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Collaborating Authors
Xinjiang Uyghur Autonomous Region
Pore pressure prediction using S-wave velocity based on rock-physics modeling
Cheng, Shuailong (China University of Petroleum (East China), Laoshan Laboratory, Shandong Provincial Key Laboratory of Deep Oil and Gas) | Zong, Zhaoyun (China University of Petroleum (East China), Laoshan Laboratory, Shandong Provincial Key Laboratory of Deep Oil and Gas) | Chen, Yu (The University of Texas at Dallas) | Yang, Yaming (China University of Petroleum (East China), Laoshan Laboratory, Shandong Provincial Key Laboratory of Deep Oil and Gas)
Abstract Pore pressure plays a critical role in improving drilling safety and exploring hydrocarbons. It is well known that the prediction of pore pressure is mainly based on P-wave velocity or acoustic transit time. However, due to the influence of various factors on P-wave velocity, it may not be sufficiently sensitive to the perturbations of effective stress, which results in inaccurate pore pressure prediction results. To solve this issue, we perform a specialized analysis of rock-physics data and find that S-wave velocity is more sensitive to the perturbations of effective stress than P-wave velocity. Therefore, in this study, we develop a new pore pressure prediction method based on S waves to predict pore pressure more accurately. To obtain the normal compaction trend (NCT) required by our method, an anisotropic rock-physics model of mudstone is first constructed, and normal compaction porosity is added to the rock-physics model. The difference between the obtained NCT and the measured S-wave velocity is then used for predicting pore pressure through our method. In practical data application, the pore pressure predicted by our method is highly consistent with the measured pore pressure points, which proves the advantages of S-wave velocity in predicting pore pressure.
- Asia > China (0.47)
- North America > United States (0.46)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock (0.37)
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling > Seismic Inversion (0.68)
- Asia > China > Xinjiang Uyghur Autonomous Region > Junggar Basin (0.99)
- North America > United States > Texas > East Gulf Coast Tertiary Basin > Newton Field (0.93)
Interpreting coal component content in logging data by combining gray relational analysis and hybrid neural network
Bai, Ze (Anhui University of Science & Technology, Hefei Comprehensive National Science Center) | Liu, Qinjie (Hefei Comprehensive National Science Center) | Tan, Maojin (School of Geophysics and Information Technology of China University of Geosciences) | Bai, Yang (School of Geophysics and Information Technology of China University of Geosciences) | Wu, Haibo (Anhui University of Science & Technology, Hefei Comprehensive National Science Center)
Abstract The coal component content is an important parameter during the coal resources exploration and exploitation. Previous logging curve regression and single neural network methods have the disadvantages of low accuracy and weak generalization ability in calculating coal component content. In this study, a gray relational analysis-hybrid neural network (GRA-HNN) method is developed by combining GRA and HNN to predict coal component content in logging data. First, the correlation degree between different conventional logging data and coal components is calculated using the GRA method, and logging curves with a correlation degree of ≥0.7 are selected as the input training data set. Then, a back propagation neural network, support vector machine neural network, and radial basis function neural network of different coal components are constructed based on the selected optimal input logging data, and the weighted average strategy is used to form an HNN prediction model. Finally, the GRA-HNN method is used to predict the coal component content of coalbed methane production wells in the Panji mining area. The application results indicate that the coal component content predicted by the GRA-HNN method has the highest accuracy compared with the logging curve regression method and its single neural network model, with a maximum average relative error of 13.4%. In addition, the accuracy of coal component content predicted by some single intelligent models is not always higher than the logging curve regression method, indicating that the neural network model is not necessarily suitable for all coal component content predictions. Our GRA-HNN method not only optimizes the prediction performance of a single neural network model by selecting effective input parameters but also comprehensively considers the prediction effect of several neural network models, which strengthens the generalization ability of neural network model and increases the log interpretation accuracy of coal component content.
- Oceania > Australia > Queensland > Central Highlands > Bowen Basin (0.99)
- Asia > India > Jharkhand > Bokaro Field (0.99)
- Asia > China > Xinjiang Uyghur Autonomous Region > Junggar Basin (0.99)
- (5 more...)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning > Regression (0.69)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning > Support Vector Machines (0.69)
An efficient deep learning method for VSP wavefield separation: A DAS-VSP case
Li, Xiaobin (Chengdu University of Technology, Chengdu University of Technology) | Qi, Qiaomu (Chengdu University of Technology, Chengdu University of Technology) | Huang, He (Tongji University) | Yang, Yuyong (Chengdu University of Technology, Chengdu University of Technology) | Duan, Pengfei (BGP Inc.) | Cao, Zhonglin (BGP Inc.)
ABSTRACT The quality of wavefield separation on vertical seismic profiling (VSP) data directly affects subsequent imaging and inversion processes. However, the traditional methods have various defects in separating upgoing and downgoing waves. The application of deep learning brings another train of thought to solve related problems. The traditional methods, such as f-k filtering and Radon transform, produce results with spatial aliasing and inaccurate amplitudes. The median filtering method relies on accurate first-break picking and waveform consistency. Moreover, the results of traditional methods are subject to manual intervention. To overcome these problems, a deep-learning method is developed for the automatic wavefield separation of VSP data. First, the traditional Radon transform method is used to produce training data sets. Then, to ensure amplitude preservation and suppress spatial aliasing, new input data is formed by recombining the upgoing and downgoing waves extracted by Radon transform. The developed deep-learning network is highly efficient, and its accuracy exceeds that of the traditional methods only after one epoch training. A practical workflow of wavefield separation based on deep learning is established, and it is applied to synthetic data and field distributed acoustic sensing VSP data. The results indicate that our method is superior in terms of amplitude preservation and spatial aliasing suppression. The time consumption of our method is very acceptable and can be further minimized by training the network using downsampling data.
- Asia > China (0.68)
- North America > United States (0.67)
ABSTRACT Distributed acoustic sensing (DAS) is a new technology for acquiring seismic data with high spatial resolution at low cost. Furthermore, in real downhole seismic exploration, DAS can receive some weak signals reflected from deep and thin layers. Unfortunately, some real downhole seismic data received by DAS often are characterized by low quality. Specifically, in real DAS records, desired signals with weak energy often are contaminated by some new noise not presented in seismic data received by conventional electronic geophones. Due to the characteristics of seismic data, such as frequency band aliasing, low signal-to-noise ratio, and complex noise wavefield, existing linear or nonlinear denoising methods based on information processing theories cannot effectively eliminate this complex and multitype noise. Recently, the deep-learning method has been regarded as a powerful tool for background noise attenuation in seismic data. Most of the existing deep-learning methods are concerned with local features and ignore the global features that can be used to enhance their performance further. To simultaneously extract global and local features, we design a novel complete perception self-attention network (CP-SANet) based on the transformer framework and apply it to the denoising of downhole DAS records. The network embeds a transformer module into multilevel encoder-decoder framework. Depth-wise convolution is applied to enhance the local perception capability. Given the transformer’s requirement for a large amount of data, we specifically design abundant seismic data samples using formation models with different parameters. The noise in the data sets is obtained from actual field DAS data. The effectiveness and feasibility of CP-SANet are verified on synthetic and field DAS records. All of the experimental results prove its satisfactory performance compared with some classical and network methods.
- Research Report > New Finding (0.34)
- Research Report > Experimental Study (0.34)
- Overview > Innovation (0.34)
- Europe > Netherlands > German Basin (0.99)
- Europe > Germany > German Basin (0.99)
- Europe > Denmark > German Basin (0.99)
- Asia > China > Xinjiang Uyghur Autonomous Region > Tarim Basin (0.99)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Near-well and vertical seismic profiles (1.00)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring > Production logging (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Neural networks (1.00)
The controls of strike-slip faults on fracture systems: Insights from 3D seismic data in the central Tarim Basin, northwest China
Liu, Jun (SINOPEC Northwest Oil Field Company) | Gong, Wei (SINOPEC Northwest Oil Field Company) | Wang, Peng (SINOPEC Northwest Oil Field Company) | Yang, Yingjun (Beijing Tianan Ruida Technology Development Co., Ltd) | You, Jun (Beijing Tianan Ruida Technology Development Co., Ltd)
Abstract The central Tarim Basin has gained wide attention for its petroleum reserves, especially the recent commercial discovery of carbonate reservoirs of the Ordovician age in the Shunbei oil and gas field. A systematic analysis is conducted based on 3D seismic interpretation in the central Tarim Basin. The results indicate the presence of several major strike-slip faults and associated fracture systems. The characteristics of major strike-slip faults indicate a lower positive or negative flower structure in the Lower-Middle Ordovician and faults with a normal sense of movement in the Upper Ordovician due to the regional stress field. The major strike-slip faults commonly cause fractures in multiple scales. The fracture systems in different segments of major strike-slip faults contain significant differences in characteristics. In addition, two models are established with regard to strike-slip fault-associated fracture systems in a compressional setting and an extensional setting. The development of fracture systems varies to a large extent in relation to the scales of a strike-slip fractured zone. The fracture systems in a small-scale shear zone or strike-slip fractured zone usually develop along the fracture plane. In contrast, the fracture systems in a large-scale strike-slip fractured zone commonly develop along the fault zone on both sides.
- Geophysics > Seismic Surveying > Surface Seismic Acquisition (1.00)
- Geophysics > Seismic Surveying > Seismic Processing (0.71)
- Asia > China > Xinjiang Uyghur Autonomous Region > Tarim Basin (0.99)
- Asia > China > Bohai Basin (0.99)
Automatic 3D fault detection and characterization — A comparison between seismic attribute methods and deep learning
Brito, Lorenna Sávilla B. (Federal University of Rio Grande do Norte) | Alaei, Behzad (Earth Science Analytics, University of Oslo, University of Oslo) | Torabi, Anita (University of Oslo) | Leopoldino-Oliveira, Karen M. (Federal University of Ceará) | Lino Vasconcelos, David (Federal University of Rio Grande do Norte) | Bezerra, Francisco Hilário Rego (Federal University of Rio Grande do Norte) | Nogueira, Francisco Cezar Costa (Federal University of Campina Grande)
Abstract Seismic interpretation is crucial for identifying faults, fluid concentrations, and flow migration pathways in the oil and gas industry. Algorithms have been developed to identify faults using seismic data and attributes such as changes in amplitude, phase, polarity, and frequency. Despite technological advancements, challenges remain in seismic interpretation due to noise, quality of data, and fault dimensions. Deep learning has recently been applied to image faults from seismic data, making the process faster and more reliable. This paper evaluates the performance of deep neural networks (DNN) in fault interpretation by comparing the results with traditional seismic attributes in onshore seismic data. Our results indicate that the DNN reveals more structural detail, which is essential in characterizing 3D fault geometry. In addition, DNN results show better continuity, fewer false positives, and are less affected by noise in the onshore seismic data used in this case. The 3D fault model from DNN identifies faults and their fault segments with greater variability of strikes and reveals more minor faults. Based on the DNN fault model, we characterized the 3D geometry of a new fault in the Rio do Peixe Basin without noise influence.
- South America > Brazil (1.00)
- North America > United States (1.00)
- Europe > Denmark > North Sea (0.28)
- Geology > Structural Geology > Fault (1.00)
- Geology > Geological Subdiscipline (1.00)
- Geology > Structural Geology > Tectonics (0.93)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock (0.68)
- Geophysics > Seismic Surveying > Surface Seismic Acquisition (1.00)
- Geophysics > Seismic Surveying > Seismic Interpretation (1.00)
- South America > Brazil > Campos Basin (0.99)
- North America > United States > Colorado > Piceance Basin > Rulison Field > Mesaverde Formation (0.99)
- Europe > United Kingdom > North Sea > North Sea Basin (0.99)
- (4 more...)
Abstract Carbon capture and storage (CCS) is a strategy that is used to reduce global greenhouse gas emissions. As a result of increased government incentives and maturing carbon markets, CCS is currently experiencing an unprecedented level of public and commercial interest. In the United States, the Appalachian Basin contains abundant hydrocarbon resources and is the location of numerous industrial facilities, making the region a promising target for CCS development. However, the lack of seismic reflection surveys and well data, along with complex geologic structure throughout much of the basin, has limited the commercial interest in CCS development. This study proposes that the thin-skinned fold-and-thrust belt of the Appalachian Basin may contain geology suitable for secure long-term CO2 storage. This region, known as the Valley and Ridge physiographic province, holds complex fold-and-thrust structures that may effectively trap commercial volumes of CO2. We test this idea by developing a suite of kinematically feasible geologic interpretations for the Catawba syncline Pulaski thrust system in southwest Virginia. We then use these geologic models to conduct numerical simulations for CO2 storage within the fold-and-thrust belt structures of the Catawba syncline. Our simulation results indicate that the geometric configuration of fold-and-thrust belt structures may offer commercially viable CO2 traps for CCS projects within the Appalachian Basin and similar geologic settings worldwide.
- Geology > Structural Geology > Tectonics > Compressional Tectonics > Fold and Thrust Belt (1.00)
- Geology > Structural Geology > Fault > Dip-Slip Fault > Reverse Fault > Thrust Fault (0.49)
- Reservoir Description and Dynamics > Storage Reservoir Engineering > CO2 capture and sequestration (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization (1.00)
- Health, Safety, Environment & Sustainability > Sustainability/Social Responsibility > Sustainable development (1.00)
- Health, Safety, Environment & Sustainability > Environment > Climate change (1.00)
Abstract Since the early 2000s, the exploitation of unconventional reservoirs has become very important to the oil and gas industry because of their high potential source of energy and economic value. Venezuela possesses a world-class hydrocarbon source rock in one of the most prolific hydrocarbon basins in the world, namely the Cretaceous La Luna Formation in the Maracaibo Basin. Outcrop and core samples collected from the northwestern Maracaibo Basin provide the database for this study. A comprehensive multiscale characterization of the samples is undertaken to unravel the stratigraphic properties of the petroleum system. In addition, a geochemical approach is taken to evaluate the prospectivity of the La Luna Formation as an unconventional resource in the Maracaibo Basin. Rock-Eval pyrolysis and biomarker data indicate that the La Luna Formation is dominated by type II kerogen, indicating an oil-prone marine organic matter origin. Total organic carbon values range between 3.85 wt% and 9.10 wt%. Distributions of isoprenoids, steranes, and terpanes including gammacerane and monoaromatic steroid hydrocarbons indicate a hypersaline, marine carbonate anoxic depositional environment. Thermal maturity parameters indicate that most of the cores are currently in the oil window. This combined stratigraphic geochemical study indicates that the La Luna Formation has excellent potential as an unconventional reservoir for oil and gas in the study area.
- Phanerozoic > Cenozoic (1.00)
- Phanerozoic > Mesozoic > Jurassic (0.92)
- Phanerozoic > Mesozoic > Cretaceous > Upper Cretaceous (0.67)
- South America > Venezuela (0.99)
- South America > Colombia > Middle Magdalena Basin > La Luna Shale Formation (0.99)
- South America > Colombia > Aguardiente Formation (0.99)
- (17 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)
- Reservoir Description and Dynamics > Fluid Characterization > Geochemical characterization (1.00)
Quantitative Prediction of Tectonic Fractures in the Fourth Member of the Leikoupo Formation in the Pengzhou Area, Sichuan Basin
Yang, Xu (Southwest Petroleum University, Chengdu) | Bai, Mingsheng (Southwest Petroleum University, Chengdu) | Xie, Qiang (PetroChina Southwest Oil and Gas Field Company) | Li, Gao (Southwest Petroleum University, Chengdu) | Shangguan, Ziran (Southwest Petroleum University, Chengdu)
Abstract This paper aims to highlight the fracture characteristics of the fourth member of the Leikoupo Formation in the Pengzhou area. An anisotropic fracture density model considering the fracture attitude was established to predict tectonic fractures quantitatively. The results indicated that the maximum and minimum principal stresses experienced during the Himalayan Period were mainly concentrated in 120–150, and 85–100 MPa, respectively. The fracture strike can be grouped into NEE, NE, NWW, NW, nearly SN, and nearly EW trending. Three typical fracture development areas were determined: fault zone, anticline core, and others. The high value of vertical linear density was chiefly concentrated in the north and southwest of the study area and around the fault. The high value of the maximum horizontal linear density was primarily concentrated in the center of the study area. In addition, the vertical linear density of the anticline core was relatively low, but the horizontal density was relatively high, resulting in high gas productivity in these areas. Considering that the core area of the anticline included in the A-5 platform is the largest and the corresponding horizontal linear density is high, it is suggested that future wells be made in the A-5 platform.
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Structural Geology > Tectonics > Compressional Tectonics > Fold and Thrust Belt (0.66)
- North America > United States > California > Sacramento Basin > 4 Formation (0.99)
- Asia > China > Xinjiang Uyghur Autonomous Region > Tarim Basin (0.99)
- Asia > China > Sichuan > Sichuan Basin > Southwest Field > Longwangmiao Formation (0.99)
- (7 more...)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Naturally-fractured reservoirs (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Faults and fracture characterization (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (1.00)
Experimental Study and 3D Numerical Analysis of Wellbore Instability in Naturally Fractured Formations
Wei, Yaoran (China University of Petroleum (Beijing)) | Li, Xiaorong (China University of Petroleum (Beijing)) | Tan, Zhenlai (China University of Petroleum (Beijing)) | Yang, Tianyu (China University of Petroleum (Beijing)) | Su, Feiyu (China University of Petroleum (Beijing)) | Feng, Yongcun (China University of Petroleum (Beijing))
Abstract Exploration and development of fractured formations is difficult, because complex conditions such as stuck pipe, wellbore collapse, and even the loss of borehole intervals occur frequently, causing huge economic losses and affecting the later drilling operations. However, most of the existing literature on wellbore stability analysis in fractured formations focuses on the coupled effects of fluid flow and deformation, and the impact of three-dimensional natural fracture characteristics (roughness, dip angle, etc.) on wellbore instability is not yet closely examined, which may yield significant errors and misleading predictions. This paper carried out a series of experiments to analyze the shale sample's fracture characteristics and shear strength. Then, a fully coupled 3D hydro-mechanical model was then constructed using the distinct element method (DEM) to investigate the wellbore instability. Finally, a comprehensive parametric study was performed to analyze the effects of characteristics of fractures (e.g., distribution density, surface roughness and associated shear strength, and dip angle) on wellbore stability under different in-situ stress states. The results show that the increase of the number of parallel weak surfaces results in a larger radial displacement around the wellbore. Fracture roughness poses an important impact on wellbore stability because larger fracture roughness is associated with higher rock shear strength. As the dip angle of the weak plane increases, the maximum wellbore displacement increases and then decreases, and the peak displacement occurs at the dip angle of 45°, which is more obvious in a normal fault stress regime. This research results provide a useful tool to understand and assess the effect of natural fracture characteristics on wellbore instability for drilling design in fractured formations.
- Asia > China (0.69)
- North America > United States (0.47)
- Asia > Middle East > Saudi Arabia (0.28)
- Europe > Norway > Norwegian Sea (0.24)
- Research Report > New Finding (0.84)
- Research Report > Experimental Study (0.70)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (0.93)
- Geology > Structural Geology > Fault > Dip-Slip Fault (0.89)