Quantitative Seismic Interpretation Research Articles

Application of the Vernik method for pore-pressure estimation in the Vaca Muerta Formation: Insights for geomechanical analysis and quantitative seismic interpretation in organic-rich mudrocks

Application of the Vernik method for pore-pressure estimation in the Vaca Muerta Formation: Insights for geomechanical analysis and quantitative seismic interpretation in organic-rich mudrocks

Lithofacies identification of deep coalbed methane reservoir based on high-resolution seismic inversion

During the exploration and development of deep coalbed methane (CBM), delineating the thickness of coal seam and lithofacies of the roof and floor is one of the major challenging tasks. In past attempts, the prediction methods of these parameters have been limited to the conventional inversion. However, the effect of coal shielding on adjacent reflecting layers restricts the identification of underlying sand effectively by conventional inversion. Also, the depth at which the deep CBM zone is located (1,500–2000 m) produces a significant overlap of P-wave impedance and Vp/Vs of sands and shale which increases classification uncertainty between these two lithofacies. We proposed a new workflow for high-precision quantitative seismic interpretation of deep CBM reservoir. Not only P-wave impedance but also GR is selected as the optimized attributes for lithofacies classification. To reduce the effect of strong reflection of coal seam and identifying thin coal layers, the seismic waveform indication inversion method is used to obtain high-resolution results of P-wave impedance and GR. It uses horizontal changes in seismic waveforms to reflect lithological assemblage characteristics for facies-controlled constraints. Then, Bayesian classification theory is used to achieve three-dimensional lithofacies classification with multi-source data. To improve the continuity and accuracy of the interpreted results, a Markov chain is applied in the Bayesian rule as the spatial prior constraint. A well-associated synthetic test and field data application in Ordos Basin demonstrates the accuracy of the proposed workflow. Compared with conventional inversion, the results of proposed workflow showed higher resolution and accuracy. By providing a new solution for the identification of roof and floor lithofacies of deep CBM reservoir, this workflow aims to contribute to the better exploration and development of deep CBM.

Characterization of seismic-scale petrofacies variability in the Arbuckle Group using supervised machine learning: Wellington Field, Kansas

The Arbuckle Group in southern Kansas has been investigated for carbon geosequestration-related studies. In this study, we evaluated the seismic-scale petrophysically defined facies variability of the Arbuckle Group at the Wellington Field, Kansas, using quantitative seismic interpretation and a supervised Random Forest classification approach. We first defined three petrophysics-based rock types (petrofacies) from core-derived porosity and permeability measurements using the flow-zone indicator approach. Then, using the artificial neural network, we classified these petrofacies in noncored intervals. We observed that petrofacies 1 corresponds to medium and coarse-grained dolomitic packstone, wackestone, and dolomitic breccia with up to 8% porosity and D-scale permeability values. Whereas petrofacies 2 and 3 correspond to argillaceous and fine-grained micritic dolomites and dolomitic mudstones with lower permeability values for a given porosity, with respect to petrofacies 1. Using the common reflection-point gathers, we performed prestack seismic inversion and calculated various amplitude-variation-with-offset (AVO) attribute volumes. We used these elastic properties and AVO attribute volumes as input for estimating supervised seismic-scale 3D petrofacies and petrofacies probability volumes using the Random Forest algorithm. Results reveal the complex distribution of the petrofacies in the candidate injection and baffle zones in the study area, where petrofacies 1 is mainly prevalent within the lower and upper portions of the Arbuckle group, whereas petrofacies 2 and 3 are mainly present in the middle Arbuckle interval. The workflow we present through this study provides the spatial variability of facies distribution that is reflective of the actual lithology and petrophysical properties of the Arbuckle group in the study area with limited well control.

Prism-shaped rock-physics template

Despite the emergence of statistical and intelligent approaches for quantitative seismic interpretation in recent years, rock-physics templates (RPTs) are still preferred because of their simplicity and easy implementation. RPTs have been introduced as a fundamental tool for lithology and fluid discrimination based on well-log and seismic data, which have already been proven in terms of accuracy and reliability. Considering the demand for comprehensive RPTs and improving their efficiency, I develop a novel 3D prism-shaped template for lithology and fluid discrimination called a prism-shaped RPT (P-RPT). For this, a theoretical methodology for designing conventional RPT is introduced with the aim of bounds and hybrid rock-physics models. Next, some novel triangular and rectangular ternary and binary templates are designed and connected to one another to build a prism for fluid discrimination and lithology identification. Two ternary charts are used for fluid and lithology, incorporating the P- and S-wave velocity ratios and lambda-mu parameters. Furthermore, binary charts, including acoustic impedance, Lamé parameters, and porosity, are designed and modified from the literature for fluid and lithology identification. Next, the obtained templates are successfully validated on blind data sets (ultrasonic, well logging, and seismic data) in different reservoirs with various lithologies and fluid types. The results indicate that the P-RPT could integrate the available RPTs into a 3D diagram and give a reliable framework for applications in seismic interpretation. User friendliness and generalizability are the most prominent advantages of this template for detecting fluid type and lithology based on well logs and seismic inversion, the results of which can be interpreted on a single chart rather than analyzing the data with different templates. The methodology and framework for implementing and generating templates are thoroughly explained in theory and practice to localize P-RPT or update the new RPT for another region.

Marchenko imaging assisted by vertical seismic profiling data for land seismic data in the Middle East

Attenuating interference from internal multiples has challenged seismic data imaging in the Middle East basins. The challenge results from the strong short-period internal multiples that exhibit nearly indistinguishable characteristics from the primaries reflected from the underlying reservoirs due to predominantly horizontal strata and occasional low-relief structures, as indicated in the Jurassic formations in Kuwait. To address the internal-multiple issues, multiple prediction followed by adaptive subtraction is the most common approach in the industry. However, due to the similarities between primaries and multiples, applying adaptive subtraction poses a high risk of primary-amplitude damage, preventing quantitative seismic data interpretation. Therefore, we examine the Marchenko method that retrieves Green’s functions from surface seismic data for target-oriented imaging without applying adaptive subtraction. Marchenko imaging has produced promising results on several offshore seismic data sets, but an onshore application is still needed. To better understand the effects of internal multiples and implement Marchenko imaging, we perform integrated analysis through well-log, vertical seismic profiling (VSP), and seismic data from a hydrocarbon field in Kuwait. In addition, we use VSP data to cross-check the retrieved Green’s functions and estimate the scaling factor of the Marchenko method. The results indicate that (1) the poor imaging at the center of the field is due to the destructive interference of internal multiples, (2) the reverberation of internal multiples between the evaporite formations of the overburden is the most likely candidate that affects the seismic images of the Jurassic reservoirs, (3) the retrieved Green’s functions conform to the recorded Green’s functions from VSP data, and (4) Marchenko imaging provides a means to improve the seismic images of the Jurassic formations in Kuwait.

Deriving mineral moduli of the noncarbonate fraction in a marly chalk reservoir using petrophysical logging data and an isoframe model

Knowledge of the elastic properties of clays and clay-rich sediments is essential when performing quantitative seismic interpretation and geomechanical modeling of reservoirs where they are present. Such is the case for the marly chalk reservoirs of Lower Cretaceous age in the Danish North Sea. We develop a new method to derive the elastic properties of the clay-rich insoluble residue (IR) fraction in marly chalks, using petrophysical logs and rock physical modeling. We calculate compressional ( M), shear ( G), and bulk ( K) moduli of the formation along the borehole using the density and sonic (shear and compressional) logs and model the elastic properties of minerals using the isoframe (IF) rock-physics model. In our model, the marly chalk is composed of two materials: calcite, with well-known elastic properties, and the IR fraction, whose elastic properties are the sought variable. We systematically vary the assumed elastic properties of the IR fraction, each time achieving equality between the measured and modeled elastic moduli solving for the IF values of calcite ([Formula: see text]) and IR fraction ([Formula: see text]) for each of the three elastic parameters one at a time, deriving the modeled values for the other two. We add up the error between the modeled and logged values for each set of assumed elastic properties of the IR fraction. We adopt the values of [Formula: see text] and [Formula: see text] that minimize the compound error as the actual elastic properties of the IR fraction. We run the algorithm in the Boje-2C and Valdemar-2H wells in different stratigraphic units of the Lower Cretaceous: the Fanø Formation and the marly chalk of the Tuxen and Sola formations. Elastic properties derived for the IR fraction compare well to data for wet clays from ultrasonic velocity laboratory tests. In a clean chalk section (almost 100% calcite), the model outputs values similar to calcite.

Interpretable machine learning model for shear wave estimation in a carbonate reservoir using LightGBM and SHAP: a case study in the Amu Darya right bank

The shear wave velocity (Vs) is significant for quantitative seismic interpretation. Although numerous studies have proved the effectiveness of the machine learning method in estimating the Vs using well-logging parameters, the real-world application is still hindered because of the black-box nature of machine learning models. With the rapid development of the interpretable machine learning (ML) technique, the drawback of ML can be overcome by various interpretation methods. This study applies the Light Gradient Boosting Machine (LightGBM) to predict the Vs of a carbonate reservoir and uses the Shapley Additive Explanations (SHAP) to interpret the model. The application of ML in Vs estimation normally involves using conventional well-log data that are highly correlated with Vs to train the model. To expand the model’s applicability in wells that lack essential logs, such as the density and neutron logs, we introduce three geologically important features, temperature, pressure, and formation, into the model. The LightGBM model is tuned by the automatic hyperparameter optimization framework; the result is compared with the Xu-Payne rock physics model and four machine learning models tuned with the same process. The results show that the LightGBM model can fit the training data and provide accurate predictions in the test well. The model outperforms the rock physics model and other ML models in both accuracy and training time. The SHAP analysis provides a detailed explanation of the contribution of each input variable to the model and demonstrates the variation of feature contribution in different reservoir conditions. Moreover, the validity of the LightGBM model is further proved by the consistency of the deduced information from feature dependency with the geological understanding of the carbonate formation. The study demonstrates that the newly added features can effectively improve model performance, and the importance of the input feature is not necessarily related to its correlation with Vs

Prediction of Reflection Seismic Low-Frequency Components of Acoustic Impedance Using Deep Learning

The unreliable prediction of the low-frequency components from inverted acoustic impedance causes uncertainty in quantitative seismic interpretation. To address this issue, we first calculate various seismic and geological attributes that contain low-frequency information, such as relative geological age, interval velocity, and integrated instantaneous amplitude. Then, we develop a method to predict the low-frequency content of seismic data using these attributes, their high-frequency components, and recurrent neural networks. Next, we test how to predict the low-frequency components using stacking velocity obtained from velocity analysis. Using all the attributes and seismic data, we propose a supervised deep learning method to predict the low-frequency components of the inverted acoustic impedance. The results obtained in both synthetic and real data cases show that the proposed method can improve the prediction accuracy of the low-frequency components of the inverted acoustic impedance, with the best improvement in a real data example of 57.7% compared with the impedance predicted using well-log interpolation.

Quantitative characterization of tight gas sandstone reservoirs using seismic data via an integrated rock-physics-based framework

Seismic characterizing of tight gas sandstone (TGS) reservoirs is essential for identifying promising gas-bearing regions. However, exploring the petrophysical significance of seismic-inverted elastic properties is challenging due to the complex microstructures in TGSs. Meanwhile, interbedded structures of sandstone and mudstone intensify the difficulty in accurately extracting the crucial tight sandstone properties. An integrated rock-physics-based framework is proposed to estimate the reservoir quality of TGSs from seismic data. TGSs with complex pore structures are modeled using the double-porosity model, providing a practical tool to compute rock physics templates for reservoir parameter estimation. The VP/VS ratio is utilized to predict the cumulative thickness of the TGS reservoirs within the target range via the threshold value evaluated from wireline logs for lithology discrimination. This approach also facilitates better capturing the elastic properties of the TGSs for quantitative seismic interpretation. Total porosity is estimated from P-wave impedance using the correlation obtained based on wireline log analysis. After that, the three-dimensional rock-physics templates integrated with the estimated total porosity are constructed to interpret microfracture porosity and gas saturation from velocity ratio and bulk modulus. The integrated framework can optimally estimate the parameters dominating the reservoir quality. The results of the indicator proposed based on the obtained parameters are in good agreement with the gas productions and can be utilized to predict promising TGS reservoirs. Moreover, the results suggest that considering microfracture porosity allows a more accurate prediction of high-quality reservoirs, further validating the applicability of the proposed method in the studied region.

Improved delineation of the Gassum Formation reservoir zones using seismic impedance inversions: implications for exploiting the Stenlille aquifer gas storage facility as a CO 2 storage demonstration site, onshore Denmark

A quantitative seismic interpretation of the Gassum Formation at the onshore aquifer gas storage near the Danish town of Stenlille is presented with its implications for exploiting the gas storage facility as a potential CO 2 demonstration site. Our objective was to better outline the reservoir heterogeneity of the Gassum Formation based on a 3D seismic volume and 20 wells available from the Stenlille gas storage facility. We derived new absolute and relative P-impedance inversion products, which are useful for delineating lithological distributions of thin sandstone reservoirs and shaly beds within the Gassum Formation. Our results broadly agree with previously published seismic interpretations, and build upon these by the identification of deviations in parts of the Gassum interval that should be considered in any subsequent development of a static reservoir model. Hence, this work is an important contribution to the planning of drilling new CO 2 injection wells as part of the development and management of the Stenlille CO 2 storage demonstration site. Nevertheless, we also recommend a modern seismic reprocessing of the 3D seismic data combined with newly acquired 2D data from the Stenlille structure as input into further quantitative seismic interpretation studies to refine reservoir characterization and reduce associated uncertainties. Supplementary material: video is available at https://doi.org/10.6084/m9.figshare.c.6662413

Rock physics modeling and quantitative seismic interpretation workflow for organic-rich mudrocks

A quantitative seismic interpretation workflow based on rock physics was developed for an organic-rich mudrock formation in order to estimate porosity, and organic matter and clay contents from seismically-derived acoustic and shear impedances. This deterministic workflow consists of: developing a rock physics model that accurately estimates the elastic properties of the formation; finding inter-relationships between the formation's petrophysical properties; and validating the model after upscaling and applying it to seismically-derived acoustic and shear impedances. By analyzing wireline data from this organic-rich mudrock formation, we find that the P-wave velocity at fixed porosity may vary by as much as 1.0 km/s, while the respective variation in the S-wave velocity reaches 0.5 km/s. A robust discriminator for velocity appears to be, in addition to the total porosity, the volumetric fraction of the soft components of the solid matrix where the kerogen is included, namely the sum of the clay and kerogen. The ensuing velocity-porosity-mineralogy relations are accurately described by the soft-sand rock physics model. Thus, an attempt to interpret seismically-derived impedance volumes based on rock physics modeling is performed after simplifying the rock's petrophysical variables by building site-specific internal relations between mineral's volume fractions that comprise the rock's solid matrix. This workflow helps in hydrocarbon exploration by identifying favorable areas with relatively high porosities and organic matter content.

Prediction of reservoir physical parameters based on adaptive step size stochastic inversion

Reservoir physical parameter prediction is the ultimate goal of the quantitative seismic interpretation. Rock physics inversion is a method to obtain reservoir physical parameters from seismic elastic properties and the accuracy of predicted physical parameters depends largely on the inversion algorithm. We proposed an adaptive step size stochastic algorithm. In the algorithm, the step size of the parameters to be inverted are not fixed, but adaptive. Firstly, the parameters to be inverted are sampled according to their prior probability density distribution, and the error of each sampling point is calculated. Then select the sampling points with small errors and calculate their probability density distribution, and then encrypt the sampling point according to the new probability density distribution. Repeat the sampling several times, and finally get the optimal solution to the parameters to be inverted. Compared with the conventional given step size algorithm, the adaptive step size algorithm is more accurate. The prediction of reservoir physical parameters based on adaptive step size stochastic inversion is applied to sandstone reservoir, obtained reservoir physical parameters, such as porosity, water saturation, clay content, and their uncertainty distribution.

Fault Interior Structure Index Construction and Quantitative Seismic Interpretation of Fault Facies

Fault Interior Structure Index Construction and Quantitative Seismic Interpretation of Fault Facies

Quantitative Seismic Interpretation of Reservoir Parameters and Elastic Anisotropy Based on Rock Physics Model and Neural Network Framework in the Shale Oil Reservoir of the Qianjiang Formation, Jianghan Basin, China

Quantitative estimates of reservoir parameters and elastic anisotropy using seismic methods is essential for characterizing shale oil reservoirs. Rock physics models were established to quantify elastic anisotropy associated with clay properties, laminated microstructures, and bedding fractures at different scales in shale. The inversion schemes based on the built rock physics models were proposed to estimate reservoir parameters and elastic anisotropy using well log data. Based on the back propagation neural network framework, the obtained rock physical inversion results were used to establish the nonlinear models between elastic properties and reservoir parameters and elastic anisotropy of shale. The established correlations were applied for quantitative seismic interpretation, converting seismic inversion results to the reservoir parameters and elastic anisotropy to characterize the shale oil reservoir comprehensively. The predicted elastic anisotropy of the shale matrix reflects the lamination degree and the mechanical properties of the shale, which is critical for the effective implementation of hydraulic fracturing. The calculated elastic anisotropy of the shale provides more accurate models for seismic modeling and inversion. The obtained bedding fracture parameters provide insights into reservoir permeability. Therefore, the proposed method provides valuable information for identifying favorable oil zones in the study area.

High-resolution seismic impedance inversion integrating the closed-loop convolutional neural network and geostatistics: an application to the thin interbedded reservoir

Abstract Seismic impedance inversion is one of the key techniques for quantitative seismic interpretation. Most conventional post-stack seismic impedance inversion approaches are based on the linear theory, whereas the relationship between seismic response and impedance is highly nonlinear. Thus, it is challenging to implement conventional inversion methods to obtain high-resolution impedance for reservoir investigation. Convolutional neural network (CNN), a superior deep neural network, has a strong learning ability, which can learn from data and establish complex nonlinear mapping. However, CNN-based methods are generally heavily dependent on amounts of labeled data. Hence, an alternative seismic inversion approach is proposed that combines the closed-loop CNN and geostatistics. The closed-loop CNN is less dependent on labeled data, characterized by utilizing labeled data and unlabeled data simultaneously to train the neural network. The two subnets represent forward modeling and inversion respectively, constraining each other during the neural network training. Geostatistics can be used to enrich the training data for neural network training, taking into account geological and geophysical prior information. Synthetic data testing reveals that the proposed inversion scheme can obtain more reasonable results benefiting from labeled training data augmentation. The proposed inversion scheme is applied to the field data for identifying thin interbedded reservoir within delta depositional system. The predicted results obtained by the proposed inversion scheme are consistent with well log data and geological settings, offering insights into reservoir characterization and hydrocarbon identification.

Joint two-step inversion of porosity and saturation based on the Biot/squirt model with multiphase fluids

It is of great importance to predict reservoir petrophysical properties, such as porosity and saturation based on elastic properties, including density and wave propagation velocity, through quantitative seismic interpretation. A joint two-step inversion (JoiTSI) method is proposed for predicting porosity and saturation with a forward petrophysical model. As the forward model, the Biot/squirt model with multiphase fluids (gas, oil, and water) describes the nonlinear relationships between petrophysical and elastic properties. The first step of the two-step inversion is to invert porosity and saturation via a semianalytical approximation approach. The second step is a joint inversion at high porosities where porosity and saturation are simultaneously optimized using a hybrid genetic algorithm. In these steps, maximum a posteriori estimations are performed based on normal prior distributions, where the posterior distributions may differ between two steps because of the potential variability in reservoir properties. The numerical tests indicate that this JoiTSI method is more efficient and more accurate than the traditional inversion methods. In addition, Poisson’s ratio, P-wave impedance, and S-wave impedance are found to give better inversion results than those based on more commonly used attributes (density, P-wave impedance, and S-wave impedance). In the oilfield seismic example, the inversion accuracy of porosity and saturation is verified by actual well logs, and the major oil and water layers are correctly identified.

A multivariate statistical approach in correlating the acoustic properties with petrophysics and mineralogy on sandstones

SUMMARYWe examined the effects of the petrophysical and mineralogical variables on the acoustic properties of Upper Assam sandstones. All parameters were evaluated in the laboratory using recognized standard laboratory methods. Compressional wave velocity was measured in the laboratory using piezoelectric transducers of 54 kHz by using the ultrasonic-through transmission technique. Single parameter correlations among bulk density, porosity, permeability and mineralogy with compressional wave velocity showed that the compressional wave velocity correlated inversely with porosity, permeability and feldspar content and directly with bulk density and quartz content. For instance, a plug with a higher amount of feldspar content showed a corresponding decrease in compressional wave velocity. Similarly, higher quartz content showed a higher compressional wave velocity. Nevertheless, to depict a clear correlation among different parameters, a multiparameter analysis was performed. It was observed that the coefficient of determination improved from 0.596 to 0.899 when compressional wave velocity was modelled in terms of bulk density, porosity, quartz and feldspar content collectively, rather than when compressional wave velocity was modelled as a function of porosity alone. Hence, our study suggests that multiple parameters exhibit a coherent influence on compressional wave velocity. Therefore, a multivariate statistical approach involving petrophysical and mineralogical influences would be more realistic. Furthermore, major mineral fractions of sandstone rock type, that is quartz and feldspar fractions were varied for a range of porosity to determine the variations in compressional wave velocity for different saturation conditions. Capturing the fluctuations in compressional wave velocity within the geological constraints will aid in possible reservoir characterization away from the well-control or beyond the available data range. The integrated approach can result in more accurate and precise rock physics models that may help to infer key reservoir parameters. The developed models can be used in quantitative seismic interpretation for reservoir characterization and to identify prospective reservoirs.

Rock Physics Model Provides Insight Into Reservoir Characterization

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 30359, “Reservoir Characterization Supported by Rock Physics Diagnostics,” by Stephan Gelinsky, Shell. The paper has not been peer reviewed. Copyright 2021 Offshore Technology Conference. Reproduced by permission. Rock physics models are often perceived as too complicated and dependent on too many difficult-to-measure parameters to be of practical use for reservoir characterization. In the complete paper, the author demonstrates how careful calibration of a common and simple rock physics model can provide valuable insights into the reservoir and seal elastic properties such as function of porosity, fluid, and mineral properties and net-to-gross. The model can be used for quantitative seismic interpretation of reservoirs with variable net-to-gross and porosity away from well control. Methodology Rock physics describes the application of fundamental physical principles to establish the elastic properties of porous rocks as a function of their mineral composition, pore fill, and microstructure and texture. Once a suitable rock physics model is calibrated locally, the use of seismic data to estimate reservoir rock and fluid properties from the elastic rock properties is possible—specifically, from the observed seismic amplitudes and if seismic data were inverted from the seismically derived acoustic and shear impedances. The Hashin-Shtrikman (HS) effective medium bounds can be used to characterize both sandstones and shales. While the application of this model for clean sandstones is straightforward and, thus, is not included in this synopsis, the application for shales is more complex.

Quantitative seismic interpretation of the Gassum Formation at the Stenlille aquifer gas storage

Quantitative seismic interpretation of the Gassum Formation at the Stenlille aquifer gas storage

Double-scale supervised inversion with a data-driven forward model for low-frequency impedance recovery

Low-frequency information is important in reducing the nonuniqueness of absolute impedance inversion and for quantitative seismic interpretation. In traditional model-driven impedance inversion methods, the low-frequency impedance background is from an initial model and is almost unchanged during the inversion process. Moreover, the inversion results are limited by the quality of the modeled seismic data and the extracted wavelet. To alleviate these issues, we have investigated a double-scale supervised impedance inversion method based on the gated recurrent encoder-decoder network (GREDN). We first train the decoder network of GREDN called the forward operator, which can map impedance to seismic data. We then implement the well-trained decoder as a constraint to train the encoder network of GREDN called the inverse operator. Besides matching the output of the encoder with broadband pseudowell impedance labels, data generated by inputting the encoder output into the known decoder match the observed narrowband seismic data. The broadband impedance information and the already-trained decoder largely limit the solution space of the encoder. Finally, after training, only the derived optimal encoder is applied to unseen seismic traces to yield broadband impedance volumes. Our approach is fully data driven and does not involve the initial model, seismic wavelet, and model-driven operator. Tests on the Marmousi model illustrate that our double-scale supervised impedance inversion method can effectively recover low-frequency components of the impedance model, and we determine that low frequencies of the predicted impedance originate from well logs. Furthermore, we apply the strategy of combining the double-scale supervised impedance inversion method with a model-driven impedance inversion method to process field seismic data. Tests on a field data set indicate that the predicted impedance results not only reveal a classic tectonic sedimentation history but also match the corresponding results measured at the locations of two wells.