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Data assimilation in reservoir modeling often involves model variables that are multimodal, such as porosity and permeability. Well established data assimilation methods such as ensemble Kalman filter and ensemble smoother approaches, are based on Gaussian assumptions that are not applicable to multimodal random variables. The selection ensemble smoother is introduced as an alternative to traditional ensemble methods. In the proposed method, the prior distribution of the model variables, for example the porosity field, is a selection-Gaussian distribution, which allows modeling of the multimodal behavior of the posterior ensemble. The proposed approach is applied for validation on a two-dimensional synthetic channelized reservoir. In the application, an unknown reservoir model of porosity and permeability is estimated from the measured data. Seismic and production data are assumed to be repeatedly measured in time and the reservoir model is updated every time new data are assimilated. The example shows that the selection ensemble Kalman model improves the characterisation of the bimodality of the model parameters compared to the results of the ensemble smoother. [ABSTRACT FROM AUTHOR]

Seismic impedance inversion is one of the most commonly used techniques for reservoir characterization. High accuracy and high resolution seismic impedance is a prerequisite for subsequent reservoir interpretation. The data-driven approach offers the opportunity for accurate impedance prediction by establishing a nonlinear mapping between seismic data and impedance. However, existing data-driven methods take the raw seismic data directly as input, making it difficult for the network to learn high frequency weak signal information and resulting in low resolution inversion results. In order to mitigate the above issues, a data-driven seismic impedance inversion method based on multi-scale strategy is proposed. The method first obtains seismic data at different scales using frequency division techniques and do normalization on the extracted multi-scale data to ensure the consistency of the seismic signal energy in different frequency bands. The multi-scale seismic data will then be fed into the network, which helps the network to learn the high frequency information features more easily, and ultimately obtain higher resolution inversion results. We use the most commonly used convolutional neural network (CNN) as an example to demonstrate that the proposed multi-scale data-driven seismic impedance inversion method can improve the resolution of the inversion results. In addition, since the above seismic impedance inversion method is executed trace-by-trace, the f-x prediction filtering technique is introduced to improve the lateral continuity of the inversion results and obtain more geologically reliable impedance profiles. The validity of the proposed method is verified in the application of synthetic model data as well as an actual data set.

Hydraulic fracturing (HF) has been used for years to enhance oil and gas production from conventional and unconventional reservoirs. HF in enhanced geothermal systems (EGS) has become increasingly common in recent years. In EGS, hydraulic fracturing creates a geothermal collector in impermeable or low-permeable hot dry rocks. Artificial fracture networks in the collector allow for a continuous flow of fluid in a loop connecting at least two wells (injector and producer). However, it is challenging to assess the fracability of geothermal reservoirs for EGS. Consequently, it is necessary to design a method that considers multiple parameters when evaluating the potential of geothermal development. This study proposes an improved fracability index model (FI) based on the influences of fracability-related geomechanical and petrophysical properties. These include brittle minerals composition, fracture toughness, minimum horizontal in-situ stress, a brittleness index model, and temperature effect to quantify the rock’s fracability. The hierarchical analytic framework was designed based on the correlation between the influencing factors and rock fracability. The results of the qualitative and quantitative approaches were integrated into a mathematical evaluation model. The improved fracability index model’s reliability was evaluated using well logs and 3D seismic data on low-permeable carbonate geothermal reservoirs and shale gas horizontal wells. The results reveal that the improved FI model effectively demonstrates brittle regions in the low-permeable carbonate geothermal reservoir and long horizontal section of shale reservoir. We divide the rock fracability into three levels: FI > 0.59 (the rock fracability is good); 0.59 > FI > 0.32 (the rock fracability is medium); and FI < 0.32, (the rock fracability is poor). The improved FI model can assist in resolving the uncertainties associated with fracability interpretation in determining the optimum location of perforation clusters for hydraulic fracture initiation and propagation in enhanced geothermal systems.

A 3D velocity model was created by using stacking velocity of 9 seismic lines and average velocity of 6 wells drilled in Iraq. The model was achieved by creating a time model to 25 surfaces with an interval time between each two successive surfaces of about 100 msec. The summation time of all surfaces reached about 2400 msec, that was adopted according to West Kifl-1 well, which penetrated to a depth of 6000 m, representing the deepest well in the study area. The seismic lines and well data were converted to build a 3D cube time model and the velocity was spread on the model. The seismic inversion modeling of the elastic properties of the horizon and well data was applied to achieve a corrected velocity cube. Then, the velocity cube was converted to a time model and, finally, a corrected 3D depth model was obtained. This model shows that the western side of the study area, which is a part of the stable shelf, is characterized by relatively low thickness and high velocity layers. While the eastern side of the study area, which is a part of the Mesopotamian, is characterized by high thickness and low velocity of the Cretaceous succession. The Abu Jir fault is considered as a boundary between the stable and unstable shelves in Iraq, situated at the extreme west part of the study area. The area of relatively high velocity gradient is considered as the limit of the western side of the Mesopotamian basin. This area extends from Najaf-Karbala axis in the west to the Euphrates River in the east. It is found that the 3D stacking velocity model can be used to obtain good results concerning the tectonic boundary. [ABSTRACT FROM AUTHOR]

The characterization of carbonate rocks is a challenging process compared to siliciclastics because of their more intricate pore-space structure. In this study, we applied rock physics methods to a heterogeneous carbonate reservoir located in south-west Iran to identify zones of various pore types, including inter-particle pores, stiff (vuggy and moldic) pores, and cracks, from geophysical measurements. We first constructed two rock physics templates (RPTs) using well logs and used them to quantitatively analyze the effect of various pore types on elastic properties. Using these RPTs, we then implemented an inversion algorithm to estimate the volume fractions of various pores using total porosity and P-wave velocity (Vp) derived from well logs. Next, we have compared the pore-type inversion results and the image log interpretation/core images at the corresponding depth intervals to validate inversion results. This comparison showed that the inversion results are consistent with the measurements. Also, we applied the introduced pore-type inversion algorithm on seismic data to achieve insight into pore-type distribution in the 3D framework of the reservoir under study. The results of these rock physics-based analyses indicate that the inter-particle pores are dominant in the pore-space, while there are stiff pores and dispersal cracks in some subzones of the studied depth interval. Additionally, employing the Xu and Payne rock physics modeling procedure, P- and S-wave velocities were estimated using pore-type inversion results at the location of several wells from the studied field. The calculated mean absolute error and the correlation coefficient indicate a high consistency between the measured and modeled velocities. This research may contribute to permeability prediction and analysis of the diagenetic processes' impact on reservoir properties. [ABSTRACT FROM AUTHOR]

Petrophysical properties have played an important and definitive role in the study of oil and gas reservoirs, necessitating that diverse kinds of information are used to infer these properties. In this study, the seismic data related to the Hendijan oil field were utilised, along with the available logs of 7 wells of this field, in order to use the extracted relationships between seismic attributes and the values of the shale volume in the wells to estimate the shale volume in wells intervals. After the overall survey of data, a seismic line was selected and seismic inversion methods (model‐based, band limited and sparse spike inversion) were applied to it. Amongst all of these techniques, the model‐based method presented the better results. By using seismic attributes and artificial neural networks, the shale volume was then estimated using three types of neural networks, namely the probabilistic neural network (PNN), multi‐layer feed‐forward network (MLFN) and radial basic function network (RBFN). [ABSTRACT FROM AUTHOR]

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

The Lower Indus Basin in Pakistan has its significance and has proven oil and gas potential in Pakistan. To explore its hidden realities on basis of advance technologies of exploration geophysics, the present study has carried out to open new horizons in the field of exploration geophysics. It combines study of stratigraphical and structural trends of Lower Indus basin for each formation on regional scales including evaluation of resource potential based on seismic and well log data. The seismic interpretation models are based on iso velocity depth contour method and seismic inversion models are based on sparse spike models. The seismic inversion data approach was used first time on Indus basin to understand trapping mechanism of hydrocarbons with performing reservoir characterization. Tomography imaging has reveal earth properties and the focal mechanism inversion covered the geological formations. The data integration techniques such as co-kriging or neural networks were used for single and multiobjective function optimization for formations evaluation of entire area. The seismic amplitude and inversion anomaly, potentially indicative of porous reservoirs, are located in each formation. The results of cretaceous sand's reserve for lower Indus basin prove compressionional tectonics with many anticlinal and faults. The study is an important support for prospect evaluation and hydrocarbon reserve estimations and was necessary to delineate unexplored parts. [ABSTRACT FROM AUTHOR]

An ensemble-based method for seismic inversion to estimate elastic attributes is considered, namely the iterative ensemble Kalman smoother. The main focus of this work is the challenge associated with ensemble-based inversion of seismic waveform data. The amount of seismic data is large and, depending on ensemble size, it cannot be processed in a single batch. Instead a solution strategy of partitioning the data recordings in time windows and processing these sequentially is suggested. This work demonstrates how this partitioning can be done adaptively, with a focus on reliable and efficient estimation. The adaptivity relies on an analysis of the update direction used in the iterative procedure, and an interpretation of contributions from prior and likelihood to this update. The idea is that these must balance; if the prior dominates, the estimation process is inefficient while the estimation is likely to overfit and diverge if data dominates. Two approaches to meet this balance are formulated and evaluated. One is based on an interpretation of eigenvalue distributions and how this enters and affects weighting of prior and likelihood contributions. The other is based on balancing the norm magnitude of prior and likelihood vector components in the update. Only the latter is found to sufficiently regularize the data window. Although no guarantees for avoiding ensemble divergence are provided in the paper, the results of the adaptive procedure indicate that robust estimation performance can be achieved for ensemble-based inversion of seismic waveform data. [ABSTRACT FROM AUTHOR]

The purpose of seismic reservoir characterization is to predict the spatial distribution of the subsurface rock properties from a set of direct and indirect measurements. Commonly, rock property volumes are obtained in a two-steps approach. First, the elastic properties are inverted from seismic reflection data and then used to compute rock properties volumes by applying a calibrated rock physics models. Such an approach is not only time consuming but may also lead to biased results as the uncertainties related to seismic inversion may not be propagated through the entire geomodeling workflow. Here, we propose an iterative geostatistical shale rock physics seismic AVA inversion method to invert seismic reflection data directly for shale rock properties. The workflow consists of three main steps starting from shale properties model generation of brittleness, total organic carbon (TOC), and porosity using stochastic sequential simulation and cosimulation and calculation of a volume of shale. In the following step, elastic property volumes are calculated based on a calibrated shale rock physics model using the self-consistent approximation. The elastic models are then used for the calculation of the synthetic seismic data. In the final step, the misfit between synthetic and real data is calculated and used as part of the stochastic update of the model parameter space. The whole process is repeated until a minimum misfit between the observed and synthetic seismic is achieved. The proposed method is successfully tested on an onshore Lower Paleozoic shale gas reservoir in Northern Poland, and the predicted shale rock properties agree with those observed at a blind-well location. [ABSTRACT FROM AUTHOR]

The Prediction of the reservoir characteristics from seismic amplitude data is a main challenge. Especially in the Nile Delta Basin, where the subsurface geology is complex and the reservoirs are highly heterogeneous. Modern seismic reservoir characterization methodologies are spanning around attributes analysis, deterministic and stochastic inversion methods, Amplitude Variation with Offset (AVO) interpretations, and stack rotations. These methodologies proved good outcomes in detecting the gas sand reservoirs and quantifying the reservoir properties. However, when the pre-stack seismic data is not available, most of the AVO-related inversion methods cannot be implemented. Moreover, there is no direct link between the seismic amplitude data and most of the reservoir properties, such as hydrocarbon saturation, many assumptions are imbedded and the results are questionable. Application of Artificial Neural Network (ANN) algorithms to predict the reservoir characteristics is a new emerging trend. The main advantage of the ANN algorithm over the other seismic reservoir characterization methodologies is the ability to build nonlinear relationships between the petrophysical logs and seismic data. Hence, it can be used to predict various reservoir properties in a 3D space with a reasonable amount of accuracy. We implemented the ANN method on the Sequoia gas field, Offshore Nile Delta, to predict the reservoir petrophysical properties from the seismic amplitude data. The chosen algorithm was the Probabilistic Neural Network (PNN). One well was kept apart from the analysis and used later as blind quality control to test the results. [ABSTRACT FROM AUTHOR]

Regularization parameter selection (RPS) is one of the most important tasks in solving inverse problems. The most common approaches seek the optimal regularization parameter (ORP) from a sequence of candidate values. However, these methods are often time-consuming because they need to conduct the estimation process on all candidate values, and they are always restricted to solve certain problem types. In this paper, we propose a novel machine learning-based prediction framework (MLBP) for the RPS problem. The MLBP first generates a large number of synthetic data by varying the inputs with different noise conditions. Then, MLBP extracts some pre-defined features to represent the input data and computes the ORP of each synthetic example by using true models. The pairs of ORP and extracted features construct a training set, which is used to train a regression model to describe the relationship between the ORP and input data. Therefore, for newly practical inverse problems, MLBP can predict their ORPs directly with the pre-trained regression model, avoiding wasting computational resources on improper regularization parameters. The numerical results also show that MLBP requires significantly less computing time and provides more accurate solutions for different tasks than traditional methods. Especially, even though the MLBP trains the regression model on synthetic data, it can also achieve satisfying performance when directly applied to field data. [ABSTRACT FROM AUTHOR]

Pre-stack seismic inversion, well log analysis approach and empirical relations were adopted in this study to better estimate geomechanical properties of Ruby field with minimum error. The use of conventional well log empirical method alone to evaluate geomechanical properties in oil/gas fields sometimes becomes problematic. Geomechanical properties were divided into: elastic moduli [Young's modulus, shear modulus, bulk modulus and Poisson ratio (PR)] and rock mechanical strength properties (closure stress ratio (CSR), brittleness (BRI) and compressibility). Four geomechanical earth models (CSR, BRI, Young's modulus and PR) were generated from the inversion analysis to understand the distribution of rock strength properties across the field. The results deciphered high Young's, shear and bulk modulus in the reservoir zone compared to the cap/seal rocks and a decrease in PR. This implies that, the cap/seal are more ductile and less compressible than the reservoir rocks, indicating that the reservoirs are highly brittle. CSR result reveals high in cap/seal indicating that the cap/seal rock are harder to fracture and has a greater chance to withstand higher compressive stress before failing as opposed to reservoir rocks. The inverted earth model shows that, Young's modulus and brittleness increase toward the northeastern part of the field, while CSR and PR increase toward the southwestern part of the field. These results suggest that harder, stiffer, highly compressible and easily fractured rocks are found in the northern and eastern part of the field as opposed to the southern to western part of the field that is ductile. [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]

Abstract Seismic inversion involves extracting qualitative as well as quantitative information from seismic reflection data that can be analyzed to enhance geological and geophysical interpretation which is more subtle in a traditional seismic data interpretation. Among many approaches that have been made to improve interpretation of post-stack seismic data, a great effort has been made to use maximum likelihood (ML), sparse spike inversion (SSI) along with multi-attribute analysis (MAA) aimed to increase the resolution power of interpreting seismic reflection data and mapping into the subsurface lithology. These methods are applied to the Blackfoot seismic reflection data to estimate reservoir. The methods were first applied to the composite trace close to well locations and were inverted for acoustic impedance (AI). The results depict that the inverted AI matches very well with the well log AI. The statistical analysis demonstrates good performance of the algorithm. Thereafter, the entire seismic section was inverted to acoustic impedance section. The analysis of the inverted impedance section shows an anomaly zone in between 1060 and 1075 ms time and characterize it as reservoir. Further, the multi-attribute analysis is performed to estimate porosity and density in the inter-well region. The inverted porosity section shows a high porosity anomaly and a low density anomaly in between 1060 and 1075 ms time intervals which corroborated well with the low impedance zone and confirm the presence of a reservoir.

In an attempt to reduce the errors and uncertainties associated with predicting reservoir properties for static modeling, seismic inversion was integrated with artificial neural network for improved porosity and water saturation prediction in the undrilled prospective area of the study field, where hydrocarbon presence had been confirmed. Two supervised neural network techniques (MLFN and PNN) were adopted in the feasibility study performed to predict reservoir properties, using P-impedance volumes generated from model-based inversion process as the major secondary constraint parameter. Results of the feasibility study for predicted porosity with PNN gave a better result than MLFN, when correlated with well porosity, with a correlation coefficient of 0.96 and 0.69, respectively. Validation of the prediction revealed a cross-validation correlation of 0.88 and 0.26, respectively, for both techniques, when a random transfer function derived from a given well is applied on other well locations. Prediction of water saturation using PNN also gave a better result than MLFN with correlation coefficient of 0.97 and 0.57 and cross-validation correlation coefficient of 0.89 and 0.3, respectively. Hence, PNN technique was adopted to predict both reservoir properties in the field. The porosity and water saturation predicted from seismic in the prospective area were 24–30% and 20–30%, respectively. This indicates the presence of good quality hydrocarbon bearing sand within the prospective region of the studied reservoir. As such, the results from the integrated techniques can be relied upon to predict and populate static models with very good representative subsurface reservoir properties for reserves estimation before and after drilling wells in the prospective zone of reservoirs. [ABSTRACT FROM AUTHOR]

Elastic impedance (EI) inversion for partial angle stack seismic data is a key technology in seismic reservoir prediction within the oil and gas industry. EI inversion provides a consistent framework to invert partial angle stack seismic data, just as the AI inversion does for post-stack data. The commonly used EI inversion process is angle by angle. Hence, the inverted EI for different angles may be nonconforming, especially for the seismic data with a low signal-to-noise ratio. This paper proposes to simultaneously invert multiple partial angle stack seismic data to obtain EI for different angles at once. To obtain conformable EI, we used the joint sparse constraint on the reflection coefficients for different angles. Then, the objective function for simultaneous EI inversion was constructed. Next, synthetic seismic data profiles with three different angles were used to show the superiority of the proposed EI inversion method compared to the conventional method. At last, a real seismic data line was used to test the feasibility of the proposed method in practice. The inversion results of synthetic data and real data showed that it provides an effective new alternative method to estimate EI from partial stack seismic data.

Seismic inversion is an effective way to get stress distribution of rock in coal mine, but its usage is still limited by multi-solution problem in practice. As seismometers can only be deployed in limited areas in underground coal mine, it is impossible to solve multi-solution problem by adding more seismometers, thus, we propose a new seismic inversion method which introduces stress value changes as constraint to solve this problem. Numerical experiments show that the new method can reduce the mean distance between inversion results and true wave velocity distributions by about 11.5%, reduce the variance of the distances by about 91%, reduce the mean distance between inversion results and frequency-domain-truncated true wave velocity distributions by about 30.6% and reduce the variance of the distances by about 84.1%. These results indicate that the new method proposed in this paper can reduce the non-uniqueness of solutions in seismic inversion and improve the reliability and stability of seismic inversion. [ABSTRACT FROM AUTHOR]

Seismic inversion problems often involve strong nonlinear relationships between model and data so that their misfit functions usually have many local minima. Global optimization methods are well known to be able to find the global minimum without requiring an accurate initial model. However, when the dimensionality of model space becomes large, global optimization methods will converge slow, which seriously hinders their applications in large-dimensional seismic inversion problems. In this article, we propose a new method for large-dimensional seismic inversion based on global optimization and a machine learning technique called autoencoder. Benefiting from the dimensionality reduction characteristics of autoencoder, the proposed method converts the original large-dimensional seismic inversion problem into a low-dimensional one that can be effectively and efficiently solved by global optimization. We apply the proposed method to seismic impedance inversion problems to test its performance. We use a trace-by-trace inversion strategy, and regularization is used to guarantee the lateral continuity of the inverted model. Well-log data with accurate velocity and density are the prerequisite of the inversion strategy to work effectively. Numerical results of both synthetic and field data examples clearly demonstrate that the proposed method can converge faster and yield better inversion results compared with common methods. [ABSTRACT FROM AUTHOR]

Reconstructing an accurate and high-resolution subsurface model is attractive in the fields of both geology and seismology. However, due to the band-limited characteristics of seismic data, the inversion greatly depends on the reliability of the initial model. A fairly acceptable initial model could lay a good foundation for seismic inversion. In this article, we first introduce a well-log interpolation method with the local slope as a constraint for building a high-fidelity starting model in prestack amplitude versus offset/angle (AVO/AVA) inversion. First, we briefly review the basic theory of general seismic inversion. Then, instead of using the conventional preconditioned least-squares method, we introduce shaping regularization theory into the geological structure-guided well-log interpolation to accelerate the convergence. We use the plane-wave destruction (PWD) algorithm to extract the slope attribute from seismic data, images, or velocity models. The slope is used as the constraint to solve the inverse problem based on the shaping regularization method. Numerical examples demonstrate that the proposed initial model building method performs better than the conventional ones. It greatly improves the accuracy of inversion results. Furthermore, we apply the proposed model building method to the inverse problems of AVO/AVA inversion and reservoir parameter estimation of several field data sets for the first time, which demonstrate encouraging performance. [ABSTRACT FROM AUTHOR]

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

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

In this letter, we propose a new global optimization method for nonlinear seismic inversion problems. The proposed method is a development of the existing method MMDE-Net by introducing a learnable strategy for choosing problem-dependent basis vectors and regularization parameters that are considered to be fixed in MMDE-Net. We name the proposed method as the optimized MMDE-Net (OMMDE-Net) and investigate its performance in seismic inversion through both synthetic and field data examples. The experimental results demonstrate that OMMDE-Net has advantages over MMDE-Net in effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

One of the characteristics that struck me most about André when I first met him in 1980 was the constant desire to expose, discuss and, above all, make accessible the emerging ideas of geostatistics. It was easy for André's first group of Stanford students to learn and have a great deal of confidence in what they learned. It was clearly a breakthrough in relation to the existing geostatistics teaching and research culture. It was in this context that the Fourth International Geostatistics Conference was organised in 1992 in Tróia, and André was the main driving force in the organisation of that event. It was an impressive showcase of the brilliant ideas of the newly created Stanford Center for Reservoir Forecasting. The resolution of an inverse problem with the integration of seismic in the high-resolution stochastic subsurface models was one of the most remarkable nuggets presented at that conference. Louis Bortoli, Francois Alabert, André Haas and André Journel are the authors of the seminal paper on geostatistical inversion. As an interface methodology between two areas of knowledge, it was not easy to get this new type of model accepted by the geophysics community, which at the time was dominated by deterministic models of inversion. This paper presents a summary of this path and the main geostatistical methods for seismic inversion that have been developed since then, and which have today become a set of practical tools for characterising mineral resources in the petroleum and mining industry. [ABSTRACT FROM AUTHOR]

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

Estimation of quartz content (QC) is important for gas hydrate production. However, the existing methods pay more attention to estimate saturations of hydrate or free gas instead of QC. QC is difficult to be estimated because of its limited and unclear influences on velocities in the hydrate-bearing sediments. A workflow was proposed to estimate QC from core to logging to seismic inversion whose core technologies were an unconsolidated anisotropic model (UAM) and an inverse modeling approach. We used the UAM to construct the quantitative relationships between physical properties including QC and velocity. Then, the velocities of the reservoirs were obtained by logging-constrained seismic inversion. Finally, QC can be scaled by the inverse modeling of the UAM. To build the UAM, we analyzed the physical properties of hydrate reservoirs based on the cores and logging while drilling (LWD) data in the Shenhu area, South China Sea, and characterized unconsolidated sediments with horizontally layered hydrates and gas occurrences. The calculated P-velocities and S-velocities from the UAM agreed with the LWD data when the input variables were QC, porosity, hydrate saturation, and gas saturation. Conversely, for a group of P-velocity and S-velocity from seismic inversion, the corresponding QC can be scaled out as well as the other parameters based on the UAM, which was defined as an inverse modeling. Because the significant parameters such as hydrate saturation have been considered as independent variables in the model, we can effectively avoid the correlation between QC and the others. Prediction multiplicity can be reduced. The estimated QC was consistent with the drilling and geological understanding in the field application, indicating that the method proposed is effective and practical. [ABSTRACT FROM AUTHOR]

Direct hydrocarbon indicator (DHI) expressions observed on seismic could arise due to various geological conditions. Such expression could lead to misinterpretation as hydrocarbon presence if not properly analyzed. This study employs rock physics attributes analysis to evaluate an identified prospect in the undrilled area of the studied reservoir. Prospect identification was actualized by analyzing structural and amplitude maps of the reservoir, which revealed a possible roll over anticline at both the exploited and prospective zone, with a very good amplitude support that conforms to structure. Well-based cross-plot analysis adopted four cross-plot techniques for feasibility study to test the applicability of rock physics for prospect evaluation in the field; Lambda-Rho versus Lambda-Rho/Mu-Rho ratio; Mu-Rho versus Lambda-Rho; and Poisson Ratio versus P-impedance. The result presented Poisson ratio, Lambda-Rho and Lambda/Mu-Rho ratio as good fluid indicator and Mu-Rho as a viable lithology indicator. As such, they were selected for seismic-based attribute and cross-plot analysis to validate the identified prospect. The results from seismic-based analysis showed consistency in the expression of the analyzed attribute at both the exploited and prospective zone. The seismic-based cross-plot analysis result was similar to the well-based analysis and was able to confirm that the observed amplitude expression in the exploited zone is an indication of hydrocarbon-bearing sand. [ABSTRACT FROM AUTHOR]

Abstract The main reservoirs in the Linguado Field are grainstone and packstone carbonates composed of oolites, oncolites, pelloids, and rare bioclasts, of the Quissamã Formation (Albian-age) and coquinas of the Coqueiros Formation (Aptian-age). Within Quissamã Formation, facies have high porosity variations and their 3D characterization is of utter importance to allow for a better location of the wells. The objective of this study was a volumetric characterization of Quissamã Formation by means of an adapted classical workflow composed of well-seismic tie, seismic preconditioning, seismic interpretation, seismic inversion, petrophysical properties estimation, and porosity geostatistical modeling. Seismic inversion results allowed the interpretation of a horizon to separate the carbonate platform in an upper zone, with low acoustic impedance, from a lower zone, with high acoustic impedance. Finally, behavioral knowledge of acoustic impedance and the porosity distribution within the Albian carbonate platform helped to better characterize the distribution of carbonaceous facies allowing the confirmation that commercial wells were drilled in low acoustic impedance and high porosity regions. It was also possible to confirm that a well, declared as noncommercial, was drilled in a poor reservoir region with high acoustic impedance and low porosity and identified other locations that were not yet drilled but could represent potential targets.

Using seismic data, logging information, geological interpretation data, and petrophysical data, it is possible to estimate the stratigraphic texture and elastic parameters of a study area via a seismic inversion. As such, a seismic inversion is an indispensable tool in the field of oil and gas exploration and development. However, due to unknown natural factors, seismic inversions are often ill-conditioned problems. One way to work around this unknowable information is to determine the solution to the seismic inversion using regularization methods after adding further a priori constraints. In this study, the nonconvex L1−2 regularization method is innovatively applied to the three-parameter prestack amplitude variation angle (AVA) inversion. A forward model is first derived based on the Fatti approximate formula and then low-frequency models for P impedance, S impedance, and density are established using logging and horizon data. In the Bayesian inversion framework, we derive the objective function of the prestack AVA inversion. To further improve the accuracy and stability of the inversion results, we remove the correlations between the elastic parameters that act as initial constraints in the inversion. Then, the objective function is solved by the nonconvex L1−2 regularization method. Finally, we validate our inversion method by applying it to synthetic and observational data sets. The results show that our nonconvex L1−2 regularization seismic inversion method yields results that are highly accurate, laterally continuous, and can be used to identify and locate reservoir formation boundaries. Overall, our method will be a useful tool in future work focused on predicting the location of reservoirs.

In the present study, seismic and well log information is incorporated with a multi-layer feed-forward neural network (MLFN) to predict porosity in the inter-well region. The aim of this study is to estimate a relationship between porosity and impedance to characterize the reservoir, if any, in the offshore F3 block, the Netherlands. MLFN is used to generate a connection between porosity logs and a set of seismic attributes, which are further used for porosity prediction. Modelbased inversion is employed to produce an acoustic impedance volume, which is a reliable technique for quantitative estimation of reservoir characteristics and acoustic impedance. The model-based inversion results indicate that the acoustic impedance (AI) in the region varies from 2500 to 6200 m/s*g/cm3, which is comparatively low and indicates loose formation. Thereafter, AI along with other attributes estimated from seismic data, is used as an input in MLFN, and porosity is predicted. The technique is first implemented on the traces close to well locations, and the findings are correlated with well log information, and after appropriate matching, the entire seismic segment is inverted for porosity. The results indicate that the porosity varies from 0.07 to 0.40. Further, a relationship between predicted porosity and inverted impedance is derived to represent the connection between these two parameters in the region. Moreover, based on this study, it is concluded that there is no significant reservoir in the region. However, as the analyses are performed for a specific range of data, it is possible that other parts of the area may have a different stratigraphy and possibility of the primary reservoir in the area. [ABSTRACT FROM AUTHOR]

The main reservoirs in the Linguado Field are grainstone and packstone carbonates composed of oolites, oncolites, pelloids, and rare bioclasts, of the Quissamã Formation (Albian-age) and coquinas of the Coqueiros Formation (Aptian-age). Within Quissamã Formation, facies have high porosity variations and their 3D characterization is of utter importance to allow for a better location of the wells. The objective of this study was a volumetric characterization of Quissamã Formation by means of an adapted classical workflow composed of well-seismic tie, seismic preconditioning, seismic interpretation, seismic inversion, petrophysical properties estimation, and porosity geostatistical modeling. Seismic inversion results allowed the interpretation of a horizon to separate the carbonate platform in an upper zone, with low acoustic impedance, from a lower zone, with high acoustic impedance. Finally, behavioral knowledge of acoustic impedance and the porosity distribution within the Albian carbonate platform helped to better characterize the distribution of carbonaceous facies allowing the confirmation that commercial wells were drilled in low acoustic impedance and high porosity regions. It was also possible to confirm that a well, declared as noncommercial, was drilled in a poor reservoir region with high acoustic impedance and low porosity and identified other locations that were not yet drilled but could represent potential targets. [ABSTRACT FROM AUTHOR]

For full-waveform inversion (FWI), building the salt structure velocity model is a challenging problem. In the absence of a high-fidelity initial model, low-frequency seismic data are indispensable for accurate reconstruction of the long-wavelength components of salt domes, which are often missing in field data. The envelope isolates the amplitude information from the seismic data and has a stronger linear relationship with the velocity than the seismic data. It is used to enhance the convexity of the objective function in an envelope inversion (EI). However, the properties of the envelope are not effectively utilized in EI due to the lack of their proper understanding. In order to solve the problems that EI has in building the salt structure velocity, such as the cycle-skipping problem, the gradient calculation problem caused by the waveform Fréchet derivative, and the multisolution problem caused by the missing polarity information of the envelope, we propose a multiscale direct signed EI (MSDSEI) based on the direct envelope Fréchet derivative and multiscale signed envelope. The development process from EI to MSDSEI also inspired us to understand the physical mechanism involved in the low-frequency components of the envelope: the signed envelope can be regarded as a low-pass filter of the reflection sequences in the subsurface. Thus, we use the smoothness and phase-independent properties of the envelope to propose an envelope-based full-band seismic data reconstruction method for multiscale FWI. Finally, the performance of various EI methods is verified on the Sigsbee2A model. [ABSTRACT FROM AUTHOR]

Seismic inversion is a process of predicting high-resolution stratigraphic parameters from low-resolution seismic data. Traditional inversion methods tend to impose human prior knowledge, such as sparsity, to the modeling of the seismic inversion process. Nowadays, with the development of deep learning, the idea of modeling by learning from data has gained great attention in varieties of research fields. As a data-driven method, an artificial neural network (ANN) has already been explored by many researchers in the field of seismic inversion. Compared to ANN, a convolutional neural network (CNN) has a stronger learning ability attributing to its sophisticated structures. However, the development of CNN is limited by the amount of labeled data in many industrial fields including the field of seismic inversion. In order to mitigate the dependence of CNN on the amount of labeled data, we propose a closed-loop CNN structure in this article. The proposed closed-loop CNN can model the seismic forward and inversion process simultaneously from the training data set. Compared to traditional CNN, which is in an open-loop form, closed-loop CNN can not only learn from labeled data but also extract information contained in unlabeled data. The experimental results show that the closed-loop CNN has a better performance than both traditional methods and other deep learning-based methods on the synthetic data set and also can be efficiently applied on the real seismic data set. [ABSTRACT FROM AUTHOR]

Attenuation exists in seismic wave propagation in subsurface layers, and relatively high attenuation occurs in oil-bearing reservoirs. Inversion of frequency components of observed seismic data generates values of attenuation factor 1/Q, which produces potential results for determining oil-bearing reservoirs. Beginning with expressions of seismic wave velocity in attenuating media, we involve P-wave maximum attenuation factor to rewrite P-wave velocity driven by an attenuating rock physics model, and we also employ an empirical relationship between P-wave attenuation factor and S-wave attenuation factor to express S-wave velocity in terms of P-wave maximum attenuation factor. Using the derived P- and S-wave velocities, we extend Zoeppritz equations to compute reflection coefficient for an interface separating two attenuating media. Under the assumption that contrasts in elastic properties of two media across the interface are small and the background attenuation is weak, we propose a linearized reflection coefficient of PP-wave as a function of contrasts in elastic parameters (i.e., P-wave velocity, S-wave velocity and density) and attenuation factor, and expression of elastic impedance (EI) is also presented. Based on the EI, we demonstrate an approach of estimating elastic parameters and attenuation factor from frequency components of partially incidence-stacked seismic data, which is implemented as a two-step inversion involving the prediction of EI datasets using a model-based damping least-squares algorithm and nonlinear inversion for elastic parameters and attenuation factor. Noisy synthetic seismic data generated using the extended Zoeppritz equations are employed to verify the robustness and stability of the proposed inversion approach. Applying the proposed approach to a real dataset acquired over an oil-bearing reservoir, we obtain convincing results of P-wave velocity, S-wave velocity, density and attenuation factor, which can reasonably match corresponding well log data. [ABSTRACT FROM AUTHOR]

To complement velocity distributions, seismic attenuation provides additional important information on fluid properties of hydrocarbon reservoirs in exploration seismology, as well as temperature distributions, partial melting, and water content within the crust and mantle in earthquake seismology. Full waveform inversion (FWI), as one of the state‐of‐the‐art seismic imaging techniques, can produce high‐resolution constraints for subsurface (an)elastic parameters by minimizing the difference between observed and predicted seismograms. Traditional waveform inversion for attenuation is commonly based on the standard‐linear‐solid (SLS) wave equation, in which case the quality factor (Q) has to be converted to stress and strain relaxation times. When using multiple attenuation mechanisms in the SLS method, it is difficult to directly estimate these relaxation time parameters. Based on a time domain complex‐valued viscoacoustic wave equation, we present an FWI framework for simultaneously estimating subsurface P wave velocity and attenuation distributions. Because Q is explicitly incorporated into the viscoacoustic wave equation, we directly derive P wave velocity and Q sensitivity kernels using the adjoint‐state method and simultaneously estimate their subsurface distributions. By analyzing the Gauss‐Newton Hessian, we observe strong interparameter crosstalk, especially the leakage from velocity to Q. We approximate the Hessian inverse using a preconditioned L‐BFGS method in viscoacoustic FWI, which enables us to successfully reduce interparameter crosstalk and produce accurate velocity and attenuation models. Numerical examples demonstrate the feasibility and robustness of the proposed method for simultaneously mapping complex velocity and Q distributions in the subsurface. Key Points: Subsurface velocity and attenuation structures are important for studying hydrocarbon reservoirs, geothermal dynamics, and magma chambersAn inversion scheme is developed for simultaneously mapping P wave velocity and attenuation based on a complex‐valued wave equationAn approximated Gauss‐Newton Hessian is utilized to reduce interparameter crosstalks and produce accurate velocity and attenuation models [ABSTRACT FROM AUTHOR]

Time lapse (4D) seismic is a valuable technology that provides vital information for field development plans and reservoir management. 4D seismic is extensively being used by oil and gas companies as a reservoir surveillance tool for better estimation of reservoir dynamic properties and reducing uncertainties of simulation models. As a result of production and enhance oil recovery activities (EOR) reservoir dynamic properties such as fluid saturation, pressure and temperature are being subjected to change which can occur independently or a combination of changes may happen in a reservoir. In this article 4D seismic technology is used to qualitatively monitor fluid saturation distribution and remaining oil using repeated 3D seismic surveys of base and monitor which are acquired in 1995 and 2006 respectively. This study integrates seismic data (base and monitor) and well log data from a mature field in the Malay basin with six years of water injection history. To predict water saturation post-stack seismic inversion is performed to extract acoustic impedance of base and monitor surveys and used them as an input for artificial neural network (ANN). 4D seismic data analysis enabled us to qualitatively interpret and identify areas of bypassed oil, generate maps of water movement pattern and guide the infill drilling activities. This methodology has the potential to further extend the life of the field and improving the recovery factor. [ABSTRACT FROM AUTHOR]

Automatic history matching using production and seismic data is still challenging due to the size of seismic datasets. The most severe problem, when applying ensemble-based methods for assimilating large datasets, is that the uncertainty is usually underestimated due to the limited number of models in the ensemble compared with the dimension of the data, which inevitably leads to an ensemble collapse. Localization and data reduction methods are promising approaches mitigating this problem. In this paper, we present a new robust and flexible workflow for assimilating seismic attributes and production data. The methodology is based on sparse representation of the seismic data, using methods developed for image denoising. We propose to assimilate production and seismic data simultaneously, and to ensure equal weight on these data types, we apply scaling based on the initial data match. Further, a newly developed flexible correlation-based localization technique is used for both data types. The workflow is successfully implemented for the released Norne benchmark dataset, and an iterative ensemble smoother is used for the simultaneous assimilation of production and seismic data. We show that the methodology is robust and ensemble collapse is avoided. Furthermore, the proposed workflow is flexible, as it can be applied to seismic data or inverted seismic properties, and the methodology requires only moderate computer memory. The results show that through this method, we can successfully reduce the data mismatch for both production data and seismic data. [ABSTRACT FROM AUTHOR]

Carbon capture and storage is being pursued globally as a geoengineering measure for reducing the emission of anthropogenic CO2 into the atmosphere. Comprehensive monitoring, verification, and accounting programs must be established for demonstrating the safe storage of injected CO 2. One of the most commonly deployed monitoring techniques is time‐lapse seismic reservoir monitoring (also known as 4‐D seismic), which involves comparing 3‐D seismic survey data taken at the same study site but over different times. Analyses of 4‐D seismic data volumes can help improve the quality of storage reservoir characterization, track the movement of injected CO 2 plume, and identify potential CO 2 spillover/leakage from the storage reservoirblue. However, the derivation of high‐resolution CO 2 saturation maps from 4‐D seismic data is a highly nonlinear and ill‐posed inverse problem, often requiring significant computational effort. In this research, we apply a physics‐based deep learning method to facilitate the solution of both the forward and inverse problems in seismic inversion while honoring physical constraints. A cycle generative adversarial neural network (CycleGAN) model is trained to learn the bidirectional functional mappings between the reservoir dynamic property changes and seismic attribute changes, such that both forward and inverse solutions can be obtained efficiently from the trained model. We show that our CycleGAN‐based approach not only improves the reliability of 4‐D seismic inversion but also expedites the quantitative interpretation. Our deep learning‐based workflow is generic and can be readily used for reservoir characterization and reservoir model updates involving the use of 4‐D seismic data. Key Points: We develop a cycle generative adversarial neural network (CycleGAN) model for time lapse seismic data inversion in carbon storage reservoirsCycleGAN combines reservoir simulation and rock physics inversion to invert high‐resolution maps of CO 2 saturation from acoustic impedanceWe show that CycleGAN can successfully identify the bidirectional mappings between CO 2 saturation changes and acoustic impedance changes [ABSTRACT FROM AUTHOR]

We propose a new method to tackle the mapping challenge from time-series data to spatial image in the field of seismic exploration, i.e., reconstructing the velocity model directly from seismic data by deep neural networks (DNNs). The conventional way of addressing this ill-posed inversion problem is through iterative algorithms, which suffer from poor nonlinear mapping and strong nonuniqueness. Other attempts may either import human intervention errors or underuse seismic data. The challenge for DNNs mainly lies in the weak spatial correspondence, the uncertain reflection–reception relationship between seismic data and velocity model, as well as the time-varying property of seismic data. To tackle these challenges, we propose end-to-end seismic inversion networks (SeisInvNets) with novel components to make the best use of all seismic data. Specifically, we start with every seismic trace and enhance it with its neighborhood information, its observation setup, and the global context of its corresponding seismic profile. From the enhanced seismic traces, the spatially aligned feature maps can be learned and further concatenated to reconstruct a velocity model. In general, we let every seismic trace contribute to the reconstruction of the whole velocity model by finding spatial correspondence. The proposed SeisInvNet consistently produces improvements over the baselines and achieves promising performance on our synthesized and proposed SeisInv data set according to various evaluation metrics. The inversion results are more consistent with the target from the aspects of velocity values, subsurface structures, and geological interfaces. Moreover, the mechanism and the generalization of the proposed method are discussed and verified. Nevertheless, the generalization of deep-learning-based inversion methods on real data is still challenging and considering physics may be one potential solution. [ABSTRACT FROM AUTHOR]

We invert three‐dimensional seismic data by a multiscale phase inversion scheme, a modified version of full waveform inversion, which applies higher order integrations to the input signal to produce low‐boost signals. These low‐boost signals are used as the input data for the early iterations, and lower order integrations are computed at the later iterations. The advantages of multiscale phase inversion are that it (1) is less dependent on the initial model compared to full waveform inversion, (2) is less sensitive to incorrectly modelled magnitudes and (3) employs a simple and natural frequency shaping filtering. For a layered model with a three‐dimensional velocity anomaly, results with synthetic data show that multiscale phase inversion can sometimes provide a noticeably more accurate velocity profile than full waveform inversion. Results with the Society of Exploration Geophysicists/European Association of Geoscientists and Engineers overthrust model shows that multiscale phase inversion more clearly resolves meandering channels in the depth slices. However, the data and model misfit functions achieve about the same values after 50 iterations. The results with three‐dimensional ocean‐bottom cable data show that, compared to the full waveform inversion tomogram, the three‐dimensional multiscale phase inversion tomogram provides a better match to the well log, and better flattens angle‐domain common image gathers. The problem is that the tomograms at the well log provide an incomplete low‐wavenumber estimate of the log's velocity profile. Therefore, a good low‐wavenumber estimate of the velocity model is still needed for an accurate multiscale phase inversion tomogram. [ABSTRACT FROM AUTHOR]

The seismic amplitude versus offset (AVO) analysis has become a prominent in the direct hydrocarbon indicator in last decade, aimed to characterizing the fluid content or the lithology of a possible reservoir and reducing the exploration drilling risk. Our research discusses the impact of studying common depth point gathers on Near, Mid and Far-offsets, to verify the credibility of the amplitude response in the prospect evaluation, through analyzing a case study of two exploratory wells; one has already penetrated a gas-bearing sandstone reservoir and the second one is dry sand, but drilled in two different prospects, using the AVO analysis, to understand the reservoir configuration and its relation to the different amplitude response. The results show that the missing of the short-offset data is the reason of the false anomaly encountered in the dry sand, due to some urban surface obstacles during acquiring the seismic data in the field, especially the study area is located in El Mansoura city, which it is a highly cultivated terrain, with multiple channels and many large orchards on the edge of the river, and sugar cane and rice fields. Several lessons have been learned, which how to differentiate between the gas reservoirs and non-reservoirs, by understanding the relation between the Near and Far-offset traces, to reduce the amplitude anomalies to their right justification, where missing of Near-offset data led to a pseudo-amplitude anomaly. The results led to a high success of exploration ratio as the positives vastly outweigh the negatives. [ABSTRACT FROM AUTHOR]

We embedded the genetic algorithm (GA) into the inner loop of the simulated annealing (SA) with a special designed junction. This strategy can boost the tunability of the searching process by providing two scales of regulation in seismic inversion problems. Moreover, a quantified uncertainty of the inversion result can be given when we put this strategy under Bayesian framework. Real data tests are conducted to support the theoretical calculation. Based on the conventional sparse spike inversion results, as a part of the prior information, our proposed method presents a superior quality and convincible uncertainty description. [ABSTRACT FROM AUTHOR]

It is common in ensemble-based methods of history matching to evaluate the adequacy of the initial ensemble of models through visual comparison between actual observations and data predictions prior to data assimilation. If the model is appropriate, then the observed data should look plausible when compared to the distribution of realizations of simulated data. The principle of data coverage alone is, however, not an effective method for model criticism, as coverage can often be obtained by increasing the variability in a single model parameter. In this paper, we propose a methodology for determining the suitability of a model before data assimilation, particularly aimed for real cases with large numbers of model parameters, large amounts of data, and correlated observation errors. This model diagnostic is based on an approximation of the Mahalanobis distance between the observations and the ensemble of predictions in high-dimensional spaces. We applied our methodology to two different examples: a Gaussian example which shows that our shrinkage estimate of the covariance matrix is a better discriminator of outliers than the pseudo-inverse and a diagonal approximation of this matrix; and an example using data from the Norne field. In this second test, we used actual production, repeat formation tester, and inverted seismic data to evaluate the suitability of the initial reservoir simulation model and seismic model. Despite the good data coverage, our model diagnostic suggested that model improvement was necessary. After modifying the model, it was validated against the observations and is now ready for history matching to production and seismic data. This shows that the proposed methodology for the evaluation of the adequacy of the model is suitable for large realistic problems. [ABSTRACT FROM AUTHOR]