relies on the fact that seismic patches always occupy a low dimensional manifold. Specifically, we exploit the dimension of the seismic patches manifold as a regularization term in the reconstruction problem, and reconstruct the missing seismic data by enforcing low dimensionality on this manifold. The crucial procedure of the proposed method is to solve the dimension of the patches manifold. Toward this, we adopt an efficient dimensionality calculation method based on low-rank approximation, which provides a reliable safeguard to enforce the constraints in the reconstruction process. Numerical experiments performed on synthetic and field seismic data demonstrate that, compared with the Curvelet-based sparsity-promoting L1-norm minimization method and the multichannel singular spectrum analysis method, the proposed method obtains state-of-the-art reconstruction results.

Deep learning has achieved great success in a variety of research fields and industrial applications. However, when applied to seismic inversion, the shortage of labeled data severely influences the performance of deep learning-based methods. In order to tackle this problem, we propose a novel seismic impedance inversion method based on a cycle-consistent generative adversarial network (Cycle-GAN). The proposed Cycle-GAN model includes two generative subnets and two discriminative subnets. Three kinds of loss, including cycle-consistent loss, adversarial loss, and estimation loss, are adopted to guide the training process. Benefit from the proposed structure, the information contained in unlabeled data can be extracted, and adversarial learning further guarantees that the prediction results share similar distributions with the real data. Moreover, a neural network visualization method is adopted to show that the proposed CNN model can learn more distinguishable features than the conventional CNN model. The robustness experiments on synthetic data sets show that the proposed method can achieve better performances than other methods in most cases. And the blind-well experiments on real seismic profiles show that the predicted impedance curve of the proposed method maintains a better correlation with the true impedance curve.

Compared with the plane-wave reflection coefficient, the spherical-wave reflection coefficient (SRC) can more accurately describe the reflected wavefield excited by a point source, especially in the case of low seismic frequency and short travel distance. However, unlike the widely used plane-wave amplitude-variation-with-offset/frequency (AVO/AVF) inversion, the practical application of spherical-wave AVO/AVF inversion in multilayer elastic media is still in the exploratory stage. One of the difficulties is how to fully use the amplitude and phase information of the complex-valued SRC and the spherical-wave response property of each frequency component to obtain the spherical-wave synthetic seismogram in multilayer elastic media. In view of this, we have developed a complex convolution model considering the amplitude and phase information of an SRC to obtain the complex synthetic seismogram of a certain frequency component. A simple harmonic superposition method is further developed. By superposing the complex synthetic seismograms of different frequency components, the synthetic seismogram of the full-frequency band can be obtained. In addition, a novel three-parameter SRC in terms of P- and S-wave moduli and density is derived. Based on the SRC and complex seismic traces with different offsets (or incidence angles) and frequency components, an inversion approach of complex spherical-wave amplitude and phase variation with offset and frequency is proposed. A noisy synthetic data example verifies the robustness of our complex spherical-wave inversion approach. Field data examples indicate that the P- and S-wave moduli estimated by the complex spherical-wave inversion approach can reasonably match the filtered well-logging data. Considering spherical waves rather than plane waves can improve the accuracy of seismic inversion results.

We describe a new seismic inversion workflow to simultaneously invert for velocity and reflectivity. With a single modelling engine, parameterized in terms of velocity and vector reflectivity, the two earth properties are iteratively updated using their appropriate sensitivity kernels based on inverse scattering theory. The vector reflectivity is estimated as a data domain least-squares migration and is the key for updating the velocity model beyond the maximum penetration depth of refracted and diving wave energy. With field examples, we demonstrate how the new solution for vector-reflectivity modelling combined with proper inversion kernels enable us to address the long-standing challenge of building full-bandwidth earth models.

The mapping and characterization of thin gas sand layers have always been a challenge through conventional seismic interpretation procedures. Seismic inversion techniques have been effectively applied to evaluate the hydrocarbon reservoirs with high heterogeneity and deep burial depth with thicknesses below seismic resolution. In this study, thin gas sand layers of the Lower Ranikot Formation of the Mehar gas field in the Kirthar Fold Belt, Pakistan, have been characterized via the application of sensitive post-stack model-based seismic inversion (PMBSI) and pre-stack simultaneous seismic inversion (PSSI). These sands are producing in the nearby fields; however, their potential is still untapped in the study area, which needs to be explored for further development. In order to evaluate the gas potential, petrophysical analysis demarcated three gas-bearing zones within the targeted reservoir. The spatial extent of these identified sand packages has been evaluated in detail by the application of the above selected seismic inversion techniques. The results of PMBSI depicted low impedance values within the sand layers of the Lower Ranikot Formation, but PSSI efficaciously separated the thin gas-bearing and non-bearing sand intervals. Furthermore, the impedance-derived porosities predicted via probabilistic neural network (PNN) show excellent agreement with the computed porosities from interpreted well logs and at producing blind wells. The techniques adopted to delineate and map the potential thin gas sands of the Mehar gas field may be very fruitful and adequate for basins with similar geological settings.

In subsurface modeling and characterization, predicting the spatial distribution of subsurface elastic properties is commonly achieved by seismic inversion. Stochastic seismic inversion methods, such as iterative geostatistical seismic inversion (GSI), are widely applied to this end. Global iterative GSI methods are computationally expensive because they require, at a given iteration, the stochastic sequential simulation of the entire inversion grid at once multiple times. Functional data analysis (FDA) is a well-established statistical method suited to model long-term and noisy temporal series. This method allows us to summarize spatiotemporal series in a set of analytical functions with a low-dimension representation. FDA has been recently extended to problems related to geosciences, but its application to geophysics is still limited. We have developed the use of FDA as a model reduction technique during the model perturbation step in global iterative GSI. FDA is used to collapse the vertical dimension of the inversion grid. We illustrate our hybrid inversion method with its application to 3D synthetic and real data sets. The results indicate the ability of our inversion methodology to predict smooth inverted subsurface models that match the observed data at a similar convergence as obtained by a global iterative GSI, but with a considerable decrease in the computational cost. Although the resolution of the inverted models might not be enough for a detailed subsurface characterization, the inverted models can be used as a starting point of global iterative GSI to speed up the inversion or to test alternative geologic scenarios by changing the inversion parameterization and obtaining inverted models in a relatively short time.

The prior information constrained impedance inversion is an important tool to improve the inversion effect. With the traditional constrained prior information extracted from logging data by the analytic formula, it is difficult to accurately describe the information of a complex reservoir. In addition, the traditional inversion method is trace-by-trace, which ignores the lateral information contained in seismic data. This paper presents a multichannel seismic impedance inversion method combining logging and seismic. In this method, the dictionary learning method is used to extract the vertical prior information of the formation from the logging data. At the same time, we can learn the dip information from seismic data cube. Under the framework of multichannel inversion, regularization and sparse representation technology are used to simultaneously add the vertical and the transverse distribution prior information into the inversion process. Block coordinate descent method is used to solve the multichannel inversion problem, making the seismic inversion efficient. This method excavates the spatial prior information in a data-driven way and is used for constrained inversion, avoiding the false prior cognition caused by manual interpretation. Through the model and field data testing, it is verified that this method is effective.

Correct lithofacies interpretation sourced from wireline log data is an essential source of prior information for joint seismic inversion for facies and impedances, among other applications. However, this information is difficult to interpret or extract manually due to the multivariate and high dimensionality of wireline logs. Facies inference is also challenging for traditional clustering-based approaches because pervasive compaction trends affect a number of petrophysical measurements simultaneously. Another common pitfall in automated clustering approaches is the inability to account for underlying diagenetic processes that correlate with depth. Here, we address these challenges by introducing a rock-physics machine learning toolkit for joint litho-fluid facies classification. The litho-fluid types are inferred from the borehole data within the objective framework of a maximum-likelihood approach for latent facies variables and rock-physics model parameters, explicitly accounting for compaction and depth effects. The inference boils down to an expectation-maximization (EM) algorithm with strong spatial coupling. Each litho-fluid type is associated with an instance of a particular rock-physics model with a unique set of fitting parameters, constrained to a physically reasonable range. These fitting parameters in turn are inferred using bound-constrained optimization as part of the EM algorithm. Outputs produced by the toolkit can be used directly to specify the necessary prior information for seismic inversion, including per-facies rock-physics models and facies proportions. We present an example application of the tool to real borehole data from the North West Shelf of Australia to illustrate the method and discuss its characteristic features in depth.

Differentiating brittle zones from ductile zones in low permeability shale formations is imperative for efficient hydraulic fracturing stimulation. The brittleness index (BI) is used to describe the rock resistance to hydraulic fracture initiation and propagation and measures the ease at which complex fracture networks can be created. In this study, we constructed brittleness templates through the correlation of fundamental rock properties and geomechanical characterization. We then employed the templates to distinguish the brittle, ductile, and brittle-ductile transition zones in the Longmaxi shale gas reservoir, Sichuan Basin of southern China. The approach works in two steps. First, we suggest a new expression for the mineralogical BI by their respective weights based on the analysis of correlation coefficients between mechanical testing and XRD results. Second, we correlate TOC, porosity, pore fluid, natural fractures, and improved BI model with multiple elastic properties to define the brittle, ductile, and transitional zones in the Longmaxi shale gas reservoir of China. Compared with the traditional mineralogy-based BI definition, the improved BI model differentiates the brittle and ductile zones and provides a better sense of the most suitable fracturing regions. Our results show that the brittleness templates, which combine fundamental rock properties, improved BI model, and geomechanical characterization led to identifying favorable zones for hydraulic fracturing and enhanced shale characterization. The proposed brittleness templates’ effectiveness was verified using data from horizontal wells, offset wells, shale gas wells from different origins, laboratory core testing, and seismic inversion of BI across the studied wells.

We have developed a method to automatically relate events between observed and synthetic data for wave-equation traveltime (WT) inversion. The scheme starts with local similarity measurements by applying crosscorrelation to localized traces. We then develop a differentiable alternative for the argmax function. By applying the differentiable argmax to each time slice of the local similarity map, we build a traveltime difference function but keep this process differentiable. WT inversion requires only the traveltime difference of related events through a phase shift. Thus, we must reject events that are not apparently related between observed and synthetic data. The local similarity map implies the possibility of doing so but indicates abrupt changes with time and offset. To mitigate this problem, we have introduced a dynamic programming algorithm to define a warping function. Wave packets in the observed and synthetic data are assumed to be related if they are connected by this warping function and if they exhibit high local similarity. Only such related events are considered in the subsequent calculation of misfit and adjoint source. Numerical examples demonstrate that our method successfully retrieves an informative model from roughly selected data. In contrast, WT inversion based on crosscorrelation or deconvolution fails to do so.

The S-wave velocity ([Formula: see text]) is a fundamental parameter in geophysical analysis, prestack seismic inversion, and reservoir prediction. For various reasons, the availability of directly measured values of [Formula: see text] is low, especially in old wells. Therefore, indirect estimations of [Formula: see text] data on the basis of available reservoir information are important, and the development of a high-efficiency and low-cost prediction method is necessary. We have developed a novel prediction method that combines the convolutional neural network (CNN) and the gated recurrent unit (GRU) algorithms, based on a deep convolutional GRU (DCGRU) approach. More specifically, a CNN structure is used to identify and memorize the complex relationship between [Formula: see text] and well-log data, whereas a GRU network is introduced to extract key features of the data series in the depth direction. Owing to its structure, the DCGRU approach can seamlessly account for data trends with depth, local correlations across data series, and the actual depth accumulation effect. This approach is tested on data sets from an actual reservoir; it provides more reliable and accurate [Formula: see text] predictions not only compared with empirical models but also compared with the CNN and GRU algorithms applied separately. Our approach has potential for accurately estimating [Formula: see text] from log data.

Increasing mining depths and intensities have resulted in frequent accidents during roadway excavation, thus increasing the necessity of preemptively determining the geological conditions of rock formations along roadways. Identifying the spatial distributions of lithotypes and the mechanical properties of non-marker bed intervals in coal measure strata are crucial for roadway horizon design, roof management, and efficient excavation. Therefore, based on petrophysical tests, well logging, and seismic data, a comprehensive method is proposed, which uses post-stack wave impedance inversion, petrophysical statistical analyses, and pre-stack simultaneous inversion to predict the lithology and elastic parameters of rocks surrounding roadways. The lithology of the target layer is first divided according to the wave impedances of the inversion body, and the sand lenses within the mudstone interval are described. Based on petrophysical test data, the shear-wave conversion equations of the sand lenses in this target interval and background mudstone are then obtained via marginal limit extrapolation. Finally, pre-stack simultaneous inversion, based on the seismic data and shear-wave conversion curves of different lithologies, is performed to obtain the elastic parameter profile of the target interval in the cross-section of the roadway, laying the foundation for further prediction of the mechanical properties of the surrounding rocks. The results agree with roadway drilling data by > 90%.

Building realistic and reliable models of the subsurface is the primary goal of seismic imaging. We have constructed an ensemble of convolutional neural networks (CNNs) to build velocity models directly from the data. Most other approaches attempt to map full data into 2D labels. We exploit the regularity of seismic acquisition and train CNNs to map gathers of neighboring common midpoints (CMPs) to vertical 1D velocity logs. This allows us to integrate well-log data into the inversion, simplify the mapping by using the 1D labels, and accommodate larger dips relative to using single CMP inputs. We dynamically generate the training data in parallel with training the CNNs, which reduces overfitting. Data generation and training of CNNs is more computationally expensive than conventional full-waveform inversion (FWI). However, once the network is trained, data sets with similar acquisition parameters can be inverted much faster than with FWI. The multiCMP CNN ensemble is tested on multiple realistic synthetic models, performs well, and was combined with FWI for even better performance.

Seismic inversion is a severely ill-posed problem because of noise in the observed record, band-limited seismic wavelets, and the discretization of a continuous medium. Regularization techniques can impose certain characteristics on inversion results based on prior information to obtain a stable and unique solution. However, it is difficult to find an appropriate regularization to describe the actual subsurface geology. We have developed a new acoustic impedance inversion method via a patch-based Gaussian mixture model (GMM), which is designed using available well logs. In this method, first, the nonlocal means method estimates acoustic impedance around wells in terms of the similarity of local seismic records. The extrapolated multichannel impedance is then decomposed into impedance patches. Using patched data rather than a window or single trace for training samples to obtain the GMM parameters, which contain local lateral structural information, can provide more impedance structure details and enhance the stability of the inversion result. Next, the expectation maximization algorithm is used to obtain the GMM parameters from the patched data. Finally, we apply the alternating direction method of multipliers to solve the conventional Bayesian inference illustrating the role of regularization and construct the objective function using the GMM parameters. Therefore, the inversion results are compliant with the local structural features extracted from the borehole data. The synthetic and field data tests validate the performance of our method. Compared with other conventional inversion methods, our method shows promise in providing a more accurate and stable inversion result.

Geostatistical inversion can combine the advantages of two aspects: high horizontal resolution from deterministic seismic inversion and high vertical resolution from geostatistical simulation. Therefore, it becomes a new high resolution seismic inversion method. The method uses Bayesian inference to determine a posteriori probability density function of reservoir parameters based on seismic data, well logs and geostatistical information. Then, reservoir parameters are sampled and estimated using MCMC. The results have high resolution, including lithology, porosity, saturation and volume of shale et al., which can be used for reservoir evaluation, geological modeling and reservoir simulation. In this paper, the principle and workflow of geostatistical inversion based on Bayes-MCMC algorithm are firstly introduced. Then the integrated research of seismic inversion, geological modeling and reservoir simulation based on geostatistical inversion are applied to the development of Marine reservoirs in the South China Sea. This method improved the accuracy of geological modeling and efficiency of reservoir simulation.

In this study, we develop a 3D geostatistical seismic inversion method based on turning bands simulation and co-simulation to estimate acoustic impedance (AI) from seismic and well data. The proposed method uses an iterative approach based on cross-over concept in genetic algorithm optimization to perform global stochastic inversion. The objective function of the optimization algorithm is the measure of correlation coefficients between the modeled and observed seismic data. In the first iteration of the proposed algorithm, the seismic cubes corresponding to the AI realizations generated by the turning bands simulation are compared to the observed seismic data. Subsequently, the local areas of AI models producing the highest correlation coefficients to the observed seismic data are merged to construct a new supplementary cube that is further used as the secondary variable in the turning bands co-simulation for the next iteration. Generation of the seismic cubes and picking the best AI cube via cross-over genetic algorithm approach is performed iteratively until convergence to a constant correlation coefficient between the modeled and observed seismic data. The proposed method is applied to a synthetic dataset that includes 20 wells with known AI logs and 3D stacked seismic data. According to the results, the algorithm converges to a constant correlation coefficient after a few iterations. In addition, it is observed that employing multi-attribute analysis outputs (meta-attributes) during turning bands co-simulation in the initialization step would improve the final global correlation coefficient from 0.774 to 0.815.

Seismic inversion plays a very useful role in the detailed stratigraphic interpretation of migrated seismic volumes by enabling the estimation of reservoir properties over the complete volume. Traditional and machine learning-based seismic inversion workflows are limited to inverting each seismic trace independently of other traces to estimate impedance profiles, leading to lateral discontinuities in the presence of noise and large geologic variations in the seismic data. In addition, machine learning-based approaches suffer the problem of overfitting if there is only a small number of wells on which the model is trained. We have developed a two-pronged strategy to overcome these problems. We present a temporal convolutional network that models seismic traces temporally. We further inject the spatial context for each trace into its estimations of the impedance profile. To counter the problem of limited labeled data, we also present a joint learning scheme whereby multiple data sets are simultaneously used for training, sharing beneficial information among each of the sets. This results in improvement in the generalization performance on all data sets. We have developed a case study of acoustic impedance inversion using the open-source SEAM and Marmousi 2 data sets. Our evaluations show that our proposed approach is able to estimate impedance in the presence of noisy seismic data and a limited number of well logs with greater robustness and spatial consistency. We compare and contrast our approach to other learning-based seismic inversion methodologies in the literature. On SEAM, we are able to obtain an average mean squared error of 0.0476, the lowest among all other methodologies.

Seismic prediction of fluid and lithofacies distribution is of great interest to reservoir characterization, geologic model building, and flow unit delineation. Inferring fluids and lithofacies from seismic data under the framework of machine learning is commonly subject to issues of limited features, imbalanced data sets, and spatial constraints. As a consequence, an extreme gradient boosting-based workflow, which takes feature engineering, data balancing, and spatial constraints into account, is proposed to predict the fluid and lithofacies distribution by integrating well-log and seismic data. The constructed feature set based on simple mathematical operations and domain knowledge outperforms the benchmark group consisting of conventional elastic attributes of P-impedance and [Formula: see text] ratio. A radial basis function characterizing the weights of training samples according to the distances from the available wells to the target region is developed to impose spatial constraints on the model training process, significantly improving the prediction accuracy and reliability of gas sandstone. The strategy combining the synthetic minority oversampling technique and spatial constraints further increases the F1 score of gas sandstone and also benefits the overall prediction performance of all of the facies. The application of the combined strategy on prestack seismic inversion results generates a more geologically reasonable spatial distribution of fluids, thus verifying the robustness and effectiveness of our workflow.

Resolution improvement always presents as the crucial task in geologic inversion. Band-limited characteristics of seismic data and noise make seismic inversion complicated. Specifically, geologic inversion suffers from the deficiency of low- and high-frequency components. We have developed the fixed-point seismic inversion method to alleviate these issues. The problem of solving the objective function is transformed into the problem of finding the fixed point of the objective function. Concretely, a recursive formula between seismic signal and reflection coefficient is established, which is characterized by good convergence and verified by model examples. The error between the model value and the inverted value is reduced to approximately zero after a few iterations. The model examples show that in either case, that is, the seismic traces are noise-free or with a little noise, the model value can almost be duplicated. Even if the seismic trace is accompanied by moderate noise, optimal inverted results can still be obtained with our method. The initial model constraint is further introduced into the objective function to increase the low-frequency component of the inverted results by adding prior information into the target function. The singular value decomposition method is applied to the inversion framework, thus making a high improvement of antinoise ability. Finally, the synthetic models and seismic data are investigated following our method. The inverted results obtained from the fixed-point seismic inversion are compared with those obtained from the conventional seismic inversion, and it is found that the former has a higher resolution than the latter.

Attenuation factor (1/Q) is an important indicator that is sensitive to hydrocarbon-bearing reservoirs. In attenuative media, seismic wave velocity is complex and frequency dependent, and 1/Q appears in the imaginary part of wave velocity. Focusing on the real part of complex wave velocity in attenuative media, we present new-parameterized P- and S-wave velocities, in which a maximum P-wave attenuation factor ( $$1/Q_\mathrm{Pm}$$ ) is involved. Using solutions of Zoeppritz equations, we derive approximate PP- and PSV-wave reflection coefficients and anelastic impedances (AEI) as a function of $$1/Q_\mathrm{Pm}$$ , and we employ an empirical relationship to replace the S-wave attenuation factor with $$1/Q_\mathrm{Pm}$$ in the derivation of PSV-wave reflection coefficient. Using the derived reflection coefficients and AEI, we establish an inversion approach and workflow of utilizing PP- and PSV-wave seismic amplitudes to estimate elastic parameters (i.e. P- and S-wave moduli, density) and anelastic parameter (i.e. $$1/Q_\mathrm{Pm}$$ ). Inputting different frequency components of PP- and PSV-wave seismic wave amplitudes, a model-based least-squares (LS) algorithm is employed to implement the deconvolution for estimating the PP- and PSV-wave AEI of different dominant frequencies and incidence angles, and the joint inversion of PP- and PSV-wave AEI for elastic parameters and attenuation factor is implemented using a Newton method. We employ synthetic seismic data of different signal-to-noise ratios (SNR) to verify the stability and robustness of the established inversion approach, and we finally apply the approach to real datasets acquired over a hydrocarbon reservoir. It illustrates that stable elastic parameters and attenuation factors may be estimated; and combining the inverted elastic parameters and attenuation factor, we may realize the reliable identification and interpretation of hydrocarbon reservoirs.

Shale formation properties are crucial for the hydrocarbon production performance of unconventional reservoirs. Microseismic-induced guided waves, which propagate within the low-velocity shale formation, are an ideal candidate for accurate estimation of shale thickness, velocity, and anisotropy. A distributed acoustic sensing fiber deployed along the horizontal section of a monitor well can provide a high-resolution recording of guided waves excited by microseismic events during hydraulic fracturing operations. These guided waves manifest a highly dispersive behavior that allows for seismic inversion of the shale formation properties. An adaptation of the propagator matrix method is presented to estimate guided-wave dispersion curves, and its accuracy is validated by comparison to 3D elastic wavefield simulations. The propagator matrix formulation holds for cases of vertical transverse isotropy (VTI) as well. A sensitivity analysis of the theoretical dispersion relations of the guided waves indicates that they are mostly influenced by the thickness and S-wave velocity of the low-velocity shale reservoir. The VTI parameters of the formation are also shown to have an impact on the dispersion relations. These physical insights provide the foundation for a dispersion-based model inversion for a 1D depth-dependent structure of the reservoir and its surroundings. The inversion procedure is validated in a synthetic case and applied to the field records collected in an Eagle Ford hydraulic fracturing project. The inverted structure agrees well with a sonic log acquired several hundred meters away from the monitor well. Therefore, seismic inversion using guided-wave dispersion indicates promise to become a novel and cost-effective strategy for in situ estimation of reservoir structure and properties, which complements microseismic-based interpretation and production-related information.

Prestack seismic inversion usually suffers from a lower signal-to-noise ratio, which could result in unstable inversion results. The conventional multitrace lateral constrained inversion blurs steeply dipping layers, whereas the simple structural constrained inversion is affected by noise. To solve this issue, an inversion method with multiple constraints is proposed, which include (1) a local smoothing operator is used to suppress the inversion anomalies caused by data noise, (2) a difference operator is used to protect the stratum boundary, and (3) a structural dipping constraint is used to enhance the characterization of the possible dipping stratum. The multiconstraint inversion (MCI) method suppresses the inversion anomalies caused by data noise without blurring the stratum boundary. The effects of different constraints in the inversion process and the influence of noise on the inversion results are analyzed. In MCI, the regularization coefficient of each constraint operator is dynamically changed, thereby controlling the significance of each regularization term in the inversion. The proposed algorithm is tested on synthetic and field data, which demonstrate its effectiveness and improved accuracy on the inversion results.

We present an open-source Python package, lsforce, for performing single-force source inversions of long-period (tens to hundreds of seconds) seismic signals. Although the software is designed primarily for landslides, it can be used for any single-force seismic source. The package allows users to produce estimates of the three-component time series of forces exerted on the Earth by a landslide with postprocessing options to estimate the trajectory of its center of mass. Green’s functions for a user-selected 1D Earth model are obtained automatically from the Incorporated Research Institutions for Seismology Synthetics Engine webservice or can be computed for custom 1D Earth models using Computer Programs in Seismology. lsforce implements the two most commonly used source parameterizations: a fully flexible, high-resolution approach and a more stable but lower-resolution method of overlapping triangle sources. Regularization options include a blended zeroth-, first-, and second-order semiautomated Tikhonov regularization scheme, as well as additional optional constraints on start times, end times, and on the sum of forces. Uncertainty due to data selection can be assessed using either a leave-one-out approach or a modified jackknife technique that randomly excludes subsets of the data for multiple re-inversions. Numerous built-in plotting methods allow for easy quality control and assessment of results. In this article, we briefly outline the theory and methodology, describe our implementation, and demonstrate the usage of lsforce using the well-studied 28 June 2016 Lamplugh rock avalanche in Alaska. Despite the rapidly increasing prevalence of landslide single-force inversions in the landslide and seismology literature over the past decade, to our knowledge this is the first open-source code for performing such inversions.

Amplitude variation with offset (AVO) inversion is a widely used approach to obtain reliable estimates of elastic parameter in the fields of seismic exploration. However, the AVO inversion is an ill-posed problem because of the band-limited characteristic of seismic data. The regularization constraint plays an important role in improving inversion resolution. Total variation (TV) class regularization based on L $$_1$$ norm has been introduced in seismic inversion. But, these methods may underestimate the high-amplitude components and obtain low-resolution results. To tackle these issues, we propose to combine a smooth nonconvex regularization approach with adaptive individual weight-gain. Compared with the L $$_1$$ norm regularizers, the proposed smoothed nonconvex sparsity-inducing regularizers can lead to more accurate estimation for high-amplitude components. Different from previous regularization methods, the proposed approach also assigns different weight regularization parameters for different strata, which we call adaptive individual weight-gain strategy. To ensure sufficient minimization of the constructed objective function, a spectral Polak–Ribiere–Polyak conjugate gradient method with line search step size is used. Further, we prove that the proposed algorithm converges to a stationary point. The synthetic data tests illustrate that our approach has improved performance compared with the conventional TV class regularization methods. Field data example further verifies the higher resolution of the proposed approach.

Prestack seismic inversion can be regarded as an optimization problem, which minimizes the error between the observed and synthetic data under the premise of certain geological/geophysical a priori information constraints. It has been proved to be a powerful approach for reconstructing the subsurface properties and building the elastic parameter models (e.g., P- and S-wave velocity, and density). With respect to the specific expressions of a priori information, the starting model and regularization are expected to be the most widely used and indispensable constraints to reconstruct structural features and subsurface properties. The conventional prestack inversion (trace-by-trace) methods perform well when the geological structure of the target area is not too complex. However, due to the lack of lateral constraint, such trace-independent methods are inevitably limited by their capability of characterization (including accuracy, resolution, and robustness) in the case of geologically complex structures, such as tilted stratum and steep faults. The geological structure-guided constraint, herein referred to as the seismic slope attribute, can be exploited as a lateral constraint integrated into the prestack inversion algorithm. In this work, the seismic slope attribute is introduced to the amplitude variation with offset/angle inversion from two aspects, i.e., starting model building and regularization penalty. Firstly, using the seismic slope attribute, instead of the traditional manual interpreted geological horizons, as a constraint, the well-log data are interpolated to build the initial model. The interpolation algorithm is formulated as solving the inverse problem by using the shaping regularization method rather than the kriging-based algorithm. Secondly, by rotating the coordinate system according to the seismic slope attribute, the directional total variation regularization is used as a constraint to improve the resolution (in both vertical and horizontal directions) and lateral continuity of the inversion results. Finally, the proposed methods are applied to synthetic and real seismic data. Synthetic tests and field data applications demonstrate that the proposed method is capable of revealing complex structural features and achieving stabilized inversion of multi-parameters with less uncertainty.

The determination of subsurface elastic property models is crucial in quantitative seismic data processing and interpretation. This problem is commonly solved by deterministic physical methods, such as tomography or full-waveform inversion. However, these methods are entirely local and require accurate initial models. Deep learning represents a plausible class of methods for seismic inversion, which may avoid some of the issues of purely descent-based approaches. However, any generic deep learning network capable of relating each elastic property cell value to each sample in a seismic data set would require a very large number of degrees of freedom. Two approaches might be taken to train such a network: first, by invoking a massive and exhaustive training data set and, second, by working to reduce the degrees of freedom by enforcing physical constraints on the model-data relationship. The second approach is referred to as “physics-guiding.” Based on recent progress in wave theory-designed (i.e., physics-based) networks, we have developed a hybrid network design, involving deterministic, physics-based modeling and data-driven deep learning components. From an optimization standpoint, a data-driven model misfit (i.e., standard deep learning) and now a physics-guided data residual (i.e., a wave propagation network) are simultaneously minimized during the training of the network. An experiment is carried out to analyze the trade-off between two types of losses. Synthetic velocity building is used to examine the potential of hybrid training. Comparisons demonstrate that, given the same training data set, the hybrid-trained network outperforms the traditional fully data-driven network. In addition, we perform a comprehensive error analysis to quantitatively compare the fully data-driven and hybrid physics-guided approaches. The network is applied to the SEG salt model data, and the uncertainty is analyzed, to further examine the benefits of hybrid training.

The Indian National Gas Hydrate Program Expedition-01 in 2006 has discovered gas hydrate in the Mahanadi offshore basin along the eastern Indian margin. However, well-log analysis, pressure core measurements, and infrared anomalies reveal that gas hydrates are distributed as disseminated within the fine-grained sediment, unlike massive gas hydrate deposits in the Krishna-Godavari Basin. The 2D multichannel seismic section, which crosses holes NGHP-01-9A and 19B located at approximately 24 km apart, indicates a continuous bottom-simulating reflector (BSR) along it. We aim to investigate the prospect of gas hydrate accumulation in this area by integrating well-log analysis and seismic methods with rock-physics modeling. First, we estimate gas hydrate saturation at these two holes from the observed impedance using the three-phase Biot-type equation. Then, we establish a linear relationship between the gas hydrate saturation and the impedance contrast with respect to the water-saturated sediment. Using this established relation and impedance obtained from prestack inversion of seismic data, we produce a 2D gas hydrate-distribution image over the entire seismic section. The gas hydrate saturation estimated from resistivity and sonic data at well locations varies within 0%–15%, which agrees well with the available pressure core measurements at hole 19. However, the 2D map of gas hydrate distribution obtained from our method indicates that the maximum gas hydrate saturation is approximately 40% just above the BSR between the common-depth points of 1450 and 2850. The presence of gas-charged sediments below the BSR is one of the reasons for the strong BSR observed in the seismic section, which is depicted as low impedance in the inverted impedance section. Closed sedimentary structures above the BSR are probably obstructing the movements of free-gas upslope, for which we do not see the presence of gas hydrate throughout the seismic section above the BSR.

Reasonable low-wavenumber initial models are essential for reducing the nonuniqueness of seismic inversion. A traditional approach to estimate the low-wavenumber models of elastic parameters is well-log interpolation. However, complex geologic structures decrease the accuracy of this method. To overcome these challenges in building prior models, we have developed an interpolation method based on pattern-feature correlation (PFC) inspired by multiple-point geostatistics (MPG). In our interpolation method, we scan a stacked seismic profile using a predefined data template to obtain a geologic pattern around each node in the seismic profile. Each pattern is then converted into several filter scores with the filters defined in the MPG algorithm of the filter-based simulation. We calculate the correlation coefficients of the filter scores among different patterns for the various nodes and define them as PFCs. We construct the initial models from well-log data based on the weighted interpolation method, in which the weighting factors are precisely determined by the PFCs. We build the initial models using our method for synthetic and field data to demonstrate its effectiveness. To verify the validity of the initial models, we apply them to Bayesian linearized inversion. The accuracy of the interpolation and inversion results verifies the excellent performance of our interpolation method. Our method provides a novel and convenient approach that combines seismic and well-log data, which contributes to seismic exploration and geologic modeling.

Seismic inversion is a fundamental tool in geophysical analysis, providing a window into Earth. In particular, it enables the reconstruction of large-scale subsurface Earth models for hydrocarbon exploration, mining, earthquake analysis, shallow hazard assessment, and other geophysical tasks.

The conventional poststack inversion uses standard recursion formulas to obtain impedance in a single trace. It cannot allow for lateral regularization. In this paper, ID edge-preserving smoothing (EPS) filter is extended to 2D/3D for setting precondition of impedance model in impedance inversion. The EPS filter incorporates a priori knowledge into the seismic inversion. The a priori knowledge incorporated from EPS filter preconditioning relates to the blocky features of the impedance model, which makes the formation interfaces and geological edges precise and keeps the inversion procedure robust. Then, the proposed method is performed on two 2D models to show its feasibility and stability. Last, the proposed method is performed on a real 3D seismic work area from Southwest China to predict reef reservoirs in practice.

Seismic signal can be expressed by nonstationary convolution model (NCM) which integrates acoustic impedance (AI), attenuation factor (AF) and source wavelet (SW) into a single formula. Although it provides attractive potential to invert AI, AF and SW, simultaneously, effective joint inversion algorithm has not been developed because of the extreme instability of this nonlinear inverse problem. In this paper, we propose an alternating optimization scheme to achieve this nonlinear joint inversion. Our algorithm repeatedly alternates among three subproblems corresponding to AI, AF and SW recovery until changes in inverted models become smaller than the user-defined tolerances. Also, when we optimize one parameter, other two parameters are fixed. NCM is an explicit linear formula for AI; therefore, AI recovery is accomplished by linear inversion which is regularized by low-frequency model and isotropy total variation domain sparse constraints. However, NCM is a complicated nonlinear formula for AF. To facilitate the AF inversion, we propose a centroid frequency-based attenuation tomography method whose forward operator and observations are acquired from the time-varying wavelet amplitude spectra which is estimated according to Gabor domain factorization of NCM. SW is decoupled from NCM based on Toeplitz structure constraint, and we obtain an orthogonal wavelet transform domain sparse regularized SW inverse subproblem. Split Bregman technique is adopted to optimize AI and SW inverse subproblems. Numerical test and field data application confirm that the proposed nonstationary seismic inversion algorithm can stably generate accurate estimates of AI, AF and SW, simultaneously.

SUMMARYSeismic inversion is one of the most commonly used methods in the oil and gas industry for reservoir characterization from observed seismic data. Deep learning (DL) is emerging as a data-driven approach that can effectively solve the inverse problem. However, existing DL-based methods for seismic inversion utilize only seismic data as input, which often leads to poor stability of the inversion results. Besides, it has always been challenging to train a robust network since the real survey has limited labelled data pairs. To partially overcome these issues, we develop a neural network framework with a priori initial model constraint to perform seismic inversion. Our network uses two parts as one input for training. One is the seismic data, and the other is the subsurface background model. The labels for each input are the actual model. The proposed method is performed by log-to-log strategy. The training data set is first generated based on forward modelling. The network is then pre-trained using the synthetic training data set, which is further validated using synthetic data that have not been used in the training step. After obtaining the pre-trained network, we introduce the transfer learning strategy to fine-tune the pre-trained network using labelled data pairs from a real survey to acquire better inversion results in the real survey. The validity of the proposed framework is demonstrated using synthetic 2-D data including both post-stack and pre-stack examples, as well as a real 3-D post-stack seismic data set from the western Canadian sedimentary basin.

A study was conducted to use low-frequency amplitudes obtained through spectral decomposition as a direct hydrocarbon indicator within the geologically and seismically challenging area. An offshore data set from the Gulf of Thailand with well-known geology and good well control for both water and hydrocarbon bearing zones were used in this study. The rocks physics reveals similar P-impedance for gas and water-wet sands. Moreover, organic shales have similar Poisson’s ratio as of gas saturated zones. This makes differentiation of fluids and lithologies through post-stack seismic inversion and AVO analysis difficult. Reservoir wedge modelling, spectral decomposition of the synthetic and field data and analyses were conducted to understand the spectral decomposition response to thickness variation and reservoir fluids. Spectral decomposition was also applied on partial angle stacks (near, mid and far). Gas sands show bright amplitudes at low frequencies (from 10 to 15 Hz). However, spectral decomposition of the synthetic seismogram of the organic shales indicates that it consists of only relatively higher frequencies (greater than 25 Hz). Therefore, low-frequency amplitudes of spectral decomposition may help to identify hydrocarbon saturated zones.

L0-norm constrained sparse seismic inversion is an effective way to invert reflectivity series of underground rocks. A blocky impedance model with sharp formation boundaries can be estimated from s...

Seismic wavelet extraction always plays a central role in high-resolution seismic processing. Conventional methods assume that seismic data are stationary when a constant wavelet is considered, which ignores the time-varying characteristics of seismic wavelets. In reality, seismic data are nonstationary because of attenuation, scattering, and other physical processes during propagation, which means that the frequency spectrum of seismic signal changes from shallow to deep formations. We have developed a time-varying wavelet extraction method by using a highly energy-concentrated time-frequency representation technique. Time-varying wavelets are generated according to the local frequency spectrum at every instant. In addition, because the estimations of parameters for wavelet extraction are fully data-driven, the results of the proposed method are more accurate and suitable for the nonstationary nature of actual seismic data. Synthetic tests indicate the reliability and robustness of the proposed method, even under noise contamination. By applying the time-varying wavelet extracted using the proposed method to seismic inversion on a field data example, we obtain the deconvolution result with improved resolution and a better fit to the well-log reflectivity compared to that by using conventional wavelet extraction methods.

Detection of fracture properties can be implemented using azimuth-dependent seismic inversion for optimal model parameters in time or frequency domain. Considering the respective potentials for sensitivities of inversion resolution and anti-noise performance in time and frequency domain, we propose a more robust azimuth-dependent seismic inversion method to achieve fracture detection by combining the Bayesian inference and joint time–frequency-domain inversion theory. Both Cauchy Sparse and low-frequency constraint regularizations are introduced to reduce multi-solvability of model space and improve inversion reliability of model parameters. Synthetic data examples demonstrate that the frequency bandwidth of inversion result is almost the same for time, frequency and joint time–frequency domain inversion in seismic dominant frequency band using the noise-free data, but the frequency bandwidth in joint time–frequency domain is larger than that in time and frequency domains using low- signal-to-noise-ratio (SNR) data. The results of cross-correlation coefficients validate that the joint time–frequency-domain inversion retains both the excellent characteristics of high resolution in frequency-domain inversion and the advantage of strong anti-noise ability in time-domain inversion. A field data example further demonstrates that our proposed inversion approach in joint time–frequency domain may provide a more stable technique for fracture detection in fractured reservoirs.

Brittleness is a key property for horizontal well placement, fracture development, and production sustainability assessments of unconventional shale and gas plays. Conventionally, brittleness is estimated from Young’s modulus and Poisson’s ratio, and averaged. As Young’s modulus is a function of density, field wide brittleness estimation from Young’s modulus after seismic inversion may be compromised because density is the least accurate estimate compared to acoustic impedance and shear impedance. A simple quantitatively equivalent brittleness computation can be made from using Young’s modulus × density rather than directly from Young’s modulus. Further, it is preferable to use the average brittleness estimation from Young’s modulus × density and shear modulus × density rather than the conventional average, because Poisson’s ratio is an indicator of lithology rather than brittleness. Examples of brittleness and total organic carbon estimation first from well logs, then from rock parameters inverted from 3D land seismic data successfully mapped a brittle shale zone in a Silurian hot shale, a known source rock onshore Turkey and now targeted as oil shale play. The estimated brittleness and total organic carbon showed agreement with the core analysis made at another well on the same prospect.

A novel approach of unconventional reservoir stress evaluation is proposed to enhance the accuracy of fracture development prediction. Differential horizontal stress ratio can reflect the stress characteristic of fractured reservoir. To calculate the differential horizontal stress ratio more intuitionistic and simpler, we re‐derive its formula with Poisson's ratio and fracture density. Meanwhile, a new azimuthal PP‐wave amplitude versus offset equation based on Poisson's ratio and fracture density was derived for inverting above elastic parameters directly. Therefore, two steps are needed to realize stress evaluation. First, amplitude‐versus‐azimuthal angle inversion in the Bayesian framework in constraint of prior information such as well log data or rock physics information is executed for elastic and fracture parameters using pre‐stack angle gathers of different azimuth angles. The second procedure is to estimate differential horizontal stress ratio with Poisson's ratio and fracture density. Finally, a real data set is studied to test the new approach and the result demonstrated that the estimated differential horizontal stress ratio can reflect the stress property and it agrees with geological law and the new drilled well interpretation. Therefore, we can conclude that the combination of newly derived differential horizontal stress ratio and azimuthal PP‐wave amplitude versus offset equation in this study provides an available method for estimating the differential horizontal stress ratio of unconventional reservoirs, and the new approach can offer reliable geophysical information for stress evaluation.

Several approaches can be taken to conduct seismic data inversion. However, usually, these approaches are unable to distinguish vertical and horizontal heterogeneities. Seismic inversion through the singular value decomposition (SVD) method offers an adequate and simple way to improve these traditional inversion models. For this study P and S wave data were acquired at a site located in northeastern Mexico, obtaining their travel times. An inversion algorithm involving the SVD analysis was then developed to establish the seismic velocities of the lithological units. Further, images of compressional and shear-wave velocities ( Vpand Vs, respectively), Vp/ Vsratio, and elastic moduli (bulk, shear and Young's moduli, Lamé's constant, and Poisson's ratio) were obtained. These were compared with two geotechnical soundings positioned over a geophysical profile line. The geological features of the exposed units were recognized on some trenches. Further, seismic images demonstrated correlations with the thickness and distribution of the geological units. Unconsolidated sediments and fine-grain clastic rocks (in the Méndez formation) were clearly distinguished by the high velocity contrast. SVD seismic inversion has shown the ability to distinguish small physical heterogeneities of shallow geological units. Its application in civil engineering, hydrogeology, and to solve soil pollution problems can be relevant.

To improve the accuracy of inversion results, geological facies distributions are considered as additional constraints in the inversion process. However, the geological facies itself also has its own uncertainty. In this paper, the initial sedimentary facies maps are obtained by integrated geological analysis from well data, seismic attributes, and deterministic inversion results. Then the first iteration of facies-constrained seismic inversion is performed. According to that result and other data such as geological information, the facies distribution can be updated using cluster analysis. The next round of facies-constrained inversion can then be performed. This process will be repeated until the facies inconsistency or error before and after the inversion is minimized. It forms a new iterative facies-constrained seismic inversion technique. Compared with conventional facies-constrained seismic inversion, the proposed method not only can reduces the non-uniqueness of seismic inversion results but also can improves its resolution. As a consequence, the sedimentary facies will be more consistent with the geology. A practical application demonstrated that the superposition relationship of sand bodies could be better delineated based on this new seismic inversion technique. The result highly increases the understanding of reservoir connectivity and its accuracy, which can be used to guide further development.

Seismic inversion is a central step within the seismic reservoir characterization workflow. In seismic inversion, seismic amplitudes are inverted to predict the spatial distribution of the subsurface elastic properties. In subsequent steps of the geomodelling workflow, the inverted models are then converted into rock properties and facies. The methodology to perform the seismic inversion does have an impact on the predictions about the spatial distribution of the rock properties of interest. Each method has advantages and limitations depending on data quality, availability and the objective of the study. This work compares the application of deterministic and stochastic elastic inversion in a challenging real dataset. The deterministic approach is based on a sparse spike approximation, while the stochastic one is a global geostatistical seismic amplitude‐versus‐angle inversion. Both methods are applied to a real dataset acquired over a producing field located offshore Greece. Both the complex geological setting, where one target is close to seismic resolution, and data quality affect the performance of the seismic inversion methods. The results of both inversion methods are compared in terms of facies probability using Bayesian classification over the inverted elastic models and by comparing predictions to direct measurements at a blind well location. This application example shows the ability of deterministic inversion to retrieve an inverted solution with broader geological predictions which matched gross features in the wells. On the other hand, geostatistcal inversion was able to predict thin continous sand bodies, which correspond to the finer details at and below the seismic resolution of the observed data.

Among many seismic inversion methods, the sparse spike inversion for post-stack seismic data uses the migrated and stacked seismic data which is regarded as zero offset reflection seismic data in the case of normal incidence to extract reflectivity and impedance of underground rocks. The seismic reflectivity and impedance can reflect underground rocks’ lithology, petrophysical property, oil–gas possibility, and so forth. However, the common used post-stack seismic inversion adopts single trace in the process of inversion and completes the whole data cube’s inversion through trace by trace. It cannot use lateral regularization. Hence, the lateral continuity of single trace inversion result is poor. It is difficult to represent the lateral variation features of underground rocks. Based on the conventional sparse spike inversion, the nuclear norm of matrix in the matrix completion theory is introduced in the process of post-stack seismic inversion. At the same time, the strategy of multi-trace seismic data simultaneous inversion is used to carry out lateral regularization constraint. Numerical tests on 2D model indicate that the inversion results obtained from the proposed method can clearly represent not only the vertical variation features but also the lateral variation features of underground rocks. At last, the inversion results of real seismic data further show the feasibility and superiority of the proposed method in practical application.

Time-lapse (4D) seismic inversion aims to predict changes in elastic rock properties, such as acoustic impedance, from measured seismic amplitude variations due to hydrocarbon production. Possible approaches for 4D seismic inversion include two classes of method: sequential independent 3D inversions and joint inversion of 4D seismic differences. We compare the standard deterministic methods, such as coloured and model-based inversions, and the probabilistic inversion techniques based on a Bayesian approach. The goal is to compare the sequential independent 3D seismic inversions and the joint 4D inversion using the same type of algorithm (Bayesian method) and to benchmark the results to commonly applied algorithms in time-lapse studies. The model property of interest is the ratio of the acoustic impedances, estimated for the monitor, and base surveys at each location in the model. We apply the methods to a synthetic dataset generated based on the Namorado field (offshore southeast Brazil). Using this controlled dataset, we can evaluate properly the results as the true solution is known. The results show that the Bayesian 4D joint inversion, based on the amplitude difference between seismic surveys, provides more accurate results than sequential independent 3D inversion approaches, and these results are consistent with deterministic methods. The Bayesian 4D joint inversion is relatively easy to apply and provides a confidence interval of the predictions.

The accuracy of seismic inversion is affected by the seismic wavelet and time-depth relationship generated by the process of the seismic well tie. The seismic well tie is implemented by comparing the synthetic seismogram computed from well logs and the poststack seismogram at or nearby the borehole location. However, precise waveform matching between the synthetic seismogram and the seismic trace does not guarantee an accurate tie between the elastic properties contained represented by the seismic data and well logs. We have performed the seismic well tie using the impedance log and the impedance inverted from poststack seismic data. We use an improved dynamic time warping to align the impedance log and impedance inverted from seismic data. Our workflow is similar to the current procedure of the seismic well tie except that the matching is implemented between the impedance log and the inverted impedance. The current seismic well-tie converges if there is no visible changes for the wavelets and time-depth relationship in the previous and current tying loops. Similarly, our seismic well tie converges if there are no visible changes for the wavelets, inverted impedance, and time-depth relationship in the previous and current tying loops. The real data example illustrates that more accurate inverted impedance is obtained by using the new wavelet and time-depth relationship.

Geoscientific evidence shows that various parameters such as compaction, buoyancy effect, hydrocarbon maturation, gas effect and tectonic activities control the pore pressure of sub-surface geology. Spatially controlled geoscientific data in the tectonically active areas is significantly useful for robust estimation of pre-drill pore pressure. The reservoir which is tectonically complex and pore pressure is changing frequently that circumference motivated us to conduct this study. The changes in pore pressure have been captured from the fine-scale to the broad scale in the Jaisalmer sub-basin. Pore pressure variation has been distinctly observed in pre- and post-Jurassic age based on the current study. Post-stack seismic inversion study was conducted to capturing the variation of pore pressure. Analysis of low-frequency spectrum and integrated interval velocity model provided a detailed feature of pore pressure in each compartment of the study area. Pore pressure estimated from well log data was correlated with seismic inversion based result. Based on the current study one well has been proposed where pore pressure was estimated and two distinguished trends are identified in the study zone. The approaches of the current study were analysed thoroughly and it will be highly useful in complex reservoir condition where pore pressure varies frequently.

Seismic inversion methods are routinely used to estimate attributes such as P-impedance, S-impedance, density, P-wave and S-wave velocity, and elastic impedances from seismic and well log data. The...

An isotropic medium is generally assumed for reservoir characterization although it has been reported that velocity anisotropy often has a non-negligible impact. One of the difficulties in incorporating anisotropy is that the exact anisotropy parameters are generally unknown. To overcome the problem, a seismic-driven approach to Bayesian probability analysis is proposed, which defines the likelihood from seismic inversion data. Comparing this approach for lithology prediction to one based on well data, it is found that an isotropic well-data approach does not work well but is improved by taking anisotropy into account. A larger improvement is achieved by the seismic-driven approach which provides a robust result which is insensitive to the anisotropy assumption.