Pre-stack Inversion Method Research Articles

Classification and Prediction Method for Dolomite Reservoirs in the Permian Maokou Formation in the Sichuan Basin

The Lower Permian Maokou Formation in the Hechuan area is one of the key horizons for natural gas exploration in the Sichuan Basin, showing a low degree of exploration and great exploration potential. The Lower Permian gas reservoir is mainly controlled by the reservoir, therefore it is subject to the development of the Maokou Formation reservoir. However, due to the strong heterogeneity and significant variations in physical properties of the Maokou Formation dolomite reservoir in the Hechuan area, there are significant differences in seismic response characteristics of reservoirs with different porosities. Through comprehensive analysis of gas testing wells in the Maokou Formation, criteria for reservoir classification were established based on lithology, average porosity, plane porosity, and thickness of dolomite reservoirs. The effective reservoirs in the Maokou Formation were divided into two types: high-porosity reservoirs and medium-porosity reservoirs. On the basis of establishing a physical quantity model for the Lower Permian carbonate rock in the research area, the rock physical characteristics and seismic response characteristics of reservoirs with different pores are determined through model forward modeling, and appropriate methods are selected for prediction. By using the optimization processing method of gathers and wavelet decomposition to remove sidelobes, the issue of seismic abnormal response caused by lateral velocity changes in the underlying Maokou Formation has been solved. The relative strength relationship of seismic response amplitude can effectively characterize the true situation of reservoir development. Then, by using post-stack inversion and pre-stack simultaneous inversion methods, the high-porosity and medium-porosity dolomite reservoirs in the Maokou Formation are classified and predicted, thereby to effectively improve the prediction accuracy of reservoirs, ultimately providing a reliable basis for the exploration and development of the Maokou Formation in the Hechuan area in the later stage.

A Pre-Stack Inversion Method Based on a Self-Facies-Control Low-Frequency Model and its Application in Offshore Reservoirs

A Pre-Stack Inversion Method Based on a Self-Facies-Control Low-Frequency Model and its Application in Offshore Reservoirs

Three-parameter prestack nonlinear inversion constrained by gradient structure similarity

Estimating the elastic parameters from prestack inversion is of great importance for reservoir characterization. Conventional three-parameter prestack inversion methods rely heavily on well logs, and it is difficult to obtain reliable inversion results in situations with limited numbers of wells. Alternatively, we have developed a joint inversion strategy, integrating the advantages of post- and prestack inversion, to deal with the situation. First, due to the high signal-to-noise ratio of poststack seismic data, the high-precision acoustic impedance (AI) inversion is conducted. Second, the exact Zoeppritz equation is used to establish the objective function of the prestack inversion. To better constrain the elastic parameters (P- and S-wave velocities and density), a new similarity measurement criterion, the gradient structure similarity (GSS), is defined to describe the structural similarity between the prestack inversion results and the inverted AI from the poststack inversion. Third, the LM optimization algorithm is used to solve the nonlinear objective function. Through the model test, we verify the effectiveness of our GSS regularization scheme. Some synthetic and field examples find that our method can provide more stable and accurate inverted results relative to the conventional prestack inversion methods.

The 3-D Global Prestack Seismic Inversion in the Time–Frequency Mixed Domain

Traditional seismic inversion has the problems of low lateral resolution and poor anti-noise performance. To address these challenges, the advantages of inversion methods in time and frequency domains are combined; the Lp norm that can preserve more sparsity information is introduced into seismic inversion. Meanwhile, multi-trace simultaneous inversion has become the mainstream of inversion methods, which can effectively improve the quality of pre-stack seismic inversion. However, traditional multi-trace simultaneous inversion methods have high computational costs and are difficult to obtain the 3D result. Ensuring spatial continuity of inversion results is a challenging task. To address this issue, a 3D global pre-stack inversion method in the time-frequency mixed domain is proposed. The proposed method adds a frequency domain fidelity constraint the objective function of traditional time domain inversion to improve the resolution of the inversion results. To improve the computational efficiency of multi-trace simultaneous inversion, the anisotropic total variation regularization method under Lp norm constraint is employed. In addition, the 2D Sylvester equation is extended to 3D space to enable 3D global inversion. The method not only improves computational efficiency, but also ensures spatial continuity of the results. The stability and feasibility of the proposed method have been demonstrated through its application on both the 3D overthrust model and field pre-stack seismic data.

An Exact Zoeppritz Based Prestack Inversion Using Whale Optimization Particle Filter Algorithm Under Bayesian Framework

Conventional AVO (Amplitude Versus Offset) inversion methods are mainly based on various Zoeppritz approximations. The assumptions of small contrast and linear relationship lead the most inversion methods being difficult to have high inversion accuracy. In this paper, the exact Zoeppritz equation is used to establish the prestack inversion method under the Bayesian framework. It integrates multi-source information to generate posterior distributions of P-, S-wave velocity and density. In the Bayesian theory, the prior model works as the regularization term which has a strong effect on the inversion results. The strategy to obtain a relatively accurate prior model can improve the inversion accuracy. Therefore, an exact Zoeppritz equation based nonlinear AVO inversion algorithm combing whale optimization particle filtering (WOPF) is proposed. The WOPF method can generate a relatively stable and accurate initial model for the Bayesian prestack inversion. We validate the new method through two synthetic models and field data. Comparisons are made with the conventional linear and nonlinear AVO inversion methods. The results show that the proposed method can provide much more accurate inverted elastic parameters in different geological conditions.

AVA inversion for novel fluid indicator considering consolidation parameter

Abstract Geofluid identification from seismic data are crucial for understanding reservoir characteristics. However, fluid indicators based on elastic parameter combinations show strong ambiguity in terms of geofluid identification. Although the effective pore-fluid bulk modulus proves to be the superior fluid indicator in geofluid discrimination, it is limited to empirical models such as the critical porosity model. The consolidation parameter model can evaluate the consolidation and compaction of sediments and is widely used due to its better applicability. Therefore, a novel fluid indicator considering consolidation parameter is proposed and a boundary-constrained inversion strategy for geofluid identification is developed. First, the novel fluid indicator is defined based on the poroelasticity theory. Then, the linearized AVA (amplitude variation with angle) approximation equation related to the new fluid indicator is derived to directly link seismic data and properties of pore-filling materials. The model analysis verifies the accuracy of the derived approximation equation at moderate incident angles, which can be used for parameter prediction by pre-stack seismic inversion. Furthermore, the contribution of each attribute of the novel equation to the reflectivity is analyzed to validate the feasibility of new fluid indicator inversion. Finally, a boundary-constrained AVA pre-stack inversion method is presented to enhance the robustness of the inversion results of the model parameter. The reliability of the improved method is proved by accurate inversion results of synthetic seismic records. After determining the consolidation parameter using well-log data combined with BGT (Biot–Gassmann Theory), the field data further demonstrated that the proposed approach is accurate and effective.

Joint PP-PS seismic prestack inversion of thin-bed reservoirs

Abstract Thin beds are significant targets in seismic exploration of lithological reservoirs. The conventional Amplitude Versus Offset/Angle (AVO/AVA) inversion methods are based on Zoeppritz equations and their approximate formulas under single-interface assumptions, which are not suitable for thin-bed problems. Therefore, AVO/AVA inversion methods for thin-bed reservoirs are needed in high-resolution seismic exploration. Based on approximate reflection coefficients of a thin bed, this paper proposes a joint PP-PS prestack inversion method of thin-bed reservoirs using a least-squares approach. The method directly predicts elastic properties, including P-wave velocities, S-wave velocities and densities, as well as thin-bed thickness. In this study, four typical thin-bed models and an actual thin coal seam were used to prove applicability and superiority of the given inversion procedure. Compared to prestack inversion methods using exact Zoeppritz equations, our inversion method reveals thin-bed existence precisely, gives higher estimation accuracy and has better noise robustness. Future applications of the proposed inversion method are expected to advance seismic reflectivity techniques of thin-bed reservoirs, making it possible to detect potential source reservoirs with higher precision and accuracy.

Applicability Analysis of Pre-Stack Inversion in Carbonate Karst Reservoir

Although pre-stack inversion has been carried out on reservoir prediction, few studies have focused on the application of pre-stack for seismic inversion in fractured-cavity carbonate reservoirs. In carbonate rock, complicated combinations and fluid predictions in karst caves are remain unclear. Post-stack methods are commonly used to predict the position, size, and fillings of caves, but pre-stack inversion is seldom applied in carbonate karst reservoirs. This paper proposes a pre-stack inversion method for forward modeling data and oil survey seismic data, using both points to indicate the application of pre-stack inversion in karst caves. Considering influence of cave size, depth, and filler on prediction, three sets of models (different caves volume; different fillings velocity of caves; complicated combination of caves) are employed and inverted by pre-stack inversion. We analyze the pre-stack results to depict Ordovician oil bearing and characterize caves. Geological model parameters came from actual data of the Tahe oilfield, and seismic data were synthesized from geological models based on full-wave equation forward simulation. Moreover, a case study of pre-stack inversion from the Tahe area was employed. The study shows that, from both the forward modeling and the oil seismic data points of view, pre-stack inversion is applicable to carbonate karst reservoirs.

Cooperative multinetworks semi-supervised pre-stack seismic inversion

SUMMARY The elastic properties of the subsurface, such as density, P-velocity and S-velocity, can be estimated in pre-stack seismic inversion. In recent research, the deep neural network is widely used in pre-stack seismic inversion for its strong non-linear fitting and feature extraction ability. However, the label data is generally inadequate due to high drilling costs and strong data-sharing barriers in the field of exploration geophysics. In order to reduce the dependence of network performance on label data and ensure the accuracy of inversion mostly, semi-supervised learning is adopted. Here, we develop a cooperative multinetworks semi-supervised pre-stack seismic inversion method. In the cooperative multinetworks inversion framework, the inversion network, mapping network, and modification network are adopted to complete the inversion task cooperatively. A forward network is constructed to automatically generate seismic data from density, P-velocity and S-velocity, which can assist the above networks to complete semi-supervised learning. Compared with some published deep learning pre-stack inversion methods, the spatio-temporal correlation of data can be fully mined, the prior geological structure and low-frequency information can be utilized effectively, and reflectivity is adopted as an intermediate output parameter to improve the robustness of the method. The experiments on the Marmousi2 model demonstrate that cooperative multinetworks semi-supervised inversion strategy is superior to conventional semi-supervised inversion methods in both inversion accuracy and antinoise performance. In addition, the susceptibility experiments of the initial model indicate that the proposed method can maintain a high inversion accuracy with little effective information in the initial model. Finally, the proposed method is successfully applied to the field data and obtains a high-resolution inversion result.

Time-lapse difference inversion based on the modified reflectivity method with differentiable hyper-Laplacian blocky constraint

Time-lapse (TL) seismic has great potential in monitoring and interpreting time-varying variations in reservoir fluid properties during hydrocarbon exploitation. Obtaining the disparities of reservoir elastic parameters by inversion is essential for TL reservoir monitoring. Conventional TL inversion is carried out by stages without coupling processing and leads to inaccurate results. We directly use the differences in seismic data responses from different vintages, namely, difference inversion, to improve the results’ credibility. It may reduce the deviations of the subtraction of base and monitor inversions in traditional methods. Moreover, most existing studies involving prestack inversion methods use the Zoeppritz equation or its approximants, which failed to consider the wave-propagation effects. We have developed a new TL difference inversion based on the modified reflectivity method (MRM), in which the internal multiples and transmission losses are taken into consideration to fine-tune the characterization of the seismic wave propagating underground. The new method is modified on the basis of the reflectivity method making it feasible in TL difference inversion, and it is derived from the Bayesian theorem. For further delineating the boundaries between layers, the differentiable hyper-Laplacian blocky constraint (DHLBC) is introduced into the prior information of a Bayesian framework, which heightens the sparseness in the vertical gradients of the inversion results and leads to sharp interlayer boundaries of the difference parameters. Synthetic and field data experiments demonstrate that our TL difference inversion method has clear advantages in accuracy and resolution compared with the Zoeppritz method and MRM without DHLBC.

The Slope-Attribute-Regularized High-Resolution Prestack Seismic Inversion

Prestack seismic inversion can be regarded as an optimization problem, which minimizes the error between the observed and synthetic data under the premise of certain geological/geophysical a priori information constraints. It has been proved to be a powerful approach for reconstructing the subsurface properties and building the elastic parameter models (e.g., P- and S-wave velocity, and density). With respect to the specific expressions of a priori information, the starting model and regularization are expected to be the most widely used and indispensable constraints to reconstruct structural features and subsurface properties. The conventional prestack inversion (trace-by-trace) methods perform well when the geological structure of the target area is not too complex. However, due to the lack of lateral constraint, such trace-independent methods are inevitably limited by their capability of characterization (including accuracy, resolution, and robustness) in the case of geologically complex structures, such as tilted stratum and steep faults. The geological structure-guided constraint, herein referred to as the seismic slope attribute, can be exploited as a lateral constraint integrated into the prestack inversion algorithm. In this work, the seismic slope attribute is introduced to the amplitude variation with offset/angle inversion from two aspects, i.e., starting model building and regularization penalty. Firstly, using the seismic slope attribute, instead of the traditional manual interpreted geological horizons, as a constraint, the well-log data are interpolated to build the initial model. The interpolation algorithm is formulated as solving the inverse problem by using the shaping regularization method rather than the kriging-based algorithm. Secondly, by rotating the coordinate system according to the seismic slope attribute, the directional total variation regularization is used as a constraint to improve the resolution (in both vertical and horizontal directions) and lateral continuity of the inversion results. Finally, the proposed methods are applied to synthetic and real seismic data. Synthetic tests and field data applications demonstrate that the proposed method is capable of revealing complex structural features and achieving stabilized inversion of multi-parameters with less uncertainty.

Bayesian Frequency-Dependent AVO Inversion Using an Improved Markov Chain Monte Carlo Method for Quantitative Gas Saturation Prediction in a Thin Layer

One of the main objectives in the hydrocarbon reservoir characterization is determining rock and fluid properties that rely extensively on inference from seismic observations. In this letter, we present a novel Bayesian prestack inversion method using frequency-dependent amplitude versus offset (AVO) analysis with the goal to directly estimate gas saturation and porosity of a target thin reservoir zone. The proposed methodology is based on an improved Markov chain Monte Carlo (MCMC) sampling algorithm, which is computationally very coefficient due to its satisfactory acceptance probability and the convergence speed of Markov chains. Using a nonlinear rock physics model (RPM), properly calibrated for the investigating area, and a seismic forward operator based on the frequency-domain propagator matrix approach in the Bayesian inversion framework, we then evaluate the full posterior probability distribution of petrophysical parameters conditioned to seismic data and available prior information, using the MCMC algorithm in which we iteratively sample within the petrophysical property space. The proposed inversion approach is validated through applications to a synthetic reservoir model and the real seismic data from gas-bearing reservoirs with strong velocity dispersion.

Data-driven pre-stack AVO inversion method based on fast orthogonal dictionary

Regularization techniques are commonly used to mitigate the ill-posed problems for pre-stack AVO (amplitude variation with offset) inversion. In recent years, a data-driven regularization method has successfully been applied to achieve desirable inversion accuracy. In contrast of assuming a certain distribution of the data, this method uses the dictionary learning algorithm to learn the structural characteristics of elastic parameters and then takes this information as a prior constraint in AVO inversion. However, in this method, KSVD (An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation) algorithm is used, which is of low computational efficiency and involves many parameters. In this paper, we propose a novel data-driven pre-stack inversion approach based on the orthogonal dictionary (ORTD) learning method. Because the atoms are orthogonal, the efficiency of dictionary operation is nearly 100 times higher than that of KSVD redundant dictionary. Instead of regulating the sparsity, atomic size and atomic length for the KSVD method, the proposed method depends on two parameters: threshold parameters and dictionary atomic length. Moreover, the parameter manipulation has little impacts on the operation efficiency for this approach. This greatly improves the applicability of the data-driven inversion method. We use model tests and field data applications to verify the performance of the proposed method.

Joint PP and PS Pre-stack Seismic Inversion for Stratified Models Based on the Propagator Matrix Forward Engine

Pre-stack seismic inversion of the P- and S-wave velocities and bulk density is important in seismic exploration for evaluating lithological units and fluid properties. Generally, this inversion is based on ray-tracing modeling, which introduces errors and requires substantial pre-processing for stratified models due to its oversimplified single-interface assumption. To overcome those problems, we propose a pre-stack inversion method, using wave-equation-based modeling as a forward engine. Most wave-equation-based pre-stack inversions are based on the reflectivity method and adopt nonlinear optimization algorithms, although accurate, but computationally expensive. Hence, we use a fast propagator matrix (PM) method valid for layered media. To improve the stability and accuracy, the PP data inversion is extended to joint PP and PS PM-based inversion (JPMI). A linear inversion scheme is adopted to reduce the computational cost, and the Frechet derivatives are computed accordingly. Moreover, to obtain an optimal model solution, the L-BFGS (Limited-memory Broyden–Fletcher–Goldfarb–Shanno) optimization algorithm and L-curve criterion, an adaptive regularization parameter acquisition method, are implemented. A posterior probability analysis shows that the method has a higher parameter sensitivity than the joint exact Zoeppritz-based inversion and gives better estimations than the single-data inversion. We discuss the effects of dataset weight, internal multi-reflections, time window setting, noise level and initial model by using model tests. Synthetic and real-data examples demonstrate that the algorithm is better than the single PP inversion in terms of stability and accuracy, especially for S-wave velocity and density estimations.

Elastic parameter inversion of a sand body based on the exact reflection coefficient

Prestack seismic inversion is an important technology for oil and gas exploration that can provide accurate lithology and pore fluid information on underground media. Prestack inversion is theoretically based on the Zoeppritz equations, which establish the relationship between the seismic reflection coefficients of different offsets and elastic parameter contrasts between the interface of two lithologies. However, these are fourth-order matrix equations with a complex form and are difficult to be directly applied to inversion. Approximate analytical solutions have been used to establish a linear relationship between the seismic reflection coefficients and elastic parameters. And the approximations are based on the assumptions of a small angle and weak contrast, they cannot be applied to layers of high contrast, such as gas-bearing sandstones and coal seams. In this paper, we present an accurate prestack seismic inversion method that can be applied to a wider range of geological models. First, we derive the partial derivatives of the exact reflection coefficients of PP waves based on the Zoeppritz equations with respect to the elastic parameters and establish the exact Jacobian and Hessian matrices. Then, we use the least-squares algorithm to construct objective functions for the simultaneous inversion of three elastic parameters: The P-wave velocity, S-wave velocity, and density. Examples show that our inversion method is more accurate for strata with strong reflection interfaces than the approximate prestack inversion methods and stable under noisy conditions. The inversion method was applied in channel sand depositions of the Lower Permian Shanxi and Shihezi Formation of the Ordos Basin, and it was efficient to identify the gas-bearing sand layers and determine the range of potentially favorable areas.

Seismic pre-stack AVA inversion scheme based on lithology constraints

AbstractSeismic pre-stack AVA inversion using the Zoeppritz equation and its approximations as a forward engine yields P- and S-wave velocities and density. Due to the presence of seismic noise and other factors, the solution to seismic inversion is generally ill-posed and it is necessary to add constraints to regularize the algorithm. Moreover, since pre-stack inversion is a nonlinear problem, linearized optimization algorithms may fall into false local minima. The simulated annealing (SA) algorithm, on the other hand, is capable of finding the global optimal solution regardless of the initial model. However, when applied to multi-parameter pre-stack inversion, standard SA suffers from instability. Thus, a nonlinear pre-stack inversion method is proposed based on lithology constraints. Specifically, correlations among the elastic parameters are introduced to establish constraints based on a Bayesian framework, with special intention of mitigating the ill-posedness of the inversion problem as well as addressing the lithological characteristics of the formations. In particular, to improve the stability, a multivariate Gaussian distribution of elastic parameters is incorporated into the model updating the SA algorithm. We apply the algorithm to synthetic and field seismic data, indicating that the proposed method has a good resolution and stability performance.

Fluid prediction of a deep carbonate reservoir using walkaround 3D-3C vertical seismic profiling data

AbstractThe considerable depth and complicated structure of the Tahe Oilfield in the Tuofutai area of China make it very difficult to delineate its Ordovician carbonate fracture-cavity reservoir. The resolution of conventional ground seismic data is inadequate to satisfy current exploitation requirements. To further improve the understanding of the carbonate fracture-cavity reservoir of the Tahe Oilfield and to provide predictions of reservoir properties that are more accurate, a walkaround 3D-3C vertical seismic profiling (VSP) survey was conducted. First, we preprocessed raw VSP data and developed a method of joint PP- and PSV-wave prestack time migration. In contrast to ground seismic imaging profiles, VSP imaging profiles have a higher resolution and wider spectrum range that provide more detailed strata information. Then, using the joint PP- and PSV-wave prestack inversion method, we obtained the PP- and PSV-wave impedance and Poisson's ratio parameters of the Ordovician carbonate reservoir. Compared with the P-wave impedance of the ground seismic inversion, we found the VSP inversion results had higher accuracy, which enabled clearer identification of the internal characteristics of the carbonate reservoir. Finally, coupled with the Poisson's ratio attribute, we predicted the distribution of favorable reservoirs and interwell connectivity. The prediction results were verified using both logging and production data. The findings of this study demonstrate the applicability of the proposed technical method for the exploration of deep carbonate fracture-cavity reservoirs.

Application of pre-stack simultaneous inversion to predict gas-bearing dolomite reservoir: a case study from Sichuan Basin, China

Pre-stack simultaneous inversion is a seismic exploration technology which is widely applied to recognize lithology and detect oil and gas by analyzing the feature of amplitude variation versus offset (AVO). A gas-bearing reservoir can cause an obvious change in P-impedance and density. To effectively discriminate gas-bearing dolomite within Longwangmiao Fm. in Sichuan Basin of China, the pre-stack inversion method is employed by strict quality procedures. These procedures mainly include rock physics analysis, AVO modeling and pre-stack simultaneous inversion. Using the technology, P-impedance, S-impedance and density are obtained simultaneously. The distribution of gas-bearing dolomite is identified by P-impedance versus density cross-plotting from well data. The final prediction result indicates the existence of gas-bearing dolomite in the upper part of Longwangmiao Fm. in the study area, which is consistent with well data. Generally, pre-stack simultaneous inversion represents an effective method to predict gas-bearing dolomite reservoir and should be applicable in other study areas.

Simultaneous Inversion of Shallow Seismic Data for Imaging of Sulfurized Carbonates

In this article, we present a high-resolution shallow seismic surveying method for imaging the inner structure of the Miocene evaporitic formation, where sulfur ore occurs. The survey was completed in the northern part of the Carpathian Foredeep (SE Poland) where sulfur deposits occur up to a depth of ca. 260 m. In this region, the sulfur ore is strata-bound and exists within a carbonate interval of a thickness of approximately 28 m. The average sulfur content reaches up to 30%. Five seismic profiles were acquired with a total length of 2450 m. The acquisition was designed to obtain high-resolution, long offsets and a satisfactory signal-to-noise ratio. In the field, we used 48 channels and variable end-on roll-along spread that allowed us to record offsets of up to 375 m. Data processing was aimed at preserving relative amplitudes (known as RAP, relative amplitude preservation processing), an approach that is necessary for seismic inversion application. With the utilization of well log data and results of simultaneous inversion, we were able to calculate the elastic properties of the deposit to evaluate sulfur ore content and changes in lithology. The sulfur content is strongly dependent on the carbonate reservoir’s porosity. To evaluate porosity changes and associated sulfur content, a simultaneous inversion procedure was used. This is a pioneering approach in which we applied pre-stack inversion methods to shallow carbonate sediments.

Inversion Analysis on Promax 3D Software for Determine Variation Amplitude with Offset Lambda MU RHO of High Resolution at West Natuna Arang Islands

A fields, which is called as gas fields, is in the western basin of Natuna. This basin has a main reservoar, such of sandstone reservoar. The thickness reservoar on this fields is very varied, so a result of inversion could only mapping the thickness which is near on tuning thickness. Based on sensitivity test on target area, Acoustic Impedance (AI) parameters on the research wells is insensitive to distinguish a hydrocarbon, so acoustic parameters with only Primer (P) velocity could not be applied in this reservoar characterization fields. But, when the Secondary (S) velocity, λρ and μρ were involved, hydrocarbon distribution could be mapped. This is what then underlies the using of Avo LMR prestack inversion method in Promax 3D software, in conducting reservoar characterization analysis. Velocity S data was needed in AVO LMR inversion method. Secondary (S) velocity data could be generated with using some method, but on this research the method used limited with only castagna method. The result of the research was obtained some conclusion that target area were obtained range value of rho lamda (λρ) 5 − 3 GPA gr/cc and mu rho (μρ) 15,8 − 17 GPA gr/cc. Its shows the existence of sandstone caracterization and hydrocarbon anomaly at Lower Arang Formation.