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The primary aim of this research is to enhance seismic data interpretation and CO 2 monitoring by utilizing seismic inversion techniques based on the simulated annealing method. Simulated annealing is a global optimization technique employed for inverting seismic data and provides better results as compared with local optimization-based inversion. This methodology is implemented in the Utsira Formation, located at a depth of 1000 m within the Sleipner Field, Norway. The study encompasses the analysis of three sets of time-lapse seismic data, first from 1994 (pre-injection), followed by surveys in 1999 and 2001, corresponding to the injection of 2.35 million tonnes and 4.26 million tonnes of CO 2 , respectively. Firstly, synthetic data is used to check the reliability of the algorithm followed by real data application. This process starts by performing the inversion analysis on the synthetic data which shows a decrease in the impedance values observed at the injection site whereas the seismic amplitude increases. The qualitative as well as quantitative analysis depicts that the algorithm works satisfactorily. The same process is applied to the real data from the Sleipner field. Acoustic impedances are calculated using a simulated annealing-based inversion scheme for the pre-injection case in 1994 and post-injection scenarios in 1999 and 2001. Because of the presence of injected CO 2 in the years 1999 and 2001, a low impedance zone that ranged from 2000 m/s*g/cc to 2400 m/s*g/cc appeared at the time interval of 0.85–1.10sec. The interpretation of the inverted impedance section and seismic attribute analysis show no signature of CO 2 leakage. The results indicated that the inverted section which is derived from the SA optimization technique shows very clear CO 2 information offering a more realistic representation with enhanced resolution of the CO 2 plume and its migratory paths. • The study developed a methodology for enhanced CO 2 monitoring of time-lapse seismic data. • The methodology uses seismic inversion based on simulated annealing, and compared with traditional methods. • The developed method provides very high-resolution subsurface information and hence CO2 movement. • Finally, the methodology is applied to the Sleipner data and CO 2 leakage has been detected with a very high confidence level. [ABSTRACT FROM AUTHOR]

The Brazilian pre-salt carbonate province is a scientific frontier that has been subject of extensive research and academic discussion. Santos basin carbonates show that reservoir quality is strongly conditioned by the characteristics of the depositional environment and its associated diagenetic history and alterations. Dolomitization and silicification are two of the most common diagenetic processes in Brazilian carbonates, and the combination of sedimentary and diagenetic complexity leads to a much more heterogeneous reservoir, especially complicated for geoscientists trying to mimic these geological features in terms of static and dynamic modelling. This study proposes a methodology for seismic derived diagenetic properties calculation through seismic quantitative interpretation and classification of inverted P- and S-impedance volumes applied to Brazilian Pre-Salt carbonate reservoirs. Our proposition is to establish relations between petroelastic properties and diagenetic features, first in the well-log domain and, later, in the seismic domain through different quantitative probabilistic techniques. Using the available seismic data and probabilistic approaches through stochastic inversion and Bayesian facies classification, we can interpret the occurrence of diagenetic silica and dolomite in 3D volumes. The volumetric analysis generated through the presented methodology can provide much more insights into the sedimentary history of the field, especially for static and dynamic porosity and permeability modelling. • Diagenetic features are a major concern for porosity modelling in pre-salt carbonates. • Most diagenetic studies are thin section limited and simply extrapolate facies away from wells without lateral constraints. • We calculated dolomitization and silicification volumes that are laterally mappable. • 3D diagenetic volumes can provide valuable inputs for petrophysical and geological modelling. [ABSTRACT FROM AUTHOR]

The fluid identification of fracture media is susceptible to factors such as observation data, fluid sensitivity parameters, and inversion algorithms. However, appropriate nonlinear algorithms can be applied to reduce errors generated during the inversion process, thus helping to improve the reliability of fluid identification. In this paper, analysis and comparison are made to various reflection coefficient approximate formulas, of which the applicability and accuracy are demonstrated in fluid-saturated poroelastic media, and the objective function is constructed further from Russell's reflection coefficient formula. In this sense, the genetic algorithm (GA) can achieve the pre-stack inversion of seismic waves. Based on the inversion results such as P-, S-wave velocity, and density, the Russell fluid indicator, Poisson's ratio, and Poisson impedance are combined to identify the water-, gas-, and oil-bearing fracture. Finally, the applicability and reliability of the method are demonstrated through numerical calculations in some regions of Marmousi2 model and the field data. The results show that the seismic data can be preliminarily analyzed by the inversion method, followed by combining the Russell fluid indicator, Poisson's ratio, and Poisson impedance, effectively reducing the multiplicity of the inversion results and improving the reliability of fluid identification in fractured media. • Proper fluid factors can effectively reduce the multiplicity of inversion results. • The objective function is formed with Russell's reflection coefficient. • Combining three parameters can improve the reliability of fluid identification in fractured media. [ABSTRACT FROM AUTHOR]

Estimating acoustic impedance from seismic data is a crucial step in reservoir characterization. While data-driven impedance inversion based on deep learning has shown promising results, it relies heavily on extensive well logs for labeling, which is often impractical in many exploration scenarios. Recently, the zero-shot or few-shot learning performance of Pretrained Foundation Models like Generative Pre-trained Transformer (GPT) and Mask Auto Encoder (MAE) has highlighted that knowledge learned from vast amounts of unlabeled data can be transferred to downstream tasks with minimal labeled data. However, applying Transformer-based representation learning models to 3D seismic data inversion poses three challenges: (1) Computational and memory constraints due to the high-dimensional nature of the data; (2) Difficulty in extracting fine-grained image features using Transformers, hampering high-frequency impedance inversion; (3) Fixed input size in Transformers, leading to inversion artifacts. In this work, we introduce the Seismic Mask Auto Encoder (SeisMAE), a Transformer-based representation model tailored for the inversion of 3D seismic data. It incorporates three key components: (1) Aggregated dimensionality reduction encoding to handle redundancy in seismic data, significantly improving computational efficiency; (2) Multi-scale self-attention feature fusion to enhance the model's capacity for low-level feature representation; and (3) A stitching decoding strategy to eliminate inversion stitching artifacts. Experimental validations highlight the efficacy of our approach. On the synthetic SEAM I dataset, we demonstrate the effectiveness of each component and SeisMAE's superior performance. For real-world data on The Netherlands F3, SeisMAE delivers reliable inversion outcomes with only four labeled examples. We compared SeisMAE against various inversion techniques, including 1D Convolutional Neural Network (1D-CNN), UNet-based, HRNet-based, and TransInver, where SeisMAE exhibited significant advantages. [ABSTRACT FROM AUTHOR]

For the purpose of reservoir modelling, precise porosity estimation is vital as it directly influences the storage capacity, fluid flow dynamics, and overall productivity of the reservoir. The computation of porosity is a key component of reservoir characterization. The Poro-Acoustic Impedance (PAI), a seismic inversion attribute, has proven to be effective for porosity estimation in hydrocarbon reservoirs. PAI, an extended version of Acoustic Impedance (AI), incorporates porosity information directly, enhancing its utility in forward modelling and seismic data inversion. In this study, offshore oil resources in Iran were examined, focusing on two components: sandstone and carbonate. The results of AI and PAI were compared, indicating that PAI is a suitable attribute for estimating porosity. The correlation between porosity and AI was −45%, while it was −74% with PAI. Moreover, the synthetic seismogram created using PAI aligns more closely with real seismograms. Porosity was estimated using both AI and PAI, with a 72% correlation between the porosity estimated using AI and the actual porosity. However, the correlation increased to 78% when using PAI. Furthermore, the porosity section was calculated using both AI and PAI, concluding that the PAI porosity section aligns more closely with the porosity log and provides a greater contrast in low porosity zones compared to AI. Given that porosity is incorporated into the PAI formula, the PAI porosity section and inversion results can be used as an indicator for evaluating the hydrocarbon capacity of the reservoir. The findings of this research suggest that PAI is an effective attribute for porosity estimation, bridging the gap between seismic data and porosity estimation, thereby enhancing our understanding and exploration of the reservoir. • Porosity estimation using PAI has better accuracy and precision. • The porosity section obtained using PAI is more consistent with the porosity log. • The effect of porosity is directly involved in the inversion procedure. • The PAI formula is simple and effective to use. [ABSTRACT FROM AUTHOR]

Many inverse problems can be formulated as constrained optimization problems in which the feasible set is a description of the a priori information. Different feasible sets represent different a priori information. In this work, we investigate the constrained optimization problem such that the feasible set is the intersection of several convex sets. A closed-form projection is necessary when using standard gradient projection methods to solve the constrained optimization problem since the projection has to be evaluated exactly. However, the numerical methods of projection onto the intersection of convex sets usually have an iterative structure which leads to an inexact projection result in practice. In this work, a feasible set expanding strategy is designed to overcome this issue. Based on this strategy, an inexact scaled gradient projection method is proposed. The convergence analysis is provided with proper assumptions. Numerical results for an inverse problem named full waveform inversion are provided. [ABSTRACT FROM AUTHOR]

• First field application of CNN to estimate CO 2 saturation map from seismic data. • SeisCO2Net overcomes data limitation of seismic monitoring. • Synthetic tests show high accuracy and robustness. • Frio-II field prediction show similar CO 2 plume characteristics to prior studies. Seismic monitoring of geological CO 2 storage (GCS) involves highly nonlinear seismic inversion and petrophysical inversion, making it challenging to estimate CO 2 volume efficiently and detect possible early CO 2 leakages. Deep learning (DL) using convolutional neural networks (CNNs) has shown promise in solving highly nonlinear seismic inversion problems. However, direct estimation of CO 2 plume extent/saturation from time-lapse seismic gathers using DL is still underexplored, with no reported field applications to date. The investigation of field data is primarily hindered by scarcity of field data for neural network training. Other obstacles include highly nonlinear seismic-petrophysics inverse relationship, and presence of noise in field seismic data. We introduce SeisCO2Net, a deep CNN that predicts CO 2 saturation maps directly from time-lapse full waveform shot gathers. For training, we use site-specific geological information, fluid flow physics, rock physics, and seismic modeling to generate synthetic datasets that closely resemble the CO 2 storage site. Synthetic tests show promising results, inspiring us to apply SeisCO2Net's trained weights on field data collected at Frio-II GCS site by leveraging transfer learning principles. As reference, we compare SeisCO2Net's predicted CO 2 saturation maps with results obtained from physics-based inversion. Our analyses show both methods display similar CO 2 plume shapes, reasonable CO 2 plume characteristics, and comparable saturation values. Our results suggest pre-training CNNs on physics-informed synthetic datasets and then applying the learned weights to field data is a viable approach to estimating field CO 2 saturation. This method effectively addresses the scarcity of field training data, thus encouraging the feasibility of long-term GCS monitoring. [ABSTRACT FROM AUTHOR]

[Display omitted] • A new workflow integrating intelligent inversion and lithology model is proposed. • This method improves the seismic resolution and prediction of tight sandbodies. • The results provide reliable geological information for horizontal well drilling. Lacustrine gravity-flow deposits are common in sedimentary basins, and sand bodies formed in the basin center mingled with organic rich mudstone may form excellent tight reservoirs of hydrocarbons. However, the complex architecture and intensive heterogeneities of lacustrine gravity-flow deposits commonly pose great risks for hydrocarbon exploration and development. To address this problem, a new workflow for predicting tight sandstone is proposed in this work. The workflow combines well log analysis, intelligent seismic inversion and 3D geological modelling to improve the prediction effectiveness of tight sandstone and sedimentary facies. The original 3D seismic data was firstly decomposed into three components with low, middle and high frequencies to improve the resolution of seismic inversion, and a nonlinear relationship between well logs and the decomposed 3D seismic data was then established by using an algorithm of support vector machine. A 3D lithofacies model was subsequently established integrating well logs and the seismic inversion result. The distribution of sand thickness was imaged relying on the lithofacies model. Correlations between the actual and predicted sand thickness based on the seismic attributes and the seismic inversion result was improved from 0.66 to 0.79. The prediction of sand bodies was further improved through the 3D lithofacies modelling, especially for these thin sandstone layers. Sedimentary facies of the Chang_7 member in the Qingcheng Oilfield, Ordos Basin, were characterized based on the distribution of sand bodies. Fan-shaped sand bodies are mapped in each zone of the Chang_7 member, revealing that the entire Chang_7 successions are of sub-lacustrine fan in origin. The Chang_7 member mainly contains six sedimentary architectural elements: main channel, branch channel, lobe, lobe edge, slump body and inter lobe/inter channel. The distribution of sedimentary facies is different in different zones, and appears to be largely related to the change of base-level. A horizontal well was designed according to the prediction of sand bodies, which show a higher probability of drilling into sandstone by approximately 10% compared with that of other horizontal wells. [ABSTRACT FROM AUTHOR]

Deltaic petroleum systems are often complex and heterogeneous. Delineation of sandstone bodies in such complex depositional settings constitutes a crucial challenge in reservoir exploration. However, reservoir characterization based on conventional well-logs and stratigraphic interpretation in complex settings often leads to uncertainties. In this work, we present an integrated quantitative interpretation to reveal heterogeneity and the reservoir properties in a complex deltaic environment. The study benefits from integrating borehole lithology logs, electrical well logging, and a 3D post-stack seismic cube. To demonstrate the practical application and effectiveness of our proposed approach, seismic inversion has been applied to the Acacus Formation in the Ghadames Basin of southern Tunisia. We estimate the acoustic impedance volume from 3D seismic data using a stochastic inversion algorithm. This algorithm enables the generation of multiple realizations, accounting for uncertainties and providing a more comprehensive understanding of subsurface impedance. Then, the porosity model is computed by establishing a functional relationship between acoustic impedance and porosity. The analysis of the relationship between acoustic impedance and different rock properties helped to differentiate lithological units of sandstone and claystone. This lithology classification is used to estimate the lithofacies model. Finally, the obtained porosity and lithofacies models are validated with well data. The crucial correlation between facies distribution and porosity allows for accurate mapping of individual thin sandstone bodies and their property distribution beyond well control. The lithofacies model shows significant sandstone bodies in the Acacus A and C units that constitute good reservoirs. The porosity model confirms this, as these sandstone facies exhibit high porosity (20%). The proposed approach has proven to be powerful in reservoir delineation, characterization, and exploration, and can be applied in similar geological setting basins, and frameworks. • Acoustic impedance volume is estimated from seismic data using a stochastic inversion algorithm. • Acoustic impedance's relationship with different rock properties is analyzed, for sandstone and claystone differentiation. • Porosity cube is obtained from a linear relationship with acoustic impedance. • Lithofacies model is derived from a lithological classification using acoustic impedance cross-plot versus VpVs ratio. • Heterogeneity and reservoir characteristics of the Acacus sandstone are revealed based on an integrated interpretation. [ABSTRACT FROM AUTHOR]

Resolving thin layers and achieve focused layer boundaries is one of the major challenges in seismic inversion. This translates into recovering a blocky solution with sparse spatial derivatives of model parameters. Here, we present two iterative focusing regularization techniques for target-oriented Amplitude Versus Angle (AVA) inversion. Target-oriented means that only the AVA responses of the reflections of interest are inverted for the simultaneous estimation of P-wave, S-wave and density reflectivities. The first approach imposes Cauchy constraints on the spatial model derivatives whereas, the second is inspired by the minimum gradient support regularization. Both the implemented algorithms enhance their focusing and edge-preserving abilities by exploiting an Edge Preserving Smoothing (EPS) filter that is used to compute both the model constraints and the model update. We include a-priori model information into the inversion kernel to guide the convergence of the algorithms toward physically plausible solutions. The two approaches are compared against the standard Bayesian inversion that simply considers Gaussian distributed model parameters, and with the well-known edge-preserving method that assumes Cauchy-distributed derivative of model parameters. For the lack of available field seismic data, we limit the attention to synthetic inversion experiments in which we simulate different signal-to noise (S/N) ratios. The inversion tests prove the suitability of the two proposed approaches for target-oriented AVA inversion and demonstrate their focusing and anti-noise abilities. In particular, the two implemented algorithms outperform the standard Bayesian inversion and the Cauchy approach in cases of low S/N ratios. The two implemented methods are also extremely flexible and can be applied to other linear or non-linear geophysical inverse problems. • Implementing a target-oriented AVA inversion algorithm. • Including blocky constraints into the inversion kernel. • Comparing the predictions of the implemented approaches with those of a standard Bayesian and Cauchy inversion. [ABSTRACT FROM AUTHOR]

Most subsurface rock models consider salt bodies as homogenous masses and set to evaporite minerals constant values of elastic properties such as density, compressional and shear velocities, Young's modulus and Poisson's ratio. Lithological analyzes of outcrops and wells indicate that evaporites present large vertical and lateral heterogeneities. Recent studies have demonstrated that the knowledge of the elastic properties of rock salt can substantially benefit velocity modeling, seismic imaging, reservoir geomechanical analysis, prediction of wellbore stability and calculation of optimum fluid weight. However, few papers document the spatial estimation of elastic properties for salt formations. We propose an approach to characterize the internal structures of the salt bodies in the Santos Basin, offshore Brazil, using well and 3D seismic data. It starts with a rock-physics analysis at well-log scale, which reveals that the elastic properties of rock salt are highly correlated. Once the relationships of the elastic properties are established, we explore the functionality of the empirical equations as property predictors. We demonstrate that the elastic properties can be empirically estimated from compressional velocity. The comparison between the estimated and the measured well logs validates the estimation. Then, we perform the seismic inversion to generate the spatial distribution of the acoustic impedance. We derive compressional and shear velocities, density, Young's modulus and Poisson's ratio volumes by applying the empirical equations to the acoustic impedance volume. The blind well points out that, through this workflow, the seismic data can successfully predict the elastic properties of the salt formation in undrilled portions. • Studies of in-situ elastic behavior of evaporites. • Formulation of new empirical equations as property predictors. • Compaction has null influence on the elastic properties of rock salt. • Combination of seismic inversion and rock physics for quantitative characterization. • Review of multidisciplinary applications of elastic properties of rock salt. [ABSTRACT FROM AUTHOR]

Abstract Geostatistical seismic inversion is one of emerging technologies in reservoir characterization and reservoir uncertainty quantification. However, the challenge of intensive computation often restricts its application in practical studies. To circumvent the computational limitation, in this work, we present a distributed parallel approach using TensorFlow to accelerate the geostatistical seismic inversion. The approach provides a general parallel scheme to efficiently take advantage of all the available computing resources, i.e. CPUs and GPUs: the computational workflow is expressed and organized as a Data Flow Graph, and the graph can be divided into several sub-graphs which are then mapped to multiple computing devices to concurrently evaluate the operations in them. The high-level interface and the feature of automatic differentiation provided by TensorFlow also makes it much easy for users to implement their algorithms in an efficient parallel manner, and allows employing programs on any computing platform almost without alteration. The proposed method was validated on a 3D seismic dataset consisting of 600 × 600 × 200 grid nodes. The results indicate that it is feasible to the practical application and the computational time can be largely reduced by using multiple GPUs. Highlights • We proposed a parallel approach for geostatistical inversion using TensorFlow. • The parallel scheme is user-friendly and highly scalable. • Adam is used as the optimization method to efficiently update model parameters. • The approach can largely reduce the computing time using GPUs. • The presented method can be widely used in other inverse problems. [ABSTRACT FROM AUTHOR]

Highlights • A method for 3D seismic time-variant inversion based on earth Q-model is proposed. • Relations between data points are inferred via seismic discontinuity measures. • Theoretical bound on the recovery error for horizontal layers is derived. • The robustness of the method is demonstrated with synthetic and real data examples. Abstract We propose a multichannel efficient method for recovery of three-dimensional (3D) reflectivity signal from 3D seismic data. The algorithm consists of solving convex constrained optimization problems that promote the sparsity of the solution. It is formulated so that it fits the earth Q model that describes the attenuation and dispersion propagation effects of reflected waves. At the same time, the method also takes into account the relations between spatially-neighboring traces. These three features together with low computational cost make the proposed method a reliable solution for the emerging need to accurately estimate reflectivity from large volumes of 3D seismic data. We also derive a theoretical bound on the recovery error in the case of horizontal layered sub-terrain. We show that the recovery error is inversely proportional to the number of traces taken into account in the estimation process. Synthetic and real data examples demonstrate the robustness of the proposed technique compared to single-channel recovery. [ABSTRACT FROM AUTHOR]

Abstract Inverse problems where relationships are linear arise in many fields of science and engineering and, consequently, algorithms for solving them are widespread. However, when the size of the problem increases, the computational challenge becomes huge, hence, unless some simplifying assumptions are yielded, it becomes impossible to solve the problem. Our study addresses these issues for large linear(izable) inverse problems when the uncertainties can be modeled as Gaussian and the forward relationship and covariance matrices can be expressed in terms of Kronecker products. Under these conditions, we illustrate an algorithm capable of addressing very large problems with very limited storage requirements and much faster than the traditional approach. The result is a complete characterization of the posterior distribution in a probabilistic sense in terms of mean and covariance. We extend this method also to nonlinear problems where a Gauss-Newton algorithm is employed. Applications to reflection seismology, magnetic anomaly inversion and image restoration are presented. Highlights • Solution to large linear inverse problems by an efficient and parallel algorithm under Gaussian and separability assumptions. • Kronecker product-based strategy allows huge memory saving and considerable speed-up. • Correlations and uncertainty are consistently back-propagated to the solution. • Solution of quasi-linear problems using a Gauss-Newton version of the algorithm. • Approximations for non-separable kernels can be used. [ABSTRACT FROM AUTHOR]

Optimal gas production from marginal fields vulnerable to tidal energetic effects is exceedingly challenging. Conventional seismic analysis is insufficient for a significant understanding of the geomorphological description, volumetric calculations, and microfacies analysis of the anticipated heterogeneous gas-bearing sands. New insights into the offshore North Sinai (ONS) Field based on quantitative and geostatistical interpretations integrated with physical and geometrical attribute analyses are boosted for reconstructing diverse-scale reservoir internal architecture. Deterministic band-limited inversion using acoustic impedance logs contributed to the initial knowledge of facies migration during tidal-dominated delta progradation and revealed four 2nd-order sequence boundaries dependent on P-velocity variations with depth as a function of rock elasticity, low impedance gas content of 2500–3000 kPa s/m and overburden compressional factors. Gradual boundaries confining reservoir bases due to spatiotemporal facies discrepancies triggered the implementation of stochastic algorism as a model norm for tracing thin-bedded and internal heterogeneous reservoirs with no predictable gas-water contacts across subtle dismantled areas. Stochastic inversion with intensive layering below −1 ms along with static property reservoir modeling allowed the evaluation of both fine and coarse-scale reservoirs discovered as extensions of injector-controlled stacked mouth bars, fan-lobes, and distributary channels crosscutting tidal mudflats of delta top facies associations within a system of spatially migrated clinoforms to the north. Stream avulsion-induced and accommodation space inconsistencies across syn-sedimentary faults caused the shifting of the Late Plio-Pleistocene clinoform depocenter northward and deltaic facies variation inside each individual sand system, not eustatic sea level fall. After calculating the biogenic gas reserves occupying each reservoir, the present strategy offers an additional seven broad gas prospects within four dismantled blocks, raising the ONS initial gas in place to 967.95 Bcf. In addition, it depicts five gas shoals emplaced at shallow depths, posing considerable drilling hazards. This paper workflow is the first of its kind in the Neogene section of the OSN Basin. • A straightforward procedure for basin analysis by sequence stratigraphic approaches and structural evolution. • Geometrical attribute analysis is insufficient for gas exploration. • Enhancing gas production through post-stack inversion techniques re-simulate tide-dominated gas reservoirs. • Stepwise strategy of reservoir simulation and volumetric calculations rises ONS gas production to 967.95 Bcf. [ABSTRACT FROM AUTHOR]

The artificial ground freezing (AGF) technique is widely used in the construction of underground structures, and the deformation of soft clay is more significant after freezing-thawing cycles. Moreover, many earthquake disasters indicate that the engineering in soft soil areas experience huge risks to seismic fortification. In this paper, the inversion formula of seismic acceleration time history for different peak ground accelerations (PGAs) in the frequency domain was derived. The maximum peak acceleration and maximum dynamic shear stress in the undisturbed and frozen-thawed soft clay sites were analyzed by DEEPSOIL. The reduction factor of dynamic shear stress suitable for the clay field was calculated to provide basic parameters for the cyclic triaxial tests considering different confining pressures, dynamic stress amplitudes and loading frequencies. The ground motion acceleration of soft clay sites has an amplification effect, and that of frozen-thawed soft clay sites is more obvious. The reduction factor of shear stress for the clay site is overestimated by the liquefaction resistance shear stress method. The axial strain of the frozen-thawed clay reaching failure is less than that of the undisturbed clay. Under the same seismic shear stress, the development of the excess pore water pressure in frozen-thawed clay is more significant than that in undisturbed clay. • Inversion formula of seismic acceleration time history was derived and applied. • Reduction factor of shear stress and equivalent seismic loading for soft clay site were calculated. • Seismic response for both frozen-thawed clay site and undisturbed clay site was compared. • Behaviors of frozen-thawed clay subjected to equivalent seismic loading were studied. [ABSTRACT FROM AUTHOR]

Delineating tidal channels that bear hydrocarbons in fluvial systems is crucial to minimize drilling risks, and raise the hydrocarbon potential. Therefore, we performed an integrated workflow that combines/compares the outcomes of a pair of post-stack inversion techniques (model-based inversion and colored inversion) and the spectral decomposition attribute to delineate (vertically and horizontally) the hydrocarbon-bearing tidal channel contained in the Upper Cretaceous Abu Roash (AR)/C reservoir in the South West Abu-Sennan (SWS) area in Abu-Gharadig basin in the Northern Egyptian Western Desert (NEWD). We used a dataset consisting of twenty seismic reflection profiles and borehole geophysical data of four wells. Data conditioning was performed in detail for the whole dataset that was used in this study. The application of the absolute impedance (model-based inversion) produced better results relative to the band-limited acoustic impendence (colored inversion). The model-based inverted sections gave an improved subsurface image of seismic data and delineated the reservoir properly. The low-frequency range detected through the use of seismic inversion techniques and the spectral decomposition attribute analysis showed a good fit with the low impedance values corresponding to hydrocarbons. The study results showed a correlation of roughly 0.99 exists between the generated synthetic impedance, original seismic, and the borehole geophysical data characterizing the tidal channel confined within the AR/C reservoir. The match between the post-stack inversion techniques and the spectral decomposition analysis results is considered a visual validation of the delineated tidal channel. The performed workflow can be applied and impact the understanding of comparable reservoirs with similar settings in near areas. • Tidal channel delineation using advanced seismic interpretation techniques. • Comparing model-based, colored seismic inversion and spectral decomposition outcomes. • Seismic inversion and spectral decomposition defined AR/C sand channels effectively. [ABSTRACT FROM AUTHOR]

The South Caspian Basin (SCB) is known as one of the World's deepest and largest hydrocarbon-prone basins. Due to high subsidence and sedimentation rate in this deep-water basin, many overpressure sandy zones are susceptible to shallow water flow (SWF) problem. SWF is one of the geological hazards in deep-water exploratory drilling such as SCB. When the drill reaches the SWF zones, sands and water flow into the wellbore which cause the drilling failure. Therefore, the present study is the first detailed evaluation of SWF problem predrilling in the SCB, offshore Iran by using 3D post-stack, pre-stack seismic data and log data. Seismic attributes have been applied to identify the buried channels which may be considered as the most potential feature for SWF occurrence. Simultaneous inversion has been used to extract the parameters such as P-impedance (AI), S-impedance (SI), Vp/Vs ratio, Poisson's ratio. Based on inversion results, unsupervised artificial neural networks also used to estimate the probability of SWF risk in the buried channel for the first time. Our initial evaluations based on the similarity and sweetness attributes indicated that some buried shallow sandy channels with a variable width between 500 and 1500 m covered by imperious clay layers are the most potential facies for creating the SWF zones in the SCB. The SWF zone is detected by low-impedances (AI<3150 (m/s)*(g/cc), SI < 1500(m/s)*(g/cc), high Vp/Vs ratio (1.21 to 2.23) and Poisson's ratio in range of 0.05 to 0.38. The probability of SWF risk of the detected channel is estimated to be >0.35 by unsupervised artificial neural networks. Finally, our findings provide a new comprehensive evaluation of SWF problem in the channel system predrilling in the SCB which may be useful for other researchers and decrease the drilling risk in future works. • Evaluates the Shallow Water flow for the first time in the South Caspian Basin, Iran. • Utilizes the similarity and sweetness attributes along with other parameterrs such as P-S impedances and Vp/Vs ration for buried channel detection. • Applies unsupervised artificial neural networks for SWF occurrence probability (between 35% and 95%) in the channel for the first time. • The predicted SWF sands in this study match with the SWF zone experienced during exploratory drilling. [ABSTRACT FROM AUTHOR]

Abstract In recent years, many important discoveries have been made in the marine deepwater exploration in the South China Sea, which confirms the abundant natural gas resources in this area. However, the prediction of deepwater reservoirs is very challenging because of the complex depositional system and the low exploration level with sparse wells in deepwater areas. To reduce the exploration risks, we develop an integrated prediction strategy for the deepwater gas reservoirs using the Bayesian adaptive seismic inversion and the frequency-dependent fluid mobility attribute. In the seismic inversion, an automatically adjusted prior stabilizer is derived to balance between the vertical resolution and the inversion stability according to the noise level, and the trace-by-trace recursive inversion process, using the inversion result of the previous adjacent trace as the initial model for the next, is adopted to ensure the lateral continuity. In the gas detection, the fluid mobility attribute is calculated by the high precision matching pursuit algorithm to directly indicate the gas reservoirs, with no need to use the well-log or horizon data. We then combine the stratigraphic seismic inversion result with the gas indication fluid mobility attribute to comprehensively predict both the distribution and thickness of gas reservoirs. Synthetic data tests on a well model and a designed seismic signal verify the performances of the seismic inversion and the matching pursuit algorithm. The real data fluid mobility attribute gas detection results of two borehole-side seismic traces show good consistency with the well log interpretation results. Finally, the feasibility of the proposed integrated prediction method is demonstrated by a deepwater application in the South China Sea. Highlights • Bayesian adaptive seismic inversion has high resolution and lateral continuity. • Matching pursuit-based fluid mobility attribute has accurate gas detection results. • Integrated method can effectively predict the thickness of deepwater gas reservoirs. [ABSTRACT FROM AUTHOR]

The pore pressure is a vital concept to the petroleum industry and cannot be ignored by either reservoir engineers or geoscientists. Based on theoretical analyses of effective stresses and the grain packing model, a new equation is proposed for predicting pore pressures from formation velocity data. The predictions agree well with both measured pressures and estimations using Eaton's empirical equation, but the application of the new equation to seismic data is simple and convenient. One application example shows that the identification of sweet spots is much easier using pore pressure data than with inverted seismic velocity data. In another application example using field seismic data, a distribution of overpressured strata is revealed, which is a crucial clue for petroleum generation and accumulation. Still, the accuracy of pore pressure prediction is hardly always guaranteed, mainly owing to the complexity of the real geology and the suitability of specific assumptions about the underlying rock physics. [ABSTRACT FROM AUTHOR]

Hidden Markov Models (HMMs) have been applied to predict reservoir lithologies using seismic inversion results as inputs. This approach takes into account the conditional probabilities between different lithologies, i.e. the vertical transitions in sedimentary sequences. These properties are used as prior geological information. In order to relate the seismic inversion results to the true well-log data, HMMs need to be trained based on the Expectation-Maximization theory. Application of the resulting model on a synthetic example from the Book Cliffs (Utah, USA) showed that most lithologies are classified correctly, even for some thin layers. A comparison with point-wise methods in which data samples are treated independently from each other, such as k -means and fuzzy logic classifiers, leads to the conclusion that the spatial correlation in HMMs allows better lithological predictions because the prior information accounts for the geological depositional processes. A real case study with data from the Vienna Basin (Austria) is performed, in which lithologies in a 3D cube are obtained based on properties from seismic inversions, via trained HMMs. While the vertical sequences are shown to be reasonably well predicted, the horizontal continuities are not. This indicates that the future research should focus on the lateral geological relationships. [ABSTRACT FROM AUTHOR]

Source rocks occurrence, extension and quality in the subsurface are usually characterized directly from well data. Seismic characterization is however becoming an emerging technique for extending source rock properties beyond well locations and an effective tool for mapping their lateral and vertical heterogeneities. The Tanezzuft Formation in Tunisia, an extremely organic-rich source rock, was used as a test for applying and comparing different seismic methods aimed at organic-rich sequences characterization. The data used consist of a 2D seismic line shot in the northern flank of the Ghadames Basin (Tunisia), pre-stack gathers extracted from an inline of a 3D survey and four wells with a complete set of wireline and petrophysical logs. Impedance inversion, AVO and seismic attributes were computed on the seismic lines and compared to well data. Impedance inversion together with a rock physics model of the formation generated a quantitative estimate of total organic carbon (TOC) content in the studied 2D seismic line. AVO analysis showed a Class IV and a Base Class IV response for the upper and lower reflectors delimiting the organic-rich interval respectively. Seismic attributes analyzed the formation by exploiting its high impedance contrast and intrinsic anelastic attenuation. The three methods here discussed showed coherent results both when compared each other and when compared with the well data and the regional geological model. In particular, AVO and seismic attribute analysis are methods that can be applied without the need of well information, and are then feasible methods for the investigation of source rock properties in undrilled basins. [ABSTRACT FROM AUTHOR]

This study integrates 3D seismic attributes and well log data to determine V sand and reservoir property distribution of the Oligo-Miocene Asmari Formation in the western Dezfol Embayment, SW Iran. The rocks consist of complex geology with siliciclastic and carbonate lithology. Hence, it creates the need for a precise interwell estimation of lithology and reservoir properties. For this purpose, first the acoustic impedance was obtained by a model-based inversion algorithm. Then, the acoustic impedance attribute and other sample-based seismic attributes were integrated with sand volume and petrophysical data by using multiple attribute regression and neural networks in order to predict V sand and reservoir property. In the next stage, cross-validation was used to estimate the reliability of the derived multi-attribute transforms. Based on the results of neural networks, the highest cross-correlation was observed between seismic attributes and the observed target logs at seven wells in the study area. After validation, and making a comparison between different available techniques for sand volume, effective porosity and water saturation estimation, multiple regression transform and neural network were used for the first two and latter, respectively. The derived sand volume and reservoir property maps for the Asmari reservoir indicated that high-porous and high-sand volume parts were laterally more continuous in the central and east part of the area under study. In addition, high-porosity zones were more related to high sand volume parts. Based on the result of interpretation and the relationship between core and acoustic impedance, variations in acoustic impedance were related to variations in geological characteristics of Asmari reservoir in the field. Therefore, seismic inversion as a powerful tool can facilitate the detailed studies of sedimentary facies and lithology in the reservoir which contribute to understand the subsurface reservoirs heterogeneities and drilling strategy of future drilling campaigns in the study area. [ABSTRACT FROM AUTHOR]

Marine sediments cover two-thirds of the earth, and porosity (or void ratio) is a major controlling parameter in virtually every model of seafloor properties, including strength, sound speed, hydrology, thermal conductivity, and electrical resistivity. Our new model of void ratio ( e ) is based on the proportional void ratio, [ e p = ( e − e r ) / ( e 0 − e r ) ], where e 0 is the depositional maximum at the sea floor, and e r is the minimum residual void ratio at depth. We assume the values of e 0 and e r are inherent characteristics of the sediment type. Our model further defines the compression index C c to be the square root of the proportional void ratio ( C c ( e ) = ( e p ) 1 / 2 ). This new formulation establishes a direct relation between void ratio and effective stress: e = ( e 0 − e r ) − 1 [ log 10 ⁡ ( σ 0 / σ ) + 2 ( e 0 − e r ) ] 2 / 4 + e r and exhibits several advantages over previous models that we demonstrate with compression test data from the Gulf of Mexico and Nankai Trough. [ABSTRACT FROM AUTHOR]

The Melut Basin is a rift basin in the interior Sudan linked to the Mesozoic-Cenozoic Central and Western African Rift System. The Paleocene Yabus Formation is the main reservoir deposited in heterogeneous fluvial/lacustrine environment. Delineation of channel sandstone from shale is a challenge in reservoir exploration and development. We demonstrate a detailed 3D quantitative seismic interpretation approach that integrates petrophysical properties derived from well logs analysis. A porosity transform of acoustic impedance inversion provided a link between elastic and rock properties. Thus, we used seismic porosity to discriminate between different facies with appropriate validation by well logs. At the basin scale, the results revealed lateral and vertical facies heterogeneity in the Melut Basin. Good reservoir quality is observed in the Paleocene Yabus Formation. The sand facies indicated high porosity (20%) corresponding to low acoustic impedance (20000–24000 g ft/(cm3.s)). However, lower quality reservoir is observed in the Cretaceous Melut Formation. The porosity of sand/shale facies is low (5%), corresponding to high acoustic impedance (29000–34000 g ft/(cm3.s)). This suggests that the Yabus Sandstone is potentially forming a better reservoir quality than Melut Formation. At the reservoir scale, we evaluated the facies quality of Yabus Formation subsequences using petrophysical analysis. The subsequences YB1 to YB3, YB4 to YB7 and YB8 to YB10 showed relatively similar linear regressions, respectively. The subsequence of YB4 to YB7 is considered the best reservoir with higher porosity (25%). However, subsequence YB1 to YB3 showed lower reservoir quality with higher shale volume (30%). This attributed to floodplain shale deposits in this subsequence. Similarly, the high porosity (20%) recognized in deeper subsequences YB6 to YB9 is due to clean sand facies. We learnt a lesson that appropriate seismic preconditioning, exhaustive petrophysical analysis and well log validation are important keys for improved reservoir quality prediction results in fluvial/lacustrine basins. [ABSTRACT FROM AUTHOR]

We consider the problem of recovering the underlying reflectivity signal from its seismic trace, taking into account the attenuation and dispersion propagation effects of the reflected waves, in noisy environments. We present an efficient method to perform seismic time-variant inversion based on the earth Q-model. We derive theoretical bounds on the recovery error, and on the localization error. It is shown that the solution consists of recovered spikes which are relatively close to spikes in the true reflectivity signal. In addition, we prove that any redundant spike in the solution, which is far from the correct support, will have small energy. The robustness of our technique is demonstrated using synthetic and real data examples. [ABSTRACT FROM AUTHOR]

We present a reservoir geophysics study, including rock physics modeling and seismic inversion, of a carbon dioxide sequestration site in Southwestern Wyoming, namely the Rock Springs Uplift, and build a petrophysical model for the potential injection reservoirs for carbon dioxide sequestration. Our objectives include the facies classification and the estimation of the spatial model of porosity and permeability for two sequestration targets of interest, the Madison Limestone and the Weber Sandstone. The available dataset includes a complete set of well logs at the location of the borehole available in the area, a set of 110 core samples, and a seismic survey acquired in the area around the well. The proposed study includes a formation evaluation analysis and facies classification at the well location, the calibration of a rock physics model to link petrophysical properties and elastic attributes using well log data and core samples, the elastic inversion of the pre-stack seismic data, and the estimation of the reservoir model of facies, porosity and permeability conditioned by seismic inverted elastic attributes and well log data. In particular, the rock physics relations are facies-dependent and include granular media equations for clean and shaley sandstone, and inclusion models for the dolomitized limestone. The permeability model has been computed by applying a facies-dependent porosity-permeability relation calibrated using core sample measurements. The study shows that both formations show good storage capabilities. The Madison Limestone includes a homogeneous layer of high-porosity high-permeability dolomite; the Weber Sandstone is characterized by a lower average porosity but the layer is thicker than the Madison Limestone. [ABSTRACT FROM AUTHOR]

• Application of deep learning for Geopressure predictions in Potwar foreland basin. • Identifying abnormal pressure intervals within molasse deposits of Murree Formation. • Integrating seismic and well data to predict two-dimensional pressure variations. • Comparison of linear and nonlinear geostatistical analysis for pressure predictions. • Constraining pore pressure predictions using DFNN in structurally complex basins. Precise geopressure predictions in younger foreland basins of the world is a challenging job. Well based pressure prediction gives only one-dimensional information about the pressure variations. Pressure predictions at a basin level by generating two-dimensional pressure sections requires integration of seismic and well data using geostatistical analysis. A combination of linear (multiattribute analysis) and non-linear (neural network) geostatistical analysis for precise two-dimensional pressure prediction is applied in an active foreland basin. Potwar Basin, which is the major oil and gas producing basin of Pakistan, exhibits abnormally high pressures within Neogene sediments even at shallow depths. Deep Feedforward Neural Network (DFNN) analysis is applied for the first time to predict pore pressures on 2D seismic transects of multiple producing fields of Potwar Basin for identification of abnormal pressure intervals within Murree Formation. Impedance sections along with original seismic trace and pressure curve from well are used as an input to train the seismic attributes to populate the pore pressure laterally. A total of 20 attributes are selected for data training using seismic based multiattribute analysis resulting in more than 85 % correlation in all the wells with a maximum correlation value of 91 %. Furthermore, DFNN analysis is then applied to enhance the results which showed more than 97 % coefficient in Qazian-1X, Balkassar OXY-01 and Amirpur-01 wells. DFNN analysis for demarcation of abnormal pressure zones depicts pressure results with an average 92 % correlation coefficient and provided a constrained pore pressure prediction workflow for getting consistent and reliable results in structurally complex foreland basins. [ABSTRACT FROM AUTHOR]

Permeability is an important factor for reservoir characterization. It is routinely estimated by building a relationship between porosity and permeability. But because of the complexity of the pore types in carbonate reservoirs, sometimes this method does not yield good results. This paper proposes an integrated workflow using rock physics modeling and seismic inversion to estimate the permeability section using core data, petrophysical logs, and seismic data. Our data set belongs to a carbonate reservoir from Abadan Plain and includes three wells and a 2D seismic offset gathers. First, we estimated geophysical pore types (stiff, reference, and cracks) using a pore type inversion algorithm. The validity of the obtained results is investigated using the scanning electron microscope, and thin section images from core samples. Then, the contribution of different pore types to permeability prediction has been investigated and compared to the routine method of permeability prediction using porosity. Analysis of the results at the well locations shows that we achieved better permeability estimation (around 10% improvement in this case) by integrating information of various pore types with the flow capacity. Furthermore, using the proposed method, a permeability section has been generated using the integration of seismic, well logs, and rock physics modeling results for pore types. [Display omitted] • Use of rock physics inversion for separation of pore types and application in permeability estimation. • Separating effect of different pore types in permeability estimation. • Improvements in permeability estimation using integration of core, log and seismic data. [ABSTRACT FROM AUTHOR]

Fractures are essential for gas accumulation in tight sandstones, as they enhance reservoir permeability and provide migration paths for fluid transport. The presence of fractures facilitates the formation of fracture networks during hydraulic fracturing and, therefore, is important for effective development of tight gas sandstones. Accordingly, predicting fracture distribution could provide insight into the reservoir quality of tight gas sandstones. Seismic amplitude variation with azimuth can evaluate the properties of vertical fractures embedded in tight sandstone. This study proposes an improved inversion scheme based on azimuthal amplitude differences to compute anisotropic parameters for fracture characterization by incorporating rock physics relationships. Our proposed method improves the robustness of the anisotropic parameter estimates by removing the effect of the isotropic background using azimuthal amplitude difference data obtained after evaluating fracture strikes. Theoretical modeling and real data analysis illustrated the enhanced sensitivity of the azimuthal amplitude differences to the anisotropy related to fractures. Real data applications indicated that the predicted fracture strikes coincide with the results estimated by tectonic stress field analysis. The computed fracture density correlates with logging permeability, implying considerable influence from fractures on permeability improvement in tight sandstones. Further analysis indicated that the gas production capacity of tight sandstones is affected simultaneously by fractures and gas enrichment. Accordingly, an integrated indicator is proposed to evaluate reservoir quality, that simultaneously considers the degree of fracture development quantified by the fracture density, and gas enrichment represented by rock compressibility. The proposed indicator is correlated positively with comprehensive reservoir properties defined by the cumulative sand layer thickness, porosity, gas saturation, and permeability, providing a reliable and practical pointer to identify favorable areas in tight gas sandstones. • Improved azimuthal amplitude difference inversion for anisotropic parameters. • Fracture estimation by anisotropic parameters using rock physical relationships. • Integrated indicator comprehensively considering fracture and gas enrichment. • Good correlation between the integrated indicator and reservoir properties. • A practical indicator to identify tight gas sandstones using seismic methods. [ABSTRACT FROM AUTHOR]

Post-stack inversion of seismic data is routinely carried out to derive acoustic impedance (AI) and, hence petrophysical properties in an area. We have been introducing here an uncommon methodology of inverting post-stack seismic data into porosity from porosity log. The post-stack inversion for estimation of direct porosity is performed by utilizing an estimated porosity wavelet, low frequency model and model based inversion. This methodology is implemented on two types of 2D post-stack seismic dataset; (a) deep water Mahanadi (MN) offshore containing gas hydrate sediments and (b) clay rich, shaly sediments in Krishna–Godavari (K–G) shallow offshore. The total porosity (φ) estimated from density log for the depth interval of 1725–2032 m ranging from 49 to 75% has been used as input for porosity inversion from the seismic data of unconsolidated sediments at 1701 m bathymetry in MN basin. The total porosity for the depth interval of 410–1494 m ranging from 5 to 45% has been used as input for porosity inversion from the 2D post-stack seismic data of shallow offshore sediments at 31 m bathymetry in K–G basin. This prediction is applied to dataset having good (for MN) and poor correlation (for K–G) between AI and porosity. In MN basin, the porosity along 2D multichannel seismic ranges from 53 to 65% in the gas hydrated zone with maximum value of 70% at the free gas filled unconsolidated sediment below the bottom simulating reflector (BSR). The water bearing silt/clay sediments indicates porosity of about 60–65% below the seafloor. In K–G basin, the porosity in Raghavapuram shale varies from 13 to 30% with maximum value of 52% is observed in Paleocene sediments. [ABSTRACT FROM AUTHOR]

We are concerned with high-fidelity subsurface imaging of the soil, which commonly arises in geotechnical site characterization and geophysical explorations. Specifically, we attempt to image the spatial distribution of the Lamé parameters in semi-infinite, three-dimensional, arbitrarily heterogeneous formations, using surficial measurements of the soil’s response to probing elastic waves. We use the complete waveform response of the medium to drive the inverse problem, by using a partial-differential-equation (PDE)-constrained optimization approach, directly in the time-domain, to minimize the misfit between the observed response of the medium at select measurement locations, and a computed response corresponding to a trial distribution of the Lamé parameters. We discuss strategies that lend algorithmic robustness to our proposed inversion scheme. To limit the computational domain to the size of interest, we employ perfectly-matched-layers (PMLs). In order to resolve the forward problem, we use a recently developed hybrid finite element approach, where a displacement–stress formulation for the PML is coupled to a standard displacement-only formulation for the interior domain, thus leading to a computationally cost-efficient scheme. Time-integration is accomplished by using an explicit Runge–Kutta scheme, which is well-suited for large-scale problems on parallel computers. We verify the accuracy of the material gradients obtained via our proposed scheme, and report numerical results demonstrating successful reconstruction of the two Lamé parameters for both smooth and sharp profiles. [ABSTRACT FROM AUTHOR]

Some subtle reservoirs are hidden in the weak reflection areas and thin layers in seismic profiles, and they cannot be identified easily. Thus, the thin layer interpretation and the subtle reservoir prediction are the essential in reservoir management plan while field development and production stage. To improve the recognition capability in the weak reflection areas and resolution, the fixed-point (FP) algorithm is employed to obtain the inversion results since the conventional FP seismic inversion (CFPSI) is sensitive to the weak changes in seismic signals. However, the CFPSI suffers from low convergence. Therefore, the new iterative equations of pre- and post-stack FP seismic inversion are designed to improve the computational efficiency and convergence. Besides, a new constraint, which is derived from the instantaneous phase of the seismic trace, is also sensitive to the weak changes of the seismic signal. The instantaneous phase constraint is then involved in the objective function, contributing to a significant improvement of identification capability in weak reflection areas and the resolution of inversion. Additionally, the initial model constraint is introduced into the objective function to weaken the multi-solution and noise sensitivity. The synthetic examples demonstrate that the computational efficiency, recognition capability in the weak reflection areas and resolution of the proposed approach are higher than those of the CFPSI. The field examples suggest that the subtle reservoirs and thin layers, which are hidden in the weak reflection areas in the field data, are accurately identified using our method. Thus, the proposed method is useful for improving the recognition capability in weak reflection areas and resolution of the inversion results. • The instantaneous phase constraint is derived and involved into the target function. • The computational efficiency and convergence of the fixed-point seismic inversion is improved. • The recognition capability of weak reflection areas and subtle reservoirs are improved. [ABSTRACT FROM AUTHOR]

Seismic inverse modeling is a common method in reservoir architecture characterization associated with geology. The conventional seismic inversion method is difficult to combine with complicated and abstract knowledge on geological modes, and its uncertainty is difficult to be assessed. In this paper, an inversion modeling method based on a wasserstein generative adversarial networks with gradient penalty (WGAN-GP) is introduced to integrate geology, well data and seismic data. A WGAN-GP is a generation model algorithm based on generative adversarial networks (GANs) that extracts spatial structure and abstract features of a training image. A gradient penalty function is added to an original loss function of GANs to improve the robustness. After assessment of the loss function, variograms and connectivity functions, the trained network is applied to seismic inversion simulation. In inversion, an optimal model is selected by the Metropolis with Markov chain Monte Carlo algorithm. Results show that the trained network can reproduce a thousand models containing millions of grid cells with a specific mode similar to a training image in 1 s. The inversion models conform to well data with 100% accuracy and have an efficient correspondence with prior seismic data. A whole inversion process completes 360,000 iterations in 4 h. The optimal inversion model has a subequal Root Mean Square Error (RMSE) with the true model and visually resembles a channel. With the proposed method, geological knowledge has a stable characterization in model realizations. • An inversion workflow is proposed to bridge a gap between geophysics data and geological knowledge. • The workflow is composed of a machine learning algorithm and a post-stack seismic inversion. • The WGAN-GP algorithm has a stable characterization on geological patterns. • Markov chain Monte Carlo (MCMC) sampling with GPUs speeds up the inversion. • The approach provides a way to decrease uncertainty and deal with the high-dimensional problem. [ABSTRACT FROM AUTHOR]

Combining seismic data with a constrained model can obtain a high-resolution inversion result. However, it is very difficult to obtain an accurate low-frequency model because of the lack of wells in the offshore exploration area. We propose a multi-scale seismic envelope inversion method based on sparse representation to solve the problem of seismic inversion in offshore well-less areas. First, we demodulate the envelope of the seismic signal based on signal modulation theory and take it as the low-frequency component for inversion. Then, decompose the seismic signal based on MPSTFT (Short-Time Fourier Transform based on Matching Pursuit algorithm) method to provide high-resolution components of intermediate-frequency and high-frequency. Next, we take the linear impedance model as the initial model and update the inversion results by multi-scale inversion based on Bayesian inversion theory. Finally, we use the synthetic data to demonstrate the effectiveness of the proposed method and apply it to real seismic data. Comparing inversion results, the result of the proposed is more accurate and possesses a higher resolution. The results show that the proposed method has good adaptability to solve the problem of offshore well-less area inversion with a high-resolution inversion result. • Demodulate the envelope to obtain the low-frequency component in the signal • Decompose the medium and high-frequency information based on MPSTFT • Envelope inverse background information • Update the inversion results as new model constraints • Multi-scale inversion improves the inversion resolution. [ABSTRACT FROM AUTHOR]

Fracture filling fluids identification is an important aspect of fractured reservoirs description. It is well known that fracture weaknesses parameters (the normal and tangential weaknesses) are closely related to fracture parameters (fracture density, fracture aspect ratio and fracture fillings). Hence, the fracture filling fluids can be identified after the fracture weaknesses parameters being obtained. The efficient method, which is presented to estimate the fracture weaknesses parameters in this paper, is azimuthally elastic impedance (EI) inversion. We first derive a new azimuthal EI equation which contains fracture weaknesses parameters. Using the formula, we propose a method of seismic inversion for elastic parameters (P-wave and S-wave impedances) and fracture weaknesses parameters. The fluid factor, which is introduced to identify the fluids in fractures, can be estimated after the calculation of elastic and fracture weaknesses parameters. Tests on real data show that the method is more stable and reasonable by reducing the uncertainty for the estimation of elastic parameters and fracture weaknesses parameters. The inverted results are in good agreement with the results of well log data. [ABSTRACT FROM AUTHOR]

Predrill estimate of pore pressure (PP) is important for well design especially in areas containing gas hydrate bearing sediments. Here we report the pressure distribution in the gas hydrate bearing sediments in Krishna–Godavari (K–G) basin predicted from the seismic data along two lines passing through the site 10 of Expedition-01 (Exp-01) conducted by the Indian National Gas Hydrate Programme (NGHP). A series of elastic parameters namely P-wave velocity (Vp), S-wave velocity (Vs), density (ρ), Vp/Vs, P impedance (Zp) and S impedance (Zs) were derived from seismic data using pre-stack inversion. First the PP estimated from a sonic transit time log was calibrated with pressure core measurements in the depth interval of 1060–1280 m. Its magnitude is found to be ranging from 10.5 to 12.9 MPa where the vertical stress varies from 10.7 to 13.4 MPa within the same depth interval. This estimated PP was next treated as a target log (desired output) and seismic attributes resulting from pre-stack seismic inversion were used as input parameters in the training of a multi-layered feedforward network (MLFN). Finally we use the trained network to generate a subsurface PP distribution map along two 2D multichannel seismic profiles (line-X and line-Y) passing through NGHP01-10 within a time interval of 1420–1620 ms corresponding to a depth interval of 1060–1280 m. This corresponds to sedimentary column from 22 to 242 mbsf. Our results demonstrate that the pressure remains hydrostatic within the gas hydrate bearing sediments and is mostly above-hydrostatic below the gas hydrate bearing zone. [ABSTRACT FROM AUTHOR]

A comprehensive understanding of porosity variations in tight gas sandstones plays an important role in reservoir management and provision of plans for developing of the field. This is especially important when we encounter with some degree of complexity in reservoir characteristics of these sandstones. Reservoir properties of tight gas sandstones of the Whicher Range field, the target reservoir of this study, were affected by internal reservoir heterogeneity mostly related to depositional and diagenetic features of the reservoir sandstones. In this study, 2D seismic data in combination with well log data were used for prediction of porosity based on seismic inversion technique and multi-attribute regression analysis. The results show that acoustic impedance from model based inversion is the main seismic attribute in reservoir characterization of tight sandstones of the field. Wide variations in this parameter can be effectively used to differentiate the reservoir sandstones based on their tightness degree. Investigation of porosity by this method resulted in 2D-view of porosity variations in sandstone reservoir which is in accordance with variations in geological characteristics of tight gas sandstones in the field. This view can be extended to a 3D-view in the framework of reservoir model to follow the variations throughout the field. [ABSTRACT FROM AUTHOR]

Skewness is often present in a wide range of spatial prediction problems, and modeling it in the spatial context remains a challenging problem. In this study a skew-Gaussian random field is considered. The skew-Gaussian random field is constructed by using the multivariate closed skew-normal distribution, which is a generalization of the traditional normal distribution. We present a Metropolis–Hastings algorithm for simulating realizations efficiently from the random field, and an algorithm for estimating parameters by maximum likelihood with a Monte Carlo approximation of the likelihood. We demonstrate and evaluate the algorithms on synthetic cases. The skewness in the skew-Gaussian random field is found to be strongly influenced by the spatial coupling in the field, and the parameter estimators appear as consistent with increasing size of the random field. Moreover, we use the closed skew-normal distribution in a multivariate random field predictive setting on real seismic data from the Sleipner field in the North Sea. [ABSTRACT FROM AUTHOR]

Objective To solve the inverse problems when outliers exist in the seismic data and discontinuities such as layer boundaries need to be clearly delineated and merge the low frequency information to the inverted parameters. Methods L1-norm misfit function, total variation regularization, a priori information constraints, method of Lagrange multipliers, and iteratively re-weighted least squares. Results and conclusions Integrating the L1-norm misfit function, total variation regularization and a priori information constraints via the method of Lagrange multipliers, we create the objective function of seismic inversion to solve the inverse problems that outliers exist in the seismic data and discontinuities such as layer boundaries need to be clearly delineated. In addition, the priori information constraints ensure the inverted parameters have low frequency components. Practice The proposed inversion method is successfully tested on noisy synthetic seismic data with outliers and real seismic data. Implications If there are a small number of outliers in the seismic data, we need to do the seismic inversion in a way that minimizes their effect on the estimated parameters. However, the L2-norm misfit function is highly susceptible to even small numbers of inconsistent seismic observations. As an alternative to L2-norm, one can consider the solution that minimizes the L1-norm misfit function (L1MF) which will be more outlier-resistant, or robust, than the L2-norm solution. Of course, there are some alternative techniques to find the favorable regularization parameters. A set of good regularization parameters is the key of the seismic inversion process. [ABSTRACT FROM AUTHOR]

Prestack least-squares reverse time migration based on a correlative objective function denoted as PCLSRTM can retrieve a higher quality image of the subsurface than standard least-squares reverse time migration based on a waveform-matching objective function. However, to invert the optimal migration images of different shots via a gradient-based iteration scheme, PCLSRTM tends to require a huge computational overhead for a large number of reverse time migration (RTM) and reverse time de-migration operators. We introduce a convolutional neural network (CNN) framework to improve the computational efficiency in PCLSRTM. Specifically, the CNN architecture aims to reconstruct the optimal reflection image for a single-shot recording from the standard RTM image. Besides, the migration model can be used as an additional input of CNN architecture. Limited by the absence of true reflectivity of the subsurface as the label data in seismic imaging, we employ the standard PCLSRTM images of a small part of total shot recordings as the labels to train the constructed CNN model. There are two benefits for the well-trained CNN model. On the one hand, it can directly invert a comparable image quality with the PCLSRTM image at a low computational cost, when the migration model does not contain a strong lateral velocity variation. On the other hand, one can use the inverted image from the well-trained CNN model as the initial image of the PCLSRTM approach to accelerate convergence. Through synthetic experiments with the Marmousi-2 and SEG/EAGE salt models and marine field data, it can determine that our approach can build a reflection image with balanced amplitude and good continuity and merely requires about one-ninth to one-fourth of the computational costs of PCLSRTM. • We develop a PCLSRTM approach to retrieve a high-solution reflection image. • A CNN framework for improving computational efficiency of prestack inversion. • Field data has verified the effectiveness of CNN approach. [ABSTRACT FROM AUTHOR]

The ever-increasing demand for new energy sources witnessed at present is leading to a shortage of oil and gas resources throughout the world. At the same time, polluting energy sources such as coal are being gradually replaced by gas, new fuel types and electricity produced by renewable methods. Unconventional shale gas reserves, relying on the presence of substantial volumes of good quality, thermally mature organic matter, are therefore crucial in shaping the economic future of multiple regions in the world. Using seismic reflection data to estimate Total Organic Carbon (TOC) in the underexplored Sembar Formation of the Qadirpur Area, Central Indus Basin, this study investigates the potential of a new unconventional resource in Pakistan. We estimate TOC based on well-log data using Passey's Δ Log R, Schmoker's, and Schwarzkopf's methods. In a second stage, thermal maturity modelling was carried out for the formations encountered in Well Qadirpur Deep-01, while focusing primarily on the Sembar Formation. Petrophysical and petroelastic properties were determined and cross-plotted to identify potential zones favourable to hydraulic fracturing. The results show calculated TOC values ranging from 2 to 4 wt% based on the multiple methods indicated above, proving that the Sembar Formation is a good to excellent unconventional oil gas play. Thermal maturity modelling further confirms that the organic matter in the Sembar Formation is mature. Our seismic based spatial distribution indicates that TOC values are particularly favourable in the lower part of the formation, which is also prone to hydraulic fracturing based on its petroelastic evaluation. This study presents a valid approach to characterise source-rock potential in sedimentary basins throughout South Asia and around the world. • Reliable spatial distribution of shale units can be obtained by seismic and wireline log analyses. • Thermal maturity models indicate hydrocarbon maturation in the Sembar Formation. • The high compaction and mineralogy of the Sembar Formation favour its hydraulic fracturing. • The lower part of the Sembar Formation meets the required criteria for hydrocarbon exploitation. [ABSTRACT FROM AUTHOR]

The evaluation of the interplay between kerogen maturation and total organic carbon (TOC) content within an organic-rich shale is critical to characterizing source rock shale's elastic behavior. We analysed a comprehensive database of twenty-nine well logs, two 3D seismic surveys, and geochemical data of eighteen wells within the Draupne Formation in the Norwegian North Sea. The Upper Jurassic (Kimmeridgian) Draupne Formation Shale is found throughout the Norwegian Continental Shelf and is the primary source, carrier, and seal rock in several oil and gas fields. The dataset explores a broad range of depths (2–5 km), total organic contents (0–20 wt%), and various maturation states ranging from immature to overmature. We study the relationship between rock brittleness and organic content as shale matures. We use calculated values of Young's modulus and Poisson's ratio. Academic and industry-standard cutoffs are applied to both the TOC content and maturation level. We introduce here the Organic Maturation Product (OMP) rock physics template, which incorporates a relationship between organic content and any subsequent maturation. The degree of brittleness or ductility is impacted directly by the kerogen maturation process, with lower TOC content having a dampening effect on the transition towards increasing brittleness. This template allows one to classify rock maturation in nine categories, from low to high maturation, and link these categories to rock geomechanical properties. Seismic inversion of the 3D surveys and mapping utilizing the OMP classification reveal how large-scale depositional environment and subsequent diagenetic events influence both placement and quality of source rock broadly within the basin context of the North Sea. • We propose a rock physics template, Organic Maturation Product (OMP), analyzing the relationship between TOC and maturation. • We introduce the Organic Maturation Product (OMP) template as a proxy for elastic stiffness in organic-rich shales. • This template shows how sedimentation and diagenesis influence the Draupne Formation in the Norwegian Continental Shelf. [ABSTRACT FROM AUTHOR]

Abstract: We present a methodology for generating pre-drill estimations of hydrate saturations using conventional 3D seismic data. These seismic-based estimates will be compared with well log derived saturations from the subsequently drilled wells of the Gulf of Mexico Gas Hydrate Joint Industry Project Leg II (JIP Leg II) expedition. Predicting saturation of gas hydrates (Sh-seismic) combines pre-stack seismic inversion, rock physics modeling and stratigraphic interpretation. Before the wells were drilled, no nearby sonic and density logs were available to define and calibrate the elastic property trends for the shallow target interval containing the gas hydrate stability zone. Therefore, rock property trends were established by applying principles of rock physics and shallow sediment compaction, constrained by known regional geological parameters. Sh-seismic volumes were generated by inverting pre-stack data to acoustic impedance (PI) and shear impedance (SI) volumes, and then analyzing deviations from modeled impedance trends. To enhance the quality of the inversion, the signal-to-noise ratio of the offset data was maximized by conditioning the seismic prior to inversion. Seismic stratigraphic interpretation plays an important role by identifying the more promising strata and structures for the presence of gas hydrates. The pre-drill Sh-seismic results are compared with saturations calculated from log data, Sh-log, of the wells drilled in the JIP Leg II campaign. Due to weaker seismic reflections, predictions may be less accurate for low concentrations, such as saturations less than 40%, and for thin intervals below the vertical resolution of the seismic data (about 15m). However, the integrated geophysical workflow is very effective for identifying and quantifying significant hydrate concentrations, making the method a promising prospecting technique. [Copyright &y& Elsevier]

Abstract: Modeling of grades is a key step and the major source of error in appraisal stage of mining projects. We used a geostatistical approach to explicitly integrate seismic travel time data, as well as acoustic and core logging data into the estimation of nickel grades in the Voisey''s Bay deposit. Firstly, the crosshole seismic travel times are inverted using a stochastic tomographic algorithm. This algorithm allows for the inclusion of acoustic log data and seismic covariance into the inverse problem, leading to high-resolution velocity tomographic images of the orebody. Secondly, grade realizations are generated using a Bayesian sequential Gaussian simulation algorithm, which integrates the ore grades measured on the core logs and the previously inverted tomographic data. The application of the presented method to the Voisey''s Bay deposit yields an improved knowledge of the geology setting and generates grade models with realistic spatial variability compared to conventional methods. [Copyright &y& Elsevier]

Abstract: In this work, we address the problem of transforming seismic reflection data into an intrinsic rock property model. Specifically, we present an application of a methodology that allows interpreters to obtain effective porosity 3D maps from post-stack 3D seismic amplitude data, using measured density and sonic well log data as constraints. In this methodology, a 3D acoustic impedance model is calculated from seismic reflection amplitudes by applying an L 1-norm sparse-spike inversion algorithm in the time domain, followed by a recursive inversion performed in the frequency domain. A 3D low-frequency impedance model is estimated by kriging interpolation of impedance values calculated from well log data. This low-frequency model is added to the inversion result which otherwise provides only a relative numerical scale. To convert acoustic impedance into a single reservoir property, a feed-forward Neural Network (NN) is trained, validated and tested using gamma-ray and acoustic impedance values observed at the well log positions as input and effective porosity values as target. The trained NN is then applied for the whole reservoir volume in order to obtain a 3D effective porosity model. While the particular conclusions drawn from the results obtained in this work cannot be generalized, such results suggest that this workflow can be applied successfully as an aid in reservoir characterization, especially when there is a strong non-linear relationship between effective porosity and acoustic impedance. [Copyright &y& Elsevier]

Abstract: Developing a geological model is the first and a very important step during the reservoir simulation and modeling process. The geological model usually represents our best interpretation of the reservoir characteristics that extends beyond the well where we have actual measurements (logs, core, well test, etc.). The only real measurement with a large areal extent that geoscientists have access to is seismic data. Therefore, using seismic data to populate the high-resolution geological model is becoming increasingly popular. Using reservoir characteristics at the wellbore as the control point helps geoscientists in measuring the goodness of the correlation they create between seismic data and well logs. This paper presents a unique approach in accomplishing this task. The uniqueness of this approach is based on the fact that (a) it reduces the complexity of the model building process by dividing a very complex problem into two slightly less complex problems (surface seismic to VSP and VSP to log—i.e., divide and conquer) and (b) it effectively employs a synthetic set of formations representing the actual sequence of geological layers in the field in order to build a model and learn from it, and then, apply the lessons learned to the model building process for the actual reservoir. The results show that this strategy proves to be successful. [ABSTRACT FROM AUTHOR]

Abstract: The determination of acoustic impedance distribution from the seismic data field measurement can be expressed as an ill-posed inverse problem. This work deals with the use of the Elman artificial neural network (ANN) (feedback connection) for the seismic data inversion. In the proposed structure the hidden neuron outputs from the previous time step are fed back to their inputs through time delay units; this enables them to process temporal behaviour and provide multi-step-ahead predictions. The ANN architectures and learning rules are presented to allow the best estimate of acoustic impedance from seismic data. The effects of network architectures using 5 to 60 neurons and 10 to 90 neurons in the hidden layer respectively for synthetic and real data on the rate of convergence and prediction accuracy of ANN models are discussed. The behaviour of networks observed on training data is very similar to the one observed on test data. The results obtained clearly prove the feasibility of the proposed method for seismic data inversion by feedback neural networks. Different tests indicate that the back-propagation conjugate gradient algorithm can easily train the proposed Elman ANN structure without getting stuck in local minima. [Copyright &y& Elsevier]

PRay is a graphical user interface for interactive displaying and editing of velocity models for seismic refraction. It is optimized for editing rayinvr models but can also be used as a dynamic viewer for ray tracing results from other software. The main features are the graphical editing of nodes and fast adjusting of the display (stations and phases). It can be extended by user-defined shell scripts and links to phase picking software. PRay is open source software written in the scripting language Perl, runs on Unix-like operating systems including Mac OS X and provides a version controlled source code repository for community development ( https://sourceforge.net/projects/pray-plot-rayinvr/ ). [ABSTRACT FROM AUTHOR]