Elastic Impedance Inversion Research Articles

Gas prediction in tight sandstones based on the rock-physics-derived seismic amplitude variation versus offset method

Estimating gas enrichments is a key objective in exploring sweet spots within tight sandstone gas reservoirs. However, the low sensitivity of elastic parameters to gas saturations in such formations makes it a significant challenge to reliably estimate gas enrichments using seismic methods. Through rock physical modeling and reservoir parameter analyses conducted in this study, a more suitable indicator for estimating gas enrichment, termed the gas content indicator, has been proposed. This indicator is formulated based on effective fluid bulk modulus and shear modulus and demonstrates a clear positive correlation with gas content in tight sandstones. Moreover, a new seismic amplitude variation versus offset (AVO) equation is derived to directly extract reservoir properties, such as the gas content indicator and porosity, from prestack seismic data. The accuracy of this proposed AVO equation is validated through comparison with the exact solutions provided by the Zoeppritz equation. To ensure reliable estimations of reservoir properties from partial angle-stacked seismic data, the proposed AVO equation is reformulated within the elastic impedance inversion framework. The estimated gas content indicator and porosity exhibit favorable agreement with logging data, suggesting that the obtained results are suitable for reliable predictions of tight sandstones with high gas enrichments. Furthermore, the proposed methods have the potential to stimulate the advancement of other suitable inversion techniques for directly estimating reservoir properties from seismic data across various petroleum resources.

Enhanced Estimation of Fluid Modulus and Porosity via Novel Nonlinear Elastic Impedance Inversion

Enhanced Estimation of Fluid Modulus and Porosity via Novel Nonlinear Elastic Impedance Inversion

An improved stochastic inversion method for 3D elastic impedance under the prior constraints of random medium parameters

Three-dimensional (3D) elastic impedance inversion is widely used in oil and gas exploration with its excellent identification of reservoirs and noise immunity. However, modifying the prior information directly in inversion will undoubtedly destroy the spatial autocorrelation structure of the elastic impedance. Maintaining the elastic impedance spatial autocorrelation structure in seismic inversion is of great significance for guiding the reservoir identification and prediction. The 3D prior information construction method is proposed under the constraint of random medium parameters. This method establishes an intrinsic connection between random medium parameters and elastic impedance based on seismic data and well data, which provides a theoretical basis for simulating prior information satisfying the spatial correlation of the subsurface medium. To ensure the invariance of the spatial structure term in the inversion process, the random term and the spatial structure term in the simulation process are separated by the gradual deformation method (GDM), and the priori constrained objective function is further constructed to reduce the inversion uncertainty. Numerical example shows that the relative error of the proposed simulation method is smaller than that of conventional sequential Gaussian simulation, and the details are more accurately depicted based on the priori inversion results in this study. In addition, the elastic impedance equation based on the p-wave modulus is derived by combining with the reservoir sensitive parameters in field data. The field examples document that this method can effectively perform high-resolution geophysical prediction of underground thin-layer reservoirs.

Identification of Reservoir Distribution Using Extended Elastic Impedance (EEI) Inversion in the "Z" Field of the Kutai Basin

This research was conducted using EEI inversion on seismic data in Z Field, Kutai Basin. The EEI inversion is effectively used to determine the reservoir distribution by eliminating the angle limit on the elastic impedance to the Chi angle so that it can be correlated with petrophysical parameters that are sensitive to lithology and fluids. The data used in this study are well data, checkshots, horizons, and partial-stack angle gather 3D seismic data. The data obtained is processed to obtain the target zone first based on log interpretation. Based on data processing, the target zone is obtained at 1513 m to 1531 m. Sensitivity analysis was conducted to determine the sensitive parameters, which can separate the lithology of the formation. In the sensitivity analysis, the most sensitive log to separate lithology is the Vp/VS log, which can separate sandstone, shale, and coal. Furthermore, the EEI inversion analysis was carried out to obtain the most suiTable model for the inversion, the Based Hard Constraint model was obtained with a correlation reaching 0.997 and an error value of 0.078. Based on the EEI inversion, the target zone in the Z-field at a depth of 1258 ms - 1269 ms with a sandstone reservoir in the EEI range of 6000 (m/s)(g/cc) - 7500 (m/s)(g/cc) which spreads from northeast to south. The distribution of the sandstone reservoir is surrounded by coal with a range of EEI 7500 (m/s)(g/cc) - 12000 (m/s)(g/cc), and also the distribution of shale in the EEI range of 7500(m/s)( g/cc) - 9200(m/s)(g/cc).

SPATIOTEMPORAL DISTRIBUTION OF THE CONGLOMERATE RESERVOIR JATIBARANG FORMATION, MELANDONG FIELD, NORTH-WEST JAVA BASIN, INDONESIA

Subsurface mapping of the distribution of the reservoir is essential to be conducted in order to minimize many risks such as financial losses and also to increase profit from hydrocarbon production. This research was conducted on the Jatibarang conglomerate reservoir in Melandong Field, North West Java Basin, Indonesia. There are three objectives of this study which are to perform elastic impedance (EI) seismic inversion using available 3D seismic data, to determine the most suitable elastic impedance angle for the data, and to map spatiotemporal distribution of the Jatibarang Formation reservoir in the Melandong field, North West Java basin, Indonesia. EI inversion was selected for this study using the inversion angle ranging from the near stack (5º-15º) to the far stack (20º-30º). Results from this study show that EI seismic inversion can help in detecting the distribution of the lithology and hydrocarbon within the target zone. Angle 5º is considered as the best EI angle for the studied data as indicated by a correlation value of 0.65. Moreover, EI angles 15º and 10º are less reliable as shown by their correlation value of 0.6 and 0.56, respectively. These results are expected to provide some new insights into the distribution of the Jatibarang reservoir, and help in exploration, exploitation, and development of oil and gas fields in Melandong Field, North West Java Basin, Indonesia.

Predicting Reservoir Petrophysical Geobodies from Seismic Data Using Enhanced Extended Elastic Impedance Inversion

The study aims to implement a high-resolution Extended Elastic Impedance (EEI) inversion to estimate the petrophysical properties (e.g., porosity, saturation and volume of shale) from seismic and well log data. The inversion resolves the pitfall of basic EEI inversion in inverting below-tuning seismic data. The resolution, dimensionality and absolute value of basic EEI inversion are improved by employing stochastic perturbation constrained by integrated energy spectra attribute in a Bayesian Markov Chain Monte Carlo framework. A general regression neural network (GRNN) is trained to learn and memorize the relationship between the stochastically perturbed EEI and the associated well petrophysical log data. The trained GRNN is then used to predict the petrophysical properties of any given stochastic processed EEI. The proposed inversion was successfully conducted to invert the volume of shale, porosity and water saturation of a 4.0 m thick gas sand reservoir in Sarawak Basin, Malaysia. The three petrophysical geobodies were successfully built using the discovery wells cut-off values, showing that the inverted petrophysical properties satisfactorily reconstruct the well petrophysical logs with sufficient resolution and an accurate absolute value at the well site and are laterally conformable with seismic data. Inversion provides reliable petrophysical properties prediction that potentially helps further reservoir development for the study field.

VALIDATION OF EXTENDED ELASTIC IMPEDANCE BASED ON MINIMUM ENERGY ANGLE. A CASE STUDY APPLICATION FOR OPTIMIZED PETROLEUM RESERVOIR CHARACTERIZATION

This work intends to showcase a validation of the applicability of Extended Elastic Impedance (EEI) inversion method in reservoir characterization and modeling. In order to achieve that, deterministic seismic inversion and extended elastic impedance (EEI) analysis were applied to obtain quantitative estimates of reservoir properties over the Pu field of the West African Congo basin. Optimum EEI angles corresponding to the reservoir properties were then analyzed using well logs data, together with a lithology indicator. Pre-stack seismic data were simultaneously inverted into density, acoustic and gradient impedances cubes, through model based inversion algorithm. The last two broadband inverted volumes were then projected to corresponding Chi angles proportionate to petrophysical indicators, thus resulting to two broadband EEI volumes. At well locations, the EEI versus petrophysical parameters linear trends were then applied to convert EEI volumes into porosity and shale volumes based on specified lithology. In order to generate reservoir facies distribution, minimum angle was applied based on background EEI, thus allowing for mapping of reservoir facies. In order to validate the EEI approach, a Geo-statistical model was further developed for the same field. Hence, from porosity, shale content and background EEI cubes, a comparison was made between the properties generated from the EEI and that generated based on geo-statistical method, which shows that EEI is a robust way of reservoir characterization that pinpointing favorable reservoir potentials which shall guide future drilling locations.

An improved seismic fluid identification method incorporating squirt flow and frequency-dependent fluid-solid inversion

Joint inversion of seismic amplitude and frequency information for fluid discrimination works as a popular approach for reservoir fluid identification. However, ignorance of the factor caused by fluid flow and the viscosity of oil and gas in current frequency-dependent inversion approaches leads to the inaccuracy of oil and gas prediction. Therefore, based on squirt flow and viscoelastic theory, a new frequency-dependent viscoelastic squirt-flow fluid factor is established, which is compared with other conventional fluid factors to verify its advantages in fluid detection. In addition, a new linearized reflectivity equation related to frequency-dependent viscoelastic squirt-flow fluid factor is derived. Finally, to verify the feasibility of the new fluid factor in predicting oil/gas, a frequency-dependent elastic impedance inversion method is introduced. Synthetic data and field data examples find that frequency-dependent viscoelastic squirt-flow fluid factor can eliminate the interference of “false bright spots” in conventional fluid factors inversion and has a higher vertical resolution, which demonstrated the effectiveness and stability of our method in fluid prediction.

Broadband Nonlinear Elastic Impedance Inversion Based on Modified Convolution Model

Broadband Nonlinear Elastic Impedance Inversion Based on Modified Convolution Model

Elastic impedance inversion incorporating fusion initial model and kernel Fisher discriminant analysis approach

Seismic inversion is a significant technique for estimating petroleum reservoir parameters. The low frequency component of the initial model represents the geological background information, which plays an important role in the seismic inversion. It is challenging to precisely depict the actual geological model in seismic inversion because of the inherent velocity-depth ambiguity. Therefore, the initial model which is closer to genuine geological backdrop is essential. We propose a workflow which estimates a fusion initial model based on data fusion algorithms. It is well known that seismic facies analysis can provide more low-frequency information about the geological background. For example, the boundaries of sedimentary bodies can be represented by seismic facies classification data. We utilize a combination of the seismic facies classification data and well curves interpolation initial models to accurately invert the special geological body with the support of a feature-level fusion algorithm. Then, a practical pre-stack seismic inversion method is implemented, and a field data example further demonstrates its applicability and steadiness in seismic inversion.

Elastic impedance inversion for stress indicator in weakly orthorhombic media

Hydraulic fracturing is a crucial technology for improving permeability and production of shale reservoirs. The precise estimation of the stress distribution has a significant guidance for optimizing the placement of hydraulic fracturing. Assuming that the shale gas reservoir is a weakly anisotropic medium with orthorhombic symmetry, a new stress indicator parameterized by rock mechanical parameters and fracture parameters is firstly presented to predict the differential horizontal stress ratio in shale gas reservoirs. Then, we derive a novel simplified P-to-P reflection coefficient and a logarithmic normalized elastic impedance (EI) as a function of Young's modulus, Poisson's ratio, Thomsen's WA parameter δb and normal excess compliance ZN. Next, we adopt azimuthal EI inversion in a Bayesian framework to estimate rock mechanics parameters and fracture parameters directly on a fractured shale gas field seismic data. Finally, the stress indicator is determined by utilizing four inverted parameters. Synthetic examples demonstrate that the proposed approach produces stable parameter estimates even with moderate noise, verifying the feasibility and effectiveness of the method. Test on a field data set illustrates that the inversion results can be reasonably estimated, and the stress indicator derived by the inversion accords with the geomechanics result. Compared with the previous method, the new stress indicator has a higher capability to describe stress characteristics in the shale reservoir. We conclude that this stress evaluation procedure can provide reliable guidance for well location deployment and hydraulic fracturing reformation.

Seismic characterization of in situ stress in orthorhombic shale reservoirs using anisotropic extended elastic impedance inversion

Seismic characterization of anisotropic in situ stress is of great importance in improving the success of planning drilling and hydraulic fracturing treatment. Shales generally exhibit orthorhombic elastic symmetry due to the presence of vertical fractures and horizontal fine layering. We propose a novel anisotropic extended elastic impedance (AEEI) inversion approach for in situ stress estimates in shale gas reservoirs with weakly orthorhombic symmetry. Considering an orthorhombic model formed by a system of aligned vertical fractures embedded in a vertical transverse isotropic (VTI) background rock, we first derive the in situ stress expression by combining poroelastic Hooke’s law and linear slip theory. Next, we deduce a linearized PP-wave reflection coefficient as a function of fluid bulk modulus, vertical effective stress-sensitive parameter, dry-rock P- and S-wave moduli, density, dry normal and tangential weaknesses of the VTI background, and dry normal and tangential weaknesses of vertical fractures. To estimate in situ stress from the observed seismic data, we derive the AEEI and Fourier coefficients (FCs) expression and establish a three-step AEEI inversion workflow involving (1) Bayesian seismic inversion for intercept impedance, gradient impedance, and curvature impedance estimates; (2) estimating model parameters using the FCs of AEEI; and (3) estimating in situ stress using the inverted model parameters. The synthetic examples demonstrate that in situ stress can be reliably estimated even with moderate noise. Test on a real data set implies that the proposed method can generate reasonable results of in situ stress that are helpful for optimizing the horizontal drilling and hydraulic fracture stimulations of shale gas reservoirs.

Complex spherical-wave elastic inversion using amplitude and phase reflection information

Unlike the real-valued plane wave reflection coefficient (PRC) at the pre-critical incident angles, the frequency-and depth-dependent spherical-wave reflection coefficient (SRC) is more accurate and always a complex value, which contains more reflection amplitude and phase information. In near field, the imaginary part of complex SRC (phase) cannot be ignored, but it is rarely considered in seismic inversion. To promote the practical application of spherical-wave seismic inversion, a novel spherical-wave inversion strategy is implemented. The complex-valued spherical-wave synthetic seismograms can be obtained by using a simple harmonic superposition model. It is assumed that geophone can only record the real part of complex-valued seismogram. The imaginary part can be further obtained by the Hilbert transform operator. We also propose the concept of complex spherical-wave elastic impedance (EI) and the complex spherical-wave EI equation. Finally, a novel complex spherical-wave EI inversion approach is proposed, which can fully use the reflection information of amplitude, phase, and frequency. With the inverted complex spherical-wave EI, the velocities and density can be further extracted. Synthetic data and field data examples show that the elastic parameters can be reasonably estimated, which illustrate the potential of our spherical-wave inversion approach in practical applications.

Elastic Impedance Simultaneous Inversion for Multiple Partial Angle Stack Seismic Data with Joint Sparse Constraint

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.

Density stability estimation method from pre-stack seismic data

As one of the most important elastic parameters in seismic exploration, density plays an important role in lithology identification and hydrocarbon indicator. Based on the approximate Zoeppritz equation, the density term is usually estimated using AVO/AVA (Amplitude variation with offset/incident angle) inversion. However, the small contribution of density to the reflection coefficient or the strong correlation with other parameters in the approximate equation renders the density estimation tough. By analyzing the contribution of density to reflection coefficient in different approximate equations, Aki-Richards is finally selected as the inversion equation, in which the density term has a higher contribution to the reflectivity. The conventional inversion approach is to invert several parameters simultaneously on the basis of approximate equation. The same convergence criterion is used for the estimation of the three parameters, but the density estimation error fails to converge to the minimum due to its small sensitivity, which makes density inversion unstable. Furthermore, the conventional inversion methods involve the inverse of large matrix, which also reduces the stability of density estimation. Therefore, a new form of Aki-Richards approximate reflection equation is derived on the assumption that the inverse of coefficient matrix exists. The reflectivity of elastic parameter is independently expressed as the weighted superposition of seismic reflection coefficients at different incident angles. Then utilizing well logging data and seismic reflection coefficient estimated by elastic impedance inversion as constraints, the inverse of coefficient matrix in inversion equation is estimated directly, which avoids inverse of large matrix and improves the precision of density estimation. In physical, the weighted coefficients directly reflect the contribution of elastic parameters reflectivity to seismic data. Finally, different experimental examples show that proposed method can stably estimate density term.

Elastic impedance inversion method based on Quantum Annealing MH algorithm

Seismic inversion is one of the important methods to realize reservoir prediction in oil and gas field exploration. The seismic stochastic inversion based on Metropolis-Hastings algorithm makes full use of well logging data and improves the vertical resolution of inversion results. However, conventional Metropolis-Hastings algorithm has slow convergence speed and poor stability. In this paper, an elastic impedance inversion method based on optimized Metropolis-Hastings algorithm is established by combining quantum annealing algorithm with Metropolis-Hastings algorithm. The feasibility of the method is verified by model calculation and actual data test.

Facies Driven Elastic Impedance Inversion Based on Fusion Initial Model

Facies Driven Elastic Impedance Inversion Based on Fusion Initial Model

Elastic Property Modeling, Extended Elastic Impedance (EEI) and Curve-Pseudo Elastic Impedance (CPEI) Inversion for Pore Type Analysis and Hydrocarbon Distribution in Carbonate Reservoir, Kujung I Formation, “Humaira” Field, North East Java Basin

The complexity of the pore shape in carbonate rocks causes the need for a special strategy to characterize carbonate reservoir. The more information used, the more accurate the reservoir characterization will be. Pore type analysis is the important study because it relates to the fluid flow properties. The elastic property modeling show a good match to the actual data. The results of the well log and petrophysical data analysis show that the gas zone is located at the upper side of Kujung I Formation. Based on rock physics modeling result, the possible pore type developing in the Kujung I Formation is reference pore with the dominance of the aspect ratio value of about 0.17-0.19. The carbonate layer containing hydrocarbons is characterized by low Lamda-Rho, Lamda/Mu values and a low Poisson ratio. Porous carbonate layer, characterized by a low Mu-Rho value. The slice results show that the gaseous area is located on the anticline. The zone that has good porosity indicated by low Mu-Rho. In the IN-3 well there are no hydrocarbons, this analysis is in accordance with the geological condition of the IN-3 well which is in a low area on the time structure map. The inversion results show a good match between CPEI against water saturation log and CPEI against porosity log.

Viscoelastic Fluid Factor Inversion and Application in Luojia Oilfield Based on Broadband Impedance

Based on the physical quantity of log data, the accurate identification of oil- and gas-bearing properties may be caused by the prestack inversion of fluid prediction, which will affect the success rate of exploration and development. Prestack data contain more information of amplitude and frequency. Using the frequency-dependent viscoelastic impedance equation and Bayesian inversion framework, the objective function of frequency-dependent elastic impedance inversion can be established to realize the frequency-dependent impedance inversion at different angles. According to the elastic impedance equation of the frequency-varying viscoelastic fluid factor, the relationship between elastic impedance and the frequency-dependent viscoelastic fluid factor is established, and the prestack seismic inversion method of the frequency-dependent viscoelastic fluid factor is studied. However, one of the important factors easily neglected is that we have been using logging data to establish fluid-sensitive parameters and the lithophysical version for fluid identification, so there are differences between logging and seismic frequency bands for fluid identification. The indicator factors with higher sensitivity to fluid can be selected by laboratory measurements. This article applies this method on Luojia oilfield data and verifies this method with log interpretation results, based on the sample of rock physics obtained in a low-frequency rock physics experiment; the technique of dispersion and fluid-sensitive parameters is studied, and the fluid prediction technology of a multifrequency band rock physics template is adopted, which can build the relationship between rock physical elastic parameters and fluid properties by the multifrequency broadband impedance method.

Hydrocarbon reservoir delineation using simultaneous and elastic impedance inversions in a Niger Delta field

The global energy demand is increasing while production from mature fields is drastically reducing consequently, oil and gas industries are expanding activities into more challenging areas. The inability of the traditional seismic data to properly delineate hydrocarbon reservoirs from subtle seismic features in ‘Sandfish’ field located offshore, Niger Delta informed the use of simultaneous and elastic impedance inversion. The elastic and derived volumes from seismic inversion would reduce risk, enhance hydrocarbon discovery and optimize development plans in the study area. Four ‘Sandfish’ (Sfn) wells (Sfn-01, Sfn-02, Sfn-04 and Sfn-05), check-shots and 3D seismic data of five angle stacks (6–12°, 12–18°, 18–26°, 26–32° and 32–42°) were used in the study. Low frequency (0–2 Hz) models were generated from interpolation of high-cut-filtered compressional wave velocity log (P-sonic), shear wave velocity log (S-sonic) and density log guided by interpreted four seismic horizons. The low frequency models broaden the spectrum of the elastic volumes and also served as inversion constraints. The five partial angle stacks varying from 6–42° were simultaneously inverted using Jason’s Rock-Trace® inversion software which iterated trial inversions until the model sufficiently matched the seismic data. The near (6–12°) angle and far-far (32–42°) angle stacks were also inverted and compared with the inverted volumes from the simultaneous inversion. This was carried out to determine the effectiveness of near and far-far elastic impedance volume in delineating hydrocarbon reservoirs. The inverted elastic volumes P-impedance (ZP), S-impedance (ZS), density (ρ), near and far-far elastic and derived volumes lambda-rho (λρ), mu-rho (µρ), Poisson’s-ratio (σ) reveal vertical and lateral continuity of the reservoirs identified (K01, N01 and P01) at 2179 m, 2484 m and 3048 m, respectively. The delineated reservoirs showed good match with the sand tops away from the well control validated by a blind well test. The cross-plot of inverted ZP from simultaneous inversion and well ZP gave correlation coefficient of 86% indicative of high quality inverted volume which will reduce exploration risk. The plot of inverted ZP from simultaneous inversion and inverted far-far elastic volume reflected 82% correlation coefficient indicating that this method could be adopted in other fields with limited data and similar geological setting. Hence, the study has shown the efficacy of elastic volumes in delineating hydrocarbon reservoirs which can help locate optimum region for development wells.