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Results
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)
ABSTRACT Thin and highly conductive objects are challenging to model in 3D direct-current (DC) problems because they often require excessive mesh refinement that leads to a significant increase in computational costs. RESistor network (RESnet) is a novel algorithm that converts any 3D geo-electric simulation to solving an equivalent 3D resistor network circuit. Two features of RESnet make it an attractive choice in the DC modeling of thin and conductive objects. First, in addition to the conductivity with units of Siemens per meter (S/m) defined at the cell centers (cell conductivity), RESnet allows conductive properties defined on mesh faces and edges as face conductivity with units of S and edge conductivity with units of S·m, respectively. Face conductivity is the thickness-integrated conductivity, which preserves the electric effect of sheet-like conductors without an explicit statement in the mesh. Similarly, edge conductivity is the product of the cross-sectional area and the intrinsic conductivity of a line-like conductive object. Modeling thin objects using face and edge conductivity can avoid extremely small mesh grids if the DC problem concerns electric field responses at a much larger scale. Second, once the original simulation is transformed into an equivalent resistor network, certain types of infrastructure, similar to above-ground metallic pipes, can be conveniently modeled by directly connecting the circuit nodes, which cannot interact with each other in conventional modeling programs. Bilingually implemented in MATLAB and Python, the algorithm has been made open source to promote wide use in academia and industry. Three examples are provided to validate its numerical accuracy, demonstrate its capability in modeling steel well casings, and indicate how it can be used to simulate the effect of complex metallic infrastructure on DC resistivity data.
- Information Technology > Artificial Intelligence > Machine Learning (0.93)
- Information Technology > Artificial Intelligence > Representation & Reasoning (0.88)
- Information Technology > Software > Programming Languages (0.67)
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)
- Information Technology > Artificial Intelligence > Machine Learning (0.93)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty (0.36)
- Europe (1.00)
- Asia (1.00)
- North America > United States > Texas (0.67)
- Summary/Review (1.00)
- Instructional Material > Course Syllabus & Notes (1.00)
- Collection > Book (1.00)
- (2 more...)
- Geology > Structural Geology > Tectonics > Plate Tectonics (1.00)
- Geology > Rock Type > Sedimentary Rock (1.00)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- (5 more...)
- Energy > Oil & Gas > Upstream (1.00)
- Education > Educational Setting (1.00)
- Materials > Metals & Mining (0.92)
- Asia > Russia > Ural Federal District > Yamalo-Nenets Autonomous Okrug > Purovsky District > West Siberian Basin > Nadym-Pur-Taz Basin > Block V > Urengoyskoye Field > Achimov Formation (0.99)
- Asia > Russia > Ural Federal District > Yamalo-Nenets Autonomous Okrug > Purovsky District > West Siberian Basin > Nadym-Pur-Taz Basin > Block IV > Urengoyskoye Field > Achimov Formation (0.99)
- Asia > Russia > Ural Federal District > Yamalo-Nenets Autonomous Okrug > Purovsky District > West Siberian Basin > Nadym-Pur-Taz Basin > Block 5A > Urengoyskoye Field > Achimov Formation (0.99)
- (4 more...)
- Information Technology > Data Science (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning (0.92)
- (2 more...)
Bayan Obo ore deposit is the worlds 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 geological modeling; variation in these parameters is necessary for successful geophysical application. REE, Nb, iron, and potassium are mainly hosted in dolomite and slate in 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 three-dimensional reconstruction, one-/two-/three-dimensional kernel density estimation, scatterplot matrix, three-dimensional 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 rare-earth element (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 to determining the geometry of ore-hosting rock masses and providing crucial evidence for the resource evaluation.
- Geology > Rock Type > Sedimentary Rock > Carbonate Rock > Dolomite (1.00)
- Geology > Rock Type > Metamorphic Rock (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Geologic modeling (0.87)
- Information Technology > Artificial Intelligence > Machine Learning (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty (0.36)
Thin and highly conductive objects are challenging to model in 3D direct current (dc) problems since they often require excessive mesh refinement that leads to a significant increase in computational costs. RESnet is a novel algorithm that converts any 3D geo-electric simulation to solving an equivalent 3D resistor network circuit. Two features of RESnet make it an attractive choice in the dc modeling of thin and conductive objects. First, in addition to the conductivity with units of S/m defined at the cell centers (cell conductivity), RESnet allows conductive properties defined on mesh faces and edges as face conductivity with units of S and edge conductivity with units of Sm, respectively. Face conductivity is the thickness-integrated conductivity, which preserves the electric effect of sheet-like conductors without an explicit statement in the mesh. Similarly, edge conductivity is the product of the cross-sectional area and the intrinsic conductivity of a line-like conductive object. Modeling thin objects using face and edge conductivity can avoid extremely small mesh grids if the dc problem concerns electric field responses at a much larger scale. Second, once the original simulation is transformed into an equivalent resistor network, certain types of infrastructure, like above-ground metallic pipes, can be conveniently modeled by directly connecting the circuit nodes, which cannot interact with each other in conventional modeling programs. Bilingually implemented in Matlab and Python, the algorithm has been made open source to promote wide use in academia and industry. Three examples are provided to validate its numerical accuracy, demonstrate its capability in modeling steel well casings, and show how it can be used to simulate the effect of complex metallic infrastructure on dc resistivity data.
- Information Technology > Artificial Intelligence > Machine Learning (0.92)
- Information Technology > Artificial Intelligence > Representation & Reasoning (0.87)
- Information Technology > Software > Programming Languages (0.66)
The Best Scenario for Geostatistical Modeling of Porosity in the Sarvak Reservoir in an Iranian Oil Field, Using Electrofacies, Seismic Facies, and Seismic Attributes
Mehdipour, Vali (Department of Petroleum Engineering, Amirkabir University of Technology) | Rabbani, Ahmad Reza (Department of Petroleum Engineering, Amirkabir University of Technology (Corresponding author)) | Kadkhodaie, Ali (Earth Sciences Department, Faculty of Natural Science, University of Tabriz)
Summary The lateral and vertical variations in porosity significantly impact the reservoir quality and the volumetric calculations in heterogeneous reservoirs. With a case study from Iran’s Zagros Basin Sarvak reservoir in the Dezful Embayment, this paper aims to demonstrate an efficient methodology for distributing porosity. Four facies models (based on electrofacies analysis data and seismic facies) with different geostatistical algorithms were used to examine the effect of different facies types on porosity propagation. Both deterministic and stochastic methods are adopted to check the impact of geostatistical algorithms on porosity modeling in the static model. A total of 40 scenarios were run and validated for porosity distribution through a blind test procedure to check the reliability of the models. The study’s findings revealed high correlation values in the blind test data for all porosity realizations linked to seismic facies, ranging from 0.778 to 0.876. In addition, co-kriging to acoustic impedance (AI), as a secondary variable, increases the correlation coefficient in all related cases. Unlike deterministic algorithms, using stochastic methods reduces the uncertainty and causes the porosity model to have an identical histogram compared with the original data. This study introduced a comprehensive workflow for porosity distribution in the studied carbonate Sarvak reservoir, considering the electrofacies, and seismic facies, and applying different geostatistical algorithms. As a result, based on this workflow, simultaneously linking the porosity distribution to seismic facies, co-kriging to AI, and applying the sequential Gaussian simulation (SGS) algorithm result in the best spatial modeling of porosity.
- Europe (1.00)
- Asia > Middle East > Iran (1.00)
- North America > United States > Texas (0.67)
- Africa > Middle East > Egypt > Nile Delta (0.28)
- Phanerozoic > Mesozoic > Cretaceous > Upper Cretaceous (0.46)
- Phanerozoic > Mesozoic > Cretaceous > Lower Cretaceous (0.46)
- Geology > Geological Subdiscipline (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock (0.93)
- Geology > Sedimentary Geology (0.93)
- Geology > Structural Geology > Tectonics > Compressional Tectonics > Fold and Thrust Belt (0.46)
- South America > Ecuador > Oriente Basin (0.99)
- South America > Ecuador > Napo > Oriente Basin > Napo Formation > Napo T Formation (0.99)
- Oceania > Australia > Western Australia > Perth Basin (0.99)
- (18 more...)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Geologic modeling (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (1.00)
- Information Technology > Data Science > Data Mining (0.93)
- Information Technology > Modeling & Simulation (0.88)
Seismic 3D full-horizon tracking based on 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)
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 effectively, we propose 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. In this method, horizon patches are generated, and the fault attribute is calculated, followed by the construction of an initial knowledge graph that characterizes the overall distribution of both horizon patches and faults. The initial knowledge graph comprises nodes and edges. Here, the nodes represent horizon patches, and their attributes cover the geographical location information of the patches and faults. Simultaneously, edges represent the relationship between horizon patches, including the stratigraphic sequence relationship, and their attributes illustrate the potential of connecting these patches. Furthermore, we developed a multi-layer knowledge graph based on the point set topology to fuse the nodes. This allows for continuous merging of horizon patches to obtain horizon surfaces across discontinuities with the constraints of fault attributes and stratigraphic sequence relationships in 3D space. Both synthetic and field examples demonstrated that the proposed method can effectively represent the stratigraphic sequence relationships and accurately track horizons dislocated in discontinuous areas with faults and unconformities.
- Asia > China (0.93)
- North America > United States > Texas (0.27)
- Geophysics > Seismic Surveying > Seismic Interpretation (1.00)
- Geophysics > Seismic Surveying > Seismic Processing (0.93)
- Oceania > Australia > Western Australia > Carnarvon Basin > Exmouth Basin (0.99)
- Asia > China > Sichuan > Sichuan Basin > Southwest Field > Longwangmiao Formation (0.99)
- North America > United States > Mississippi > Hale Field (0.93)
- 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)
Bayesian geophysical basin modeling with seismic kinematic metrics to quantify uncertainty for pore pressure prediction
Fonseca, Josue (Stanford University) | Pradhan, Anshuman (Stanford University, California Institute of Technology) | Mukerji, Tapan (Stanford University, Stanford University, Stanford University)
ABSTRACT Bayesian geophysical basin modeling (BGBM) methodology is an interdisciplinary workflow that incorporates data, geologic expertise, and physical processes through Bayesian inference in sedimentary basin models. Its application culminates in subsurface models and properties that integrate the geohistory of a basin, rock-physics definitions, well-log and drilling data, and seismic information. Monte Carlo basin modeling realizations are performed by sampling from prior probability distributions on facies parameters and basin boundary conditions. After the data assimilation, the accepted set of posterior subsurface models yields an uncertainty quantification of subsurface properties. This procedure is especially suitable for pore pressure prediction in a predrill stage. However, the high computational cost of seismic data assimilation decreases the practicality of the workflow. Therefore, we introduce and investigate seismic traveltime criteria as computationally faster proxies for analyzing the seismic data likelihood when using BGBM. Our surrogate schemes weigh the prior basin model results with the available seismic data with no need to perform expensive seismic depth-migration procedures for each Monte Carlo realization. Furthermore, we apply BGBM in a real field case from the Gulf of Mexico using a 2D section for pore pressure prediction considering different kinematic criteria. The workflow implementation with the novel seismic data assimilation proxies is compared with the complete computationally expensive benchmark approach, which uses a global analysis of the residual moveout in depth-migrated seismic image samples. Moreover, we validate and compare the outcomes for predicted pore pressure with mudweight data from a blind well. The fast proxy for analyzing the depth positioning of seismic horizons developed in this work yields similar uncertainty quantification results in pore pressure prediction compared with the computationally expensive benchmark. Our fast proxies make the BGBM methodology efficient and practical.
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock (0.93)
- Geophysics > Seismic Surveying > Seismic Processing > Seismic Migration (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling (1.00)
- Geophysics > Seismic Surveying > Seismic Interpretation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Reservoir geomechanics (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Geologic modeling (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty > Bayesian Inference (0.86)
- Information Technology > Artificial Intelligence > Machine Learning > Learning Graphical Models > Directed Networks > Bayesian Learning (0.48)
Understanding the synergistic impact of stress release and cementation on sandstone using sound waves — Implications for exhumation estimation
Yu, Jiaxin (Norwegian University of Science and Technology) | Duffaut, Kenneth (Equinor) | Avseth, Per (Dig Science, Norwegian University of Science and Technology)
ABSTRACT Exhumation is the process that encompasses uplift and erosion, leading to the removal of overburden and the release of effective stress exerted on rocks. When estimating exhumation magnitude using the compaction trend method, it is commonly assumed that the physical properties of rocks are insensitive to stress reduction. However, recent laboratory evidence has indicated that porosity exhibits weaker sensitivity to stress release compared with velocity that can be significantly affected by stress release. This raises questions regarding the validity of irreversible compaction assumed by compaction trend method. It remains unclear whether the impact of stress release can be observed in real rocks in exhumed areas because there is a lack of methods to directly measure the impact of stress release on field data. In addition, studying real rocks is further complicated by the presence of rock diagenesis and its interaction with stress release. To address these knowledge gaps, this study uses stress-dependent burial and uplift modeling and interprets an extensive well-log data set using the modeling-derived evaluation metrics. Conceptual modeling suggests that methods that neglect the combined effect of cementation and stress release tend to underestimate the exhumation magnitude. Furthermore, we discover that the disparity between porosity sensitivity and velocity sensitivity to stress release can be leveraged to derive a metric we call “porosity inconsistency” that can serve as a qualitative and quantitative measure for identifying and evaluating stress release in sandstone using geophysical field measurements. We gather a significant amount of sonic velocity and porosity data from normally compacted and uplifted clean sandstones in the Norwegian Sea and the Barents Sea. Notably, we observe significant porosity inconsistency in the exhumed well 6510/2-1 in the Norwegian Sea. In the Barents Sea, which has experienced extensive Cenozoic exhumation, the well data reveal a varying pattern of porosity inconsistency increasing toward the north and decreasing toward the west. This distribution of porosity inconsistencies in the Barents Sea wells not only aligns with the spatial variation of exhumation reported in various studies but also exhibits a positive correlation with the magnitude of exhumation. Furthermore, the exhumation magnitude derived from velocity-depth trends is considerably lower than the magnitude obtained from porosity/density-depth trends for wells displaying significant porosity inconsistency. These observations provide support for the predictions made by the conceptual modeling. The results of this study enhance our understanding of the synergistic impact of stress release and cementation on sandstone. Moreover, these findings have implications for pore pressure prediction and core evaluation in exhumed areas. They also provide insights into the feasibility and interpretation of time-lapse data of reservoir injection, for which the effective stress is likely to decrease due to pore pressure buildup.
- Europe > Norway > Norwegian Sea (0.68)
- Europe > United Kingdom (0.68)
- Phanerozoic > Cenozoic (1.00)
- Phanerozoic > Mesozoic > Jurassic (0.93)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Sandstone (1.00)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (0.48)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling (0.93)
- Geophysics > Borehole Geophysics (0.67)
- Oceania > Australia > Western Australia > Perth Basin (0.99)
- Oceania > Australia > Victoria > Otway Basin (0.99)
- Oceania > Australia > South Australia > Otway Basin (0.99)
- (12 more...)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Reservoir geomechanics (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Geologic modeling (1.00)
- (3 more...)