Improved reservoir characterization by means of supervised machine learning and model-based seismic impedance inversion in the Penobscot field,Scotian Basin

The present research work attempted to delineate and characterize the reservoir facies from the Dawson Canyon Formation in the Penobscot field,Scotian Basin.An integrated study of instantaneous frequency,P-impedance,volume of clay and neutron-porosity attributes,and structural framework was done to unravel the Late Cretaceous depositional system and reservoir facies distribution patterns within the study area.Fault strikes were found in the EW and NEE-SWW directions indicating the dominant course of tectonic activities during the Late Cretaceous period in the region.P-impedance was estimated using model-based seismic inversion.Petrophysical properties such as the neutron porosity(NPHI)and volume of clay(VCL)were estimated using the multilayer perceptron neural network with high accuracy.Comparatively,a combination of low instantaneous frequency(15-30 Hz),moderate to high impedance(7000-9500 gm/cc*m/s),low neutron porosity(27%-40%)and low volume of clay(40%-60%),suggests fair-to-good sandstone development in the Dawson Canyon Formation.After calibration with the welllog data,it is found that further lowering in these attribute responses signifies the clean sandstone facies possibly containing hydrocarbons.The present study suggests that the shale lithofacies dominates the Late Cretaceous deposition(Dawson Canyon Formation)in the Penobscot field,Scotian Basin.Major faults and overlying shale facies provide structural and stratigraphic seals and act as a suitable hydrocarbon entrapment mechanism in the Dawson Canyon Formation's reservoirs.The present research advocates the integrated analysis of multi-attributes estimated using different methods to minimize the risk involved in hydrocarbon exploration.

Statistical Inversion Based on Nonlinear Weighted Anisotropic Total Variational Model and Its Application in Electrical Impedance Tomography

Electrical impedance tomography (EIT) aims to reconstruct the conductivity distribution using the boundary measured voltage potential. Traditional regularization based method would suffer from error propagation due to the iteration process. The statistical inverse problem method uses statistical inference to estimate unknown parameters. In this article, we develop a nonlinear weighted anisotropic total variation (NWATV) prior density function based on the recently proposed NWATV regularization method. We calculate the corresponding posterior density function, i.e., the solution of the EIT inverse problem in the statistical sense, via a modified Markov chain Monte Carlo (MCMC) sampling. We do numerical experiment to validate the proposed approach.

Model-constrained and data-driven double-supervision acoustic impedance inversion

Seismic impedance inversion is an important technique for structure identification and reservoir prediction.Model-based and data-driven impedance inversion are the commonly used inversion methods.In practice,the geophysical inversion problem is essentially an ill-posedness problem,which means that there are many solutions corresponding to the same seismic data.Therefore,regularization schemes,which can provide stable and unique inversion results to some extent,have been introduced into the objective function as constrain terms.Among them,given a low-frequency initial impedance model is the most commonly used regularization method,which can provide a smooth and stable solution.However,this model-based inversion method relies heavily on the initial model and the inversion result is band limited to the effective frequency bandwidth of seismic data,which cannot effectively improve the seismic vertical resolution and is difficult to be applied to complex structural regions.Therefore,we propose a data-driven approach for high-resolution impedance inversion based on the bidirectional long short-term memory recurrent neural network,which regards seismic data as time-series rather than image-like patches.Compared with the model-based inversion method,the data-driven approach provides higher resolution inversion results,which demonstrates the effectiveness of the data-driven method for recovering the high-frequency components.However,judging from the inversion results for characterization the spatial distribution of thin-layer sands,the accuracy of high-frequency components is difficult to guarantee.Therefore,we add the model constraint to the objective function to overcome the shortages of relying only on the data-driven schemes.First,constructing the supervisor1 based on the bidirectional long short-term memory recurrent neural network,which provides the predicted impedance with higher resolution.Then,convolution constraint as supervisor2 is introduced into the objective function to guarantee the reliability and accuracy of the inversion results,which makes the synthetic seismic data obtained from the inversion result consistent with the input data.Finally,we test the proposed scheme based on the synthetic and field seismic data.Compared to model-based and purely data-driven impedance inversion methods,the proposed approach provides more accurate and reliable inversion results while with higher vertical resolution and better spatial continuity.The inversion results accurately characterize the spatial distribution relationship of thin sands.The model tests demonstrate that the model-constrained and data-driven impedance inversion scheme can effectively improve the thin-layer structure characterization based on the seismic data.Moreover,tests on the oil field data indicate the practicality and adaptability of the proposed method.

Recursive impedance inversion of ground-penetrating radar data in stochastic media

The travel time and amplitude of ground-penetrating radar （GPR） waves are closely related to medium parameters such as water content, porosity, and dielectric permittivity. However, conventional estimation methods, which are mostly based on wave velocity, are not suitable for real complex media because of limited resolution. Impedance inversion uses the reflection coefficient of radar waves to directly calculate GPR impedance and other parameters of subsurface media. We construct a 3D multiscale stochastic medium model and use the mixed Gaussian and exponential autocorrelation function to describe the distribution of parameters in real subsurface media. We introduce an elliptical Gaussian function to describe local random anomalies. The tapering function is also introduced to reduce calculation errors caused by the numerical simulation of discrete grids. We derive the impedance inversion workflow and test the calculation precision in complex media. Finally, we use impedance inversion to process GPR field data in a polluted site in Mongolia. The inversion results were constrained using borehole data and validated by resistivity data.

Inversion of Seabed Geotechnical Properties in the Arctic Chukchi Deep Sea Basin Based on Time Domain Adaptive Search Matching Algorithm

The chirp sub-bottom profiler,for its high resolution,easy accessibility and cost-effectiveness,has been widely used in acoustic detection.In this paper,the acoustic impedance and grain size compositions were obtained based on the chirp sub-bottom profiler data collected in the Chukchi Plateau area during the 11th Arctic Expedition of China.The time-domain adaptive search matching algorithm was used and validated on our established theoretical model.The misfit between the inversion result and the theoretical model is less than 0.067%.The grain size was calculated according to the empirical relationship between the acoustic impedance and the grain size of the sediment.The average acoustic impedance of sub-seafloor strata is 2.5026×10^(6) kg(s m^(2))^(-1)and the average grain size(θvalue)of the seafloor surface sediment is 7.1498,indicating the predominant occurrence of very fine silt sediment in the study area.Comparison of the inversion results and the laboratory measurements of nearby borehole samples shows that they are in general agreement.

Analysis of the ambiguity of log-constrained seismic impedance inversion

Although the ambiguity of seismic inversion is widely recognized in both theory and practice, so far as a concrete inversion example is concerned, there is not any objective, controllable method or any standard for how to evaluate and determine its ambiguity and reliability, especially for the high frequency components beyond the effective seismic frequency band. Taking log-constrained impedance inversion as an example, a new appraisal method is proposed on the basis of analyzing a simple geological model. Firstly, the inverted impedance model is transformed to a reflection coefficient series. Secondly, the maximum effective frequency of the real seismic data is chosen as a cutoff point and the reflection coefficient series is decomposed into two components by low-pass and high-pass filters. Thirdly, the geometrical reflection characteristics of the high-frequency components and that of the real seismic data are compared and analyzed. Then, the reliability of the inverted impedance model is appraised according to the similarity of geometrical characteristics between the high-frequency components and the real seismic data. The new method avoids some subjectivity in appraising the inverted result, and helps to enhance the reliability of reservoir prediction by impedance inversion technology.

Lamé parameters inversion based on elastic impedance and its application

The Connolly （1999） elastic impedance （EI） equation is a function of P-wave velocity, S-wave velocity, density, and incidence angle. Conventional inversion methods based on this equation can only extract P-velocity, S-velocity, and density data directly and the elastic impedance at different incidence angles are not at the same scale, which makes comparison difficult. We propose a new elastic impedance equation based on the Gray et al. （1999） Zoeppritz approximation using Lamé parameters to address the conventional inversion method＇s deficiencies. This equation has been normalized to unify the elastic impedance dimensions at different angles and used for inversion. Lamé parameters can be extracted directly from the elastic impedance data obtained from inversion using the linear relation between Lamé parameters and elastic impedance. The application example shows that the elastic parameters extracted using this new method are more stable and correct and can recover the reservoir information very well. The new method is an improvement on the conventional method based on Connolly＇s equation.

Acoustic impedance inversion of zero-offset VSP data

Highly precise acoustic impedance inversion is a key technology for pre-drilling prediction by VSP data. In this paper, based on the facts that VSP data has high resolution, high signal to noise ratio, and the downgoing and upgoing waves can be accurately separated, we propose a method of predicting the impedance below the borehole in front of the bit using VSP data. First, the method of nonlinear iterative inversion is adopted to invert for impedance using the VSP corridor stack. Then, by modifying the damping factor in the iteration and using the preconditioned conjugate gradient method to solve the equations, the stability and convergence of the inversion results can be enhanced. The results of theoretical models and actual data demonstrate that the method is effective for pre-drilling prediction using VSP data.

Prediction of Overpressure Contributed Mainly by Structural Compression Using Impedance Inversion in Kuqa Depression, Tarim Basin

The origin and distribution of formation overpressure have effect not only on hydrocarbon migration and accumulation, but also on technique of drilling well. The study and prediction of overpressure are very important in basin analysis. At present, overpressure is mostly predicted by stack velocity. The process in calculating inter-velocity from stack velocity is very complex and inevitably leads to errors. Especially, this method is not available in the case that structural compression contribution to overpressure occurred. This paper introduces a new method, impedance inversion, to predict overpressure, and the principle is discussed. This method is used to predict the overpressure in Kuqa depression, Tarim basin and as a result, the absolute errors are less than 0.1, and relative errors are less than 5 % for predicted fluid pressure coefficients to the drill stem test (DST) measurements. It suggests that this method can be widely used to predict overpressure in foreland basins.

Two-dimensional regularized inversion of AMT data based on rotation invariant of Central impedance tensor

Considering the uncertainty of the electrical axis for two-dimensional audo-magnetotelluric(AMT) data processing, an AMT inversion method with the Central impedance tensor was presented. First, we present a calculation expression of the Central impedance tensor in AMT, which can be considered as the arithmetic mean of TE-polarization mode and TM-polarization mode in the twodimensional geo-electrical model. Second, a least-squares iterative inversion algorithm is established, based on a smoothnessconstrained model, and an improved L-curve method is adopted to determine the best regularization parameters. We then test the above inversion method with synthetic data and field data. The test results show that this two-dimensional AMT inversion scheme for the responses of Central impedance is effective and can reconstruct reasonable two-dimensional subsurface resistivity structures. We conclude that the Central impedance tensor is a useful tool for two-dimensional inversion of AMT data.

Model-data-driven P-wave impedance inversion using ResNets and the normalized zero-lag cross-correlation objective function

Model-driven and data-driven inversions are two prominent methods for obtaining P-wave impedance,which is significant in reservoir description and identification.Based on proper initial models,most model-driven methods primarily use the limited frequency bandwidth information of seismic data and can invert P-wave impedance with high accuracy,but not high resolution.Conventional data-driven methods mainly employ the information from well-log data and can provide high-accuracy and highresolution P-wave impedance owing to the superior nonlinear curve fitting capacity of neural networks.However,these methods require a significant number of training samples,which are frequently insufficient.To obtain P-wave impedance with both high accuracy and high resolution,we propose a model-data-driven inversion method using Res Nets and the normalized zero-lag cross-correlation objective function which is effective for avoiding local minima and suppressing random noise.By using initial models and training samples,the proposed model-data-driven method can invert P-wave impedance with satisfactory accuracy and resolution.Tests on synthetic and field data demonstrate the proposed method’s efficacy and practicability.

A comparison of deep learning methods for seismic impedance inversion

Deep learning is widely used for seismic impedance inversion,but few work provides in-depth research and analysis on designing the architectures of deep neural networks and choosing the network hyperparameters.This paper is dedicated to comprehensively studying on the significant aspects of deep neural networks that affect the inversion results.We experimentally reveal how network hyperparameters and architectures affect the inversion performance,and develop a series of methods which are proven to be effective in reconstructing high-frequency information in the estimated impedance model.Experiments demonstrate that the proposed multi-scale architecture is helpful to reconstruct more high-frequency details than a conventional network.Besides,the reconstruction of high-frequency information can be further promoted by introducing a perceptual loss and a generative adversarial network from the computer vision perspective.More importantly,the experimental results provide valuable references for designing proper network architectures in the seismic inversion problem.

Impedance inversion of pre-stack seismic data in the depth domain

The extensive application of pre-stack depth migration has produced huge volumes of seismic data,which allows for the possibility of developing seismic inversions of reservoir properties from seismic data in the depth domain.It is difficult to estimate seismic wavelets directly from seismic data due to the nonstationarity of the data in the depth domain.We conduct a velocity transformation of seismic data to make the seismic data stationary and then apply the ridge regression method to estimate a constant seismic wavelet.The estimated constant seismic wavelet is constructed as a set of space-variant seismic wavelets dominated by velocities at different spatial locations.Incorporating the weighted superposition principle,a synthetic seismogram is generated by directly employing the space-variant seismic wavelets in the depth domain.An inversion workflow based on the model-driven method is developed in the depth domain by incorporating the nonlinear conjugate gradient algorithm,which avoids additional data conversions between the time and depth domains.The impedance inversions of the synthetic and field seismic data in the depth domain show good results,which demonstrates that seismic inversion in the depth domain is feasible.The approach provides an alternative for forward numerical analyses and elastic property inversions of depth-domain seismic data.It is advantageous for further studies concerning the stability,accuracy,and efficiency of seismic inversions in the depth domain.

Improvement and application of preprocessing technique for multitrace seismic impedance inversion

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

Fluids discrimination by ray-path elastic impedance inversion: A successful case from Sulige tight gas field

Existing seismic prediction methods struggle to effectively discriminate between fluids in tight gas reservoirs,such as those in the Sulige gas field in the Ordos Basin,where porosity and permeability are extremely low and the relationship between gas and water is complicated.In this paper,we have proposed a comprehensive seismic fluid identification method that combines ray-path elastic impedance(REI)inversion with fluid substitution for tight reservoirs.This approach is grounded in geophysical theory,forward modeling,and real data applications.We used geophysics experiments in tight gas reservoirs to determine that Brie's model is better suited to calculate the elastic parameters of mixed fluids than the conventional Wood’s model.This yielded a more reasonable and accurate fluid substitution model for tight gas reservoirs.We developed a forward model and carried out inversion of REI.which reduced the non-uniqueness problem that has plagued elastic impedance inversion in the angle domain.Our well logging forward model in the ray-path domain with different fluid saturations based on a fluid substitution model proved that REI identifies fluids more accurately when the ray parameters are large.The distribution of gas saturation can be distinguished from the crossplot of REI(p=0.10)and porosity.The inverted ray-path elastic impedance profile was further used to predict the porosity and gas saturation profile.Our new method achieved good results in the application of 2D seismic data in the western Sulige gas field.

Seismic fluid identification using a nonlinear elastic impedance inversion method based on a fast Markov chain Monte Carlo method

Elastic impedance inversion with high efficiency and high stability has become one of the main directions of seismic pre-stack inversion. The nonlinear elastic impedance inversion method based on a fast Markov chain Monte Carlo （MCMC） method is proposed in this paper, combining conventional MCMC method based on global optimization with a preconditioned conjugate gradient （PCG） algorithm based on local optimization, so this method does not depend strongly on the initial model. It converges to the global optimum quickly and efficiently on the condition that effi- ciency and stability of inversion are both taken into consid- eration at the same time. The test data verify the feasibility and robustness of the method, and based on this method, we extract the effective pore-fluid bulk modulus, which is applied to reservoir fluid identification and detection, and consequently, a better result has been achieved.

Robust elastic impedance inversion using L1-norm misfit function and constraint regularization

The classical elastic impedance （EI） inversion method, however, is based on the L2-norm misfit function and considerably sensitive to outliers, assuming the noise of the seismic data to be the Guassian-distribution. So we have developed a more robust elastic impedance inversion based on the Ll-norm misfit function, and the noise is assumed to be non-Gaussian. Meanwhile, some regularization methods including the sparse constraint regularization and elastic impedance point constraint regularization are incorporated to improve the ill-posed characteristics of the seismic inversion problem. Firstly, we create the Ll-norm misfit objective function of pre-stack inversion problem based on the Bayesian scheme within the sparse constraint regularization and elastic impedance point constraint regularization. And then, we obtain more robust elastic impedances of different angles which are less sensitive to outliers in seismic data by using the IRLS strategy. Finally, we extract the P-wave and S-wave velocity and density by using the more stable parameter extraction method. Tests on synthetic data show that the P-wave and S-wave velocity and density parameters are still estimated reasonable with moderate noise. A test on the real data set shows that compared to the results of the classical elastic impedance inversion method, the estimated results using the proposed method can get better lateral continuity and more distinct show of the gas, verifying the feasibility and stability of the method.

Nonlinear Electrical Impedance Tomography Method Using a Complete Electrode Model for the Characterization of Heterogeneous Domains

This paper presents an electrical impedance tomography(EIT)method using a partial-differential-equationconstrained optimization approach.The forward problem in the inversion framework is described by a complete electrodemodel(CEM),which seeks the electric potential within the domain and at surface electrodes considering the contact impedance between them.The finite element solution of the electric potential has been validated using a commercial code.The inverse medium problem for reconstructing the unknown electrical conductivity profile is formulated as an optimization problem constrained by the CEM.The method seeks the optimal solution of the domain’s electrical conductivity to minimize a Lagrangian functional consisting of a least-squares objective functional and a regularization term.Enforcing the stationarity of the Lagrangian leads to state,adjoint,and control problems,which constitute the Karush-Kuhn-Tucker(KKT)first-order optimality conditions.Subsequently,the electrical conductivity profile of the domain is iteratively updated by solving the KKT conditions in the reduced space of the control variable.Numerical results show that the relative error of the measured and calculated electric potentials after the inversion is less than 1%,demonstrating the successful reconstruction of heterogeneous electrical conductivity profiles using the proposed EIT method.This method thus represents an application framework for nondestructive evaluation of structures and geotechnical site characterization.

Simultaneous Waveform Inverse Modelling for Litho-Fluid Prediction in an Old Marginal, “Agbbo”Field, Onshore Niger Delta, Nigeria

Simultaneous waveform inversion was used to predict lithofacies and fluid type across the field. Very often, characterizing reservoirs in terms of lithology and fluid type using conventional methods is replete with uncertainties, especially in marginal fields. An approach is employed in this study that integrated rock physics and waveform inverse modelling for lithology and fluid-type characterization to appropriately identify potential hydrocarbon saturated zones and their corresponding lithology. Seismic and well-log data were analyzed using Hampson Russel software. The method adopted includes lithofacies and fluid content analysis using rock physics parameters and seismic simultaneous inverse modelling. Rock physics analysis identified 2 broad reservoirs namely: HDZ1 and HDZ2 reservoirs. Results from the inverse modelling showed that low values of acoustic impedance from 19,743 to 20,487 (ft/s)(g/cc) reflect hydrocarbon-bearing reservoirs while medium to high values shows brine and shale respectively, with brine zone ranging from 20,487 to 22,531 (ft/s)(g/cc) and shale above 22,531 (ft/s)(g/cc). Two lithofacies were identified from inversion analysis of Vp/Vs and Mu-Rho, namely: sand and shale with VpVs 1.95 values respectively. Mu-Rho > 12.29 (GPa)(g/cc) and <12.29 (GPa) (g/cc) represent sand and shale respectively. From 3D volume, it was observed that a high accumulation of hydrocarbon was observed to be saturated at the north to the eastern part of the field forming a meandering channel. Sands were mainly distributed around the northeastern to the southwestern part of the field, that tends to be away from Well 029. This was also validated by the volume of rigidity modulus (Mu-Rho) showing high values indicating sands fall within the northeastern part of the field.

A Method of Improving Seismic Data Resolution：Comprehensive Inversion of Well logging and Seismic Data

Comprehensive inversion of logging and seismic data presented in this paper is a method to improve seismic data resolution. It involves using ample high-frequency information and complete low-frequency information of known logging to make up for the lack of limited bandwidth of practical seismic recording, obtaining an approximate reflection coefficient sequence (or wave impedance) of high resolution by iterative inversion and providing more reliable seismic evidence for further lithologic interpretation and lateral tracking, correlation and prediction of thin reservoir. The comprehensive inversion can be realized in the following steps: (1) to establish an initial model of higher resolution; (2) to obtain wavelets, and (3) to constrain iterative inversion. The key to this inversion lies in building an initial model. It is assumed from our experience that when the initial model is properly given, iterative inversion can be quickly converged to the ideal result.