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The black rectangle shows the area of the Viking Graben oil field. This Viking Graben map was modified from Brown (1990) and Glennie and Underhill (1998).
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In geophysics, more specifically in rock physics and petrophysics, empirical equations play an important role in data regularization, especially in datasets that are difficult to acquire in situ. In terms of well logging, a common scenario is the absence of shear wave slowness data, which can be handled by different methods that aim filling data ga...
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... from the North Sea region. The first was from the Norne Field and the second from the Viking Graben field. These two fields are located at the Norwegian sea. Related to the Norne field, Fig. 3 shows a map illustration of the Norne location. This picture is modified from Gjerstad et al. (1995), which has a geological description of this oil field. Fig. 2 shows that the Viking Graben is a north-south-trending linear trough straddling the boundary between the Norwegian and UK sectors of the northern North Sea. This Figure is modified from Brown (1990) and Glennie and Underhill (1998). In these two last literatures, it can be found a detailed description about the geologic features of the ...
Context 2
... from the North Sea region. The first was from the Norne Field and the second from the Viking Graben field. These two fields are located at the Norwegian sea. Related to the Norne field, Fig. 3 shows a map illustration of the Norne location. This picture is modified from Gjerstad et al. (1995), which has a geological description of this oil field. Fig. 2 shows that the Viking Graben is a north-south-trending linear trough straddling the boundary between the Norwegian and UK sectors of the northern North Sea. This Figure is modified from Brown (1990) and Glennie and Underhill (1998). In these two last literatures, it can be found a detailed description about the geologic features of the ...
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... Petrophysical modeling allows for a reasonable petrophysical model to be established through related analyses, and then, the model is used to obtain the relevant rock parameters, such as the elastic modulus and density. Conversion equations of P wave and shear-wave velocities are obtained based on these parameters and are used to complete the shear-wave prediction (Keys and Xu 2002, Lee 2006, Sousa et al. 2019. Recently, with the development of machine learning technologies, some new methods of predicting shear-wave velocities have emerged, such as those based on support vector regression (Bagheripour et al. 2015) and the Elman artificial neural network (Mehrgini et al. 2017). ...
Increasing mining depths and intensities have resulted in frequent accidents during roadway excavation, thus increasing the necessity of preemptively determining the geological conditions of rock formations along roadways. Identifying the spatial distributions of lithotypes and the mechanical properties of non-marker bed intervals in coal measure strata are crucial for roadway horizon design, roof management, and efficient excavation. Therefore, based on petrophysical tests, well logging, and seismic data, a comprehensive method is proposed, which uses post-stack wave impedance inversion, petrophysical statistical analyses, and pre-stack simultaneous inversion to predict the lithology and elastic parameters of rocks surrounding roadways. The lithology of the target layer is first divided according to the wave impedances of the inversion body, and the sand lenses within the mudstone interval are described. Based on petrophysical test data, the shear-wave conversion equations of the sand lenses in this target interval and background mudstone are then obtained via marginal limit extrapolation. Finally, pre-stack simultaneous inversion, based on the seismic data and shear-wave conversion curves of different lithologies, is performed to obtain the elastic parameter profile of the target interval in the cross-section of the roadway, laying the foundation for further prediction of the mechanical properties of the surrounding rocks. The results agree with roadway drilling data by > 90%.
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