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Collaborating Authors
China
Quantitative prediction of the fracture scale based on frequency-dependent S-wave splitting
Yu, Peilin (Chengdu University of Technology) | Yang, Yuyong (Chengdu University of Technology, State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation) | Qi, Qiaomu (Chengdu University of Technology, State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation) | Zhou, Huailai (Chengdu University of Technology, State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation) | Wang, Yuanjun (Chengdu University of Technology, China West Normal University)
ABSTRACT The development of natural fractures has a significant impact on underground reservoirs and leads to seismic anisotropy. Furthermore, the scale of natural fractures directly affects oil and gas preservation, hydraulic fracture construction, and the production development of shale reservoirs. The S-wave anisotropy is a frequency-dependent parameter and the change in S-wave anisotropy with frequency is a function of the fracture scale. We develop an innovative method for predicting the fracture scale quantitatively using frequency-dependent S-wave anisotropy. The quantitative relationship between different fracture scales and the frequency-dependent response of the S-wave splitting (SWS) anisotropy can be obtained using a dynamic rock-physics model. The frequency-dependent S-wave anisotropy is calculated via SWS analysis in the frequency domain, after which this quantitative relationship and the calculated frequency-dependent response are used to establish an objective function for the inversion of the fracture scale at different depths using the least-squares algorithm. We synthesize data under ideal conditions, test our method, apply our method to field data, and find that the quantitative prediction method of the fracture scale yielded reasonable prediction results. The S-wave anisotropy is calculated based on the SWS analysis from the horizontal components of the upgoing wavefields of the field vertical seismic profile. We compare the fracture scale calculated from logging data using our method, and the results obtained indicate that this method can successfully predict the fracture scale quantitatively.
- Geology > Geological Subdiscipline > Geomechanics (0.88)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (0.34)
- North America > United States > California > San Joaquin Basin > Lost Hills Field (0.99)
- Asia > Middle East > Oman > Ad Dhahirah Governorate > Fahud Salt Basin > Natih Field (0.99)
- Asia > China > Xinjiang Uyghur Autonomous Region > Tarim Basin (0.99)
- (2 more...)
Modeling and sparsity-promoting separation of wind turbine noise in common-shot gathers
Hu, Yanglijiang (Xi’an Jiaotong University) | Wang, Xiaokai (Xi’an Jiaotong University) | Hou, Qinlong (Xi’an Jiaotong University) | Liu, Dawei (Xi’an Jiaotong University, Purdue University) | Shang, Xinmin (Sinopec Shengli Oilfield) | Zhang, Meng (Sinopec Shengli Oilfield) | Chen, Wenchao (Xi’an Jiaotong University)
ABSTRACT In land seismic acquisition, the quality of common-shot gathers is severely degraded by wind turbine noise (WTN) when wind turbines are operating continuously in survey areas. The high-amplitude WTN overlaps or even completely submerges the body and surface waves (signals). Through time-space and frequency analysis, three main features of the WTN are observed: (1) it is periodic with nearly constant frequencies over time, (2) it is coherent but exhibits different apparent velocities in space, and (3) it has relatively narrow bands with varying central frequencies. The first feature enables WTN to distort signals from shallow to deep, whereas the latter two features make traditional methods that separate noise and signals based on velocity and frequency differences less effective. To suppress the WTN, we first analyze its formation and propagation mechanism and then develop a WTN simulation model to validate the presented mechanism. Based on our analysis of WTN and signals, we consider common-shot gathers as the linear superpositions of periodic WTN and relatively broadband signals (referred to as low-oscillatory signals). This additive mixture aligns with the feasibility premise of morphological component analysis (MCA). Finally, based on MCA theory, we develop a sparsity-promoting separation method to suppress WTN in common-shot gathers. To implement our separation method, we construct two dictionaries using the tunable Q-factor wavelet transform (TQWT) and the discrete cosine transform (DCT). TQWT and DCT can sparsely represent the oscillating waves (signals) and periodic waves (WTN), respectively. This work contributes to the existing knowledge of WTN separation by modeling the periodicity of WTN and the low-oscillatory behavior of a signal, rather than relying on velocity or frequency differences. Our method is tested on synthetic and field data, and both tests demonstrate its effectiveness in separating WTN and preserving signals.
- Asia > China (0.68)
- North America > United States (0.46)
Deep carbonate reservoir characterization using multiseismic attributes: A comparison of unsupervised machine-learning approaches
Zhao, Luanxiao (Tongji University) | Zhu, Xuanying (Tongji University) | Zhao, Xiangyuan (SINOPEC, Petroleum Exploration and Production Research Institute) | You, Yuchun (SINOPEC, Petroleum Exploration and Production Research Institute) | Xu, Minghui (Tongji University) | Wang, Tengfei (Tongji University) | Geng, Jianhua (Tongji University)
ABSTRACT Seismic reservoir characterization is of great interest for sweet spot identification, reservoir quality assessment, and geologic model building. The sparsity of the labeled samples often limits the application of supervised machine learning (ML) for seismic reservoir characterization. Unsupervised learning methods, in contrast, explore the internal structure of data and extract low-dimensional features of geologic interest from seismic data without the need for labels. We compare various unsupervised learning approaches, including the linear method of principal component analysis (PCA), the manifold learning methods of t-distributed stochastic neighbor embedding and uniform manifold approximation and projection (UMAP), and the convolutional autoencoder (CAE), on the 3D synthetic and field seismic data of a deep carbonate reservoir in southwest China. On the synthetic data, the low-dimensional features extracted by UMAP and CAE provide a better indication of porosity and gas saturation than traditional seismic attributes. In particular, UMAP better preserves the global structure of geologic features and indicates the potential of decoupling the gas saturation and porosity effects from seismic responses. We demonstrate that joint use of several types of seismic attributes, instead of using a single type of seismic attributes, can better delineate the reservoir structures using unsupervised ML. On the field seismic data, UMAP can effectively characterize the sedimentary facies distribution, which is consistent with the geologic understanding. Nevertheless, the porosity and saturation can not be reliably identified from field seismic data using unsupervised ML, which is likely caused by the complex pore structures in carbonates complicating the mapping relationship between seismic responses and reservoir parameters.
- North America > United States > Texas > Yoakum County (0.75)
- North America > United States > Louisiana (0.75)
- Geology > Rock Type > Sedimentary Rock > Carbonate Rock (1.00)
- Geology > Geological Subdiscipline (0.93)
- South America > Brazil > Brazil > South Atlantic Ocean > Santos Basin (0.99)
- North America > Mexico > Veracruz > Veracruz Basin (0.99)
- North America > Mexico > Gulf of Mexico > Veracruz Basin (0.99)
- Asia > China > Sichuan > Sichuan Basin (0.99)
- Information Technology > Artificial Intelligence > Machine Learning > Unsupervised or Indirectly Supervised Learning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (0.88)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (0.46)
Seismic 3D full-horizon tracking based on a knowledge graph to represent the stratigraphic sequence relationship
He, Xin (University of Electronic Science and Technology of China (UESTC)) | Zhou, Cheng (University of Electronic Science and Technology of China (UESTC)) | Zhang, Yusheng (PetroChina Southwest Oil and Gas Field Company) | Qian, Feng (University of Electronic Science and Technology of China (UESTC)) | Hu, Guangmin (University of Electronic Science and Technology of China (UESTC)) | Li, Yalin (BGP Inc. China National Petroleum Corporation)
ABSTRACT Seismic 3D full-horizon tracking is a fundamental and crucial step in sequence analysis and reservoir modeling. Existing automatic full-horizon tracking approaches lack effective methods for representing the stratigraphic sequence relationships in seismic data. However, the inability to represent the stratigraphic sequence relationships fully and accurately makes it challenging to address discontinuous areas affected by faults and unconformities. To address this issue, we develop a knowledge graph representing the stratigraphic sequence relationship, which enables the simultaneous extraction of all horizon surfaces once the stratigraphic distribution of the seismic data is obtained. This method first generates horizon patches and calculates the fault attributes, followed by the construction of an initial knowledge graph that characterizes the overall distribution of horizon patches and faults. The initial knowledge graph comprises nodes and edges. The nodes represent horizon patches, and their attributes cover the geographical location information of the patches and faults. Simultaneously, the edges represent the relationship between the horizon patches, including the stratigraphic sequence relationship, and their attributes illustrate the potential for connecting these patches. Furthermore, we introduce a multilayer knowledge graph based on the point-set topology to fuse the nodes. This allows for the continuous merging of the horizon patches to obtain horizon surfaces across discontinuities with the constraints of fault attributes and stratigraphic sequence relationships in 3D space. Synthetic and field examples demonstrate that our approach can effectively represent stratigraphic sequence relationships and accurately track horizons located in discontinuous areas with faults and unconformities.
- Geophysics > Seismic Surveying > Seismic Interpretation (1.00)
- Geophysics > Seismic Surveying > Seismic Processing (0.68)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Geologic modeling (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)
Quantitative characterization of organic and inorganic pores in shale based on deep learning
Yan, Bohong (China University of Petroleum) | Sun, Langqiu (China University of Petroleum) | Zhao, Jianguo (China University of Petroleum) | Cao, Zixiong (Object Research Systems (ORS) Company) | Li, Mingxuan (China University of Petroleum) | Shiba, K. C. (China University of Petroleum) | Liu, Xinze (Yumen Oil Field Branch of China National Petroleum Corporation (CNPC) Exploration and Development Research Institute) | Li, Chuang (China National Petroleum Corporation (CNPC))
ABSTRACT Organic matter (OM) maturity is closely related to organic pores in shales. Quantitative characterization of organic and inorganic pores in shale is crucial for rock-physics modeling and reservoir porosity and permeability evaluation. Focused ion beam-scanning electron microscopy (FIB-SEM) can capture high-precision three-dimensional (3D) images and directly describe the types, shapes, and spatial distribution of pores in shale gas reservoirs. However, due to the high scanning cost, wide 3D view field, and complex microstructure of FIB-SEM, more efficient segmentation for the FIB-SEM images is required. For this purpose, a multiphase segmentation workflow in conjunction with a U-net is developed to segment pores from the matrix and distinguish organic pores from inorganic pores simultaneously in the entire 3D image stack. The workflow is repeated for FIB-SEM data sets of 17 organic-rich shales with various characteristics. The analysis focuses on improving the efficiency and relevance of the workflow, that is, quantifying the minimum number of training slices while ensuring accuracy and further combining the fractal dimension (FD) and lacunarity to study a simple and objective method of selection. Meanwhile, the computational efficiency, accuracy, and robustness to noise of the 2D U-net model are discussed. The intersection over the union of automatic segmentation can amount to 80%–95% in all data sets with manual labels as ground truth. In addition, calculated by the FIB-SEM multiphase segmentation, the organic porosity is used to quantitatively evaluate the OM decomposition level. Deep-learning-based segmentation shows great potential for characterizing shale pore structures and quantifying OM maturity.
- Asia > China (1.00)
- North America > United States > Texas (0.68)
- North America > United States > Texas > West Gulf Coast Tertiary Basin > Eagle Ford Shale Formation (0.99)
- North America > United States > Texas > Sabinas - Rio Grande Basin > Eagle Ford Shale Formation (0.99)
- North America > United States > Texas > Maverick Basin > Eagle Ford Shale Formation (0.99)
- (7 more...)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Shale gas (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Neural networks (1.00)
Research on focal mechanism of microseismic events and the regional stress during hydraulic fracturing at a shale play site in southwest China
Chen, Xin-Xing (Chengdu University of Technology) | Meng, Xiao-Bo (Chengdu University of Technology) | Chen, Hai-Chao (China University of Petroleum) | Chen, Xin-Yu (Chengdu University of Technology) | Li, Qiu-Yu (Optical Science and Technology (Chengdu) Ltd.) | Guo, Ming-Yu (Chengdu University of Technology)
ABSTRACT We develop a waveform-matching inversion method to determine the focal mechanism of microseismic events recorded by a single-well observation system. Our method uses the crosscorrelation technique to mitigate the influence of anisotropy on the S wave. Then, by conducting a grid search for strike, dip, and rake, we match the observed waveforms of P and S wave with the corresponding theoretical waveforms. A synthetic test demonstrates the robustness and accuracy of our method in resolving the focal mechanism of microseismic events under a single-well observation system. By applying our method to the events that have been categorized into two clusters based on spatial and temporal evolution recorded during the hydraulic fracturing operation in the Weiyuan shale reservoir, we observe that the two clusters have distinct focal mechanism and stress characteristics. The events in the remote cluster (cluster A) exhibit consistent focal mechanisms, with a concentrated distribution of P-axis orientations. The inverted maximum principal stress direction of cluster A aligns with the local maximum principal stress direction (). This implies that events in cluster A occur in a uniform stress condition. In contrast, the other cluster (cluster B) near the injection well exhibits significant variation in focal mechanisms, with a scattered distribution of P-axis orientations. The inverted maximum principal stress direction deviates from local maximum principal stress direction (), indicating that events in cluster B occur in a more complicated stress condition.
- North America > Canada > Alberta (0.47)
- North America > United States > Texas (0.47)
- Asia > China > Sichuan Province (0.29)
- Geology > Geological Subdiscipline > Geomechanics (0.94)
- Geology > Structural Geology > Tectonics > Plate Tectonics > Earthquake (0.70)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (0.68)
- Geology > Petroleum Play Type > Unconventional Play > Shale Play (0.50)
- North America > United States > Texas > Fort Worth Basin > Barnett Shale Formation (0.99)
- North America > Canada > Alberta > Western Canada Sedimentary Basin > Alberta Basin > Deep Basin > West Pembina Field (0.99)
- North America > Canada > Alberta > Western Canada Sedimentary Basin > Alberta Basin > Deep Basin > Pembina Field > Viking Formation (0.99)
- (2 more...)
Physical property characterization of rocks in the Bayan Obo REE-Nb-Fe deposit, China
Zhang, Lili (Chinese Academy of Sciences) | Fan, Hongrui (Chinese Academy of Sciences, University of Chinese Academy of Sciences) | Wang, Jian (Chinese Academy of Sciences) | Zhao, Liang (University of Chinese Academy of Sciences, Chinese Academy of Sciences) | Yang, Kuifeng (Chinese Academy of Sciences, University of Chinese Academy of Sciences) | Xu, Ya (Chinese Academy of Sciences, University of Chinese Academy of Sciences) | Zhao, Yonggang (Baotou Iron and Steel (Group) Co., Ltd) | Xu, Xingwang (Chinese Academy of Sciences, University of Chinese Academy of Sciences) | Hao, Meizhen (Baotou Steel & Kings Mineral Processing Co., Ltd) | Yang, Zhanfeng (The Chinese Society of Rare Earths, State Key Laboratory of Baiyunobo Rare Earth Resource Researches and Comprehensive Utilization) | Li, Xianhua (University of Chinese Academy of Sciences, Chinese Academy of Sciences)
ABSTRACT Bayan Obo ore deposit is the world’s largest rare-earth element (REE) resource, the second largest niobium (Nb) resource, and also a significant iron (Fe) resource in China. Evaluating resource potential for the deposit has become a focus of global interest. Rock-physical properties bridge geophysical exploration and geologic modeling; variation in these parameters is necessary for successful geophysical application. REE, Nb, iron, and potassium are mainly hosted in dolomite and slate of the Bayan Obo Group, and REE mineralization is genetically associated with carbonatite. Three physical properties (resistivity, polarizability, and magnetic susceptibility [MS]) of iron ore, slate, dolomite, and carbonatite dike outcrop samples at Bayan Obo are measured and statistically analyzed using 3D reconstruction, 1D/2D/3D kernel density estimation, scatterplot matrix, 3D histogram, and Pearson- and maximum information coefficient-based correlation analysis. It is evident that iron ore, iron-mineralized fine-grained dolomite, and iron-mineralized slate are mainly of low resistivity, and iron ore and iron-mineralized fine-grained dolomite have high MS. MS favorably distinguishes iron ore from slate; MS and resistivity distinguish between iron-mineralized fine-grained dolomite and carbonatite dikes. The physical properties and whole-rock geochemistry (major and trace elements) jointly demonstrate that MS of iron ore, slate, and dolomite is positively correlated with TFe2O3 content, polarizability is correlated with TFe2O3, SiO2 content is correlated with K2O, and resistivity is correlated with MS and polarizability, respectively. Resistivity of iron ore and dolomite is negatively correlated with TFe2O3 content. Resistivity of iron ore is negatively correlated with TFe2O3, total REE (), and Nb, respectively, and correlated with thorium. The methods used have intuitive visual expression and reflect the characteristics of the physical properties and their correlation with the mineralogical composition. The results will be beneficial for determining the geometry of ore-hosting rock masses and providing crucial evidence for the resource evaluation.
- Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (1.00)
- Data Science & Engineering Analytics > Information Management and Systems (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (0.88)
- Reservoir Description and Dynamics > Reservoir Characterization > Geologic modeling (0.66)
Modeling and numerical simulation of a type of pure-viscoacoustic-wave equation in attenuated transversely isotropic media
Huang, Rong (Southwest Petroleum University) | Wang, Zhiliang (Southwest Petroleum University) | Song, Guojie (Southwest Petroleum University) | Wang, Dan (Southwest Petroleum University) | Zhang, Xinmin (Southwest Petroleum University) | Min, Fan (Southwest Petroleum University)
ABSTRACT The anisotropy and attenuation features of subsurface media significantly affect seismic data processing. Ignoring anisotropy and attenuation in seismic wave propagation may result in inaccurate reflector positions, dimming amplitudes, and reduced spatial resolution in the imaging results. Therefore, accurate seismic wave modeling of anisotropy and attenuation is essential for understanding wave propagation in the earth’s interior. This paper derives three pure-viscoacoustic-wave equations from characterizing the earth’s frequency-independent Q behavior in transversely isotropic (TI) media. First, we develop three time-space domain pure-qP-wave equations in TI media based on different approximation methods, whose coefficients can be determined by different approximation methods and Thomsen’s anisotropic parameters , . Subsequently, we introduce the Kelvin-Voigt attenuation model into our derived three time-space domain pure-qP-wave equations and then obtain three pure-viscoacoustic-wave equations. To further demonstrate the effectiveness and accuracy of our methods, we provide some 2D and 3D numerical tests. The numerical results indicate that the wavefield generated by pure-qP-wave equations and pure-viscoacoustic-wave equations have accurate kinematic characteristics of qP-wave in TI media and attenuated TI media and are free of S-wave artifacts while remaining stable under Thomsen’s anisotropic parameters , so our methods have broader applicability compared with some existing methods. At the same time, the simulation results of pure-viscoacoustic-wave equations also reflect the absorption and attenuation characteristics of qP-waves in attenuated TI media.
- Asia > China (0.28)
- North America > United States > Colorado (0.28)
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling (0.68)
- North America > United States > Colorado > Piceance Basin > Rulison Field > Mesaverde Formation (0.99)
- Asia > China > Sichuan > Sichuan Basin (0.99)
Empirically informed convolutional neural network model for logging curve calibration
Hu, Xinyu (Xi’an Jiaotong University) | Li, Hui (Xi’an Jiaotong University) | Zhang, Hao (Exploration Department of Xinjiang Oilfield Company Karamy) | Wu, Baohai (Xi’an Jiaotong University) | Ma, Li (Shaanxi Provincial Coal Geology Group Co. Ltd.) | Wen, Xiaogang (Shaanxi Coal Field Geophysical Prospecting and Surveying Co., Ltd.,) | Gao, Jinghuai (Xi’an Jiaotong University)
ABSTRACT Environmental calibration of logging curves is critical for petrophysical interpretation and sweet spot characterization. Wellbore failure frequently occurs in clay-rich shale rocks during drilling, leading to biased logging interpretation and uncertainty. To reduce the biased correction or erroneous decision making in the interpreter-dominated logging curve calibration process, we develop an empirically informed convolutional neural network (EiCNN) logging curve correction strategy to calibrate the borehole failure-induced logging curve abnormity more accurately. The EiCNN method, together with high-quality logging curves as labeled samples, provides a nonlinear mapping between input logging curves and calibrations for the distorted curves. The EiCNN method completely alleviates biased correction or decision making by the interpreter-dominated method. It has a strong generalization ability, using many empirically interpreted high-quality data as input samples. The field validation wells demonstrate that the EiCNN model can precisely correct the distorted logging curves of mudstone segments with a correlation coefficient of >0.95. Moreover, the validation and test wells illustrate that the EiCNN method is capable of precisely correcting logging curves of interlayer mudstone, implying that the EiCNN method, to a certain degree, can also accurately perform environmental correction of logging curves from thin mudstone layers.
- Asia > China (0.69)
- North America > United States (0.68)
- Asia > Middle East > Iran (0.28)
- Geophysics > Seismic Surveying (1.00)
- Geophysics > Borehole Geophysics (1.00)
- Geophysics > Time-Lapse Surveying > Time-Lapse Seismic Surveying (0.47)
- North America > United States > West Virginia > Stringtown Field (0.99)
- Asia > Middle East > Iran > Arabian Gulf > Arabian Basin > Arabian Gulf Basin > South Pars Field > Upper Khuff Formation (0.99)
- Asia > Middle East > Iran > Arabian Gulf > Arabian Basin > Arabian Gulf Basin > South Pars Field > Upper Dalan Member (0.99)
- (8 more...)
Simultaneous prediction of the geofluid and permeability of reservoirs in prestack seismic inversion
Yang, Wenqiang (Laoshan Laboratory, China University of Petroleum (East China), Pilot National Laboratory for Marine Science and Technology (Qingdao)) | Zong, Zhaoyun (Laoshan Laboratory, China University of Petroleum (East China), Pilot National Laboratory for Marine Science and Technology (Qingdao)) | Sun, Qianhao (Laoshan Laboratory, China University of Petroleum (East China), Pilot National Laboratory for Marine Science and Technology (Qingdao))
ABSTRACT Geofluid discrimination and permeability prediction are indispensable steps in reservoir evaluation. From the perspective of prestack seismic inversion, predicting fluid indicators is an effective method for obtaining fluid properties directly from seismic data. In contrast, the direct prediction of permeability from observed seismic gathers is constrained by the difficulty in establishing a link between permeability and elastic parameters. However, we show that the pore structure parameters in seismic petrophysical theory are highly related to permeability, providing a new solution for predicting permeability using seismic data. Therefore, the correlation between the shear flexibility factor and permeability is first verified based on logging curves and laboratory data, and the results demonstrate that the shear flexibility factor can be an indicator of reservoir permeability. Second, an approximate reflection coefficient equation is derived for the direct characterization of the shear flexibility factor. In the developed equation, a novel fluid indicator, expressed as the ratio of Russell’s fluid indicator to the square of the shear flexibility factor, is defined for the simultaneous prediction of fluid types and permeability. With the validated response of the novel fluid indicator to geofluid types, we achieve simultaneous predictions of fluid types and reservoir permeability characteristics from prestack seismic data, using a boundary-constrained Bayesian inversion strategy. The model tests and the application of field data from a clastic reservoir confirm the effectiveness and applicability of the method.
- Asia > China > Sichuan Province (0.28)
- North America > United States > Texas (0.28)
- Research Report > New Finding (0.34)
- Research Report > Experimental Study (0.34)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Sandstone (0.69)
- Geophysics > Seismic Surveying > Seismic Processing > Seismic Migration (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling > Seismic Inversion (1.00)
- Oceania > New Zealand > North Island > Taranaki Basin (0.99)
- North America > United States > Texas > Permian Basin > Yeso Formation (0.99)
- North America > United States > Texas > Permian Basin > Yates Formation (0.99)
- (24 more...)