Ali Dadkhah Tehrani; Reza Shirinabadi
Abstract
The soil's physical and mechanical properties are obtained through laboratory or in-situ tests. The dilatometer is an in-situ tool in rock mechanics and geotechnical engineering, and is widely used in developed countries. In the advanced version of this device, a geophone receives ground vibration. Thus ...
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The soil's physical and mechanical properties are obtained through laboratory or in-situ tests. The dilatometer is an in-situ tool in rock mechanics and geotechnical engineering, and is widely used in developed countries. In the advanced version of this device, a geophone receives ground vibration. Thus Vs [1] could be obtained at the depth of the blade. This research work investigates the feasibility and performance of the first electronic seismic sensor due to its lower cost, more life span, more sensitivity instead of the geophone, and the ability to transfer signal. These changes make it an online tool connected to Arduino[2], a platform so the digital or analog result could be transferred automatically. The test is carried out under construction of Bahar Shiraz station of Tehran Metro Line 6 at the depth of 30 m. The hammer generates a shear wave, and after amplification, the received signals are measured with the software. The shear wave velocity at the test site is obtained at 504 m/s. The result compared to Vs reported geotechnical investigation done by “Darya-Khak-Pey consulting engineers” for Metro line 6 shows a 10% deviation. It is suggested to conduct more comparative tests to check the results and calibrate. Using an 801-S sensor with more life span (of more than 60 million times) and the ability to connect to the internet with an Arduino board is the innovation applied to introduce a new generation of this tool in the engineering world.
Y. Asgari Nezhad; A. Moradzadeh
Abstract
One of the most essential factors involved in unconventional gas reserves for drilling and production is a suitable quality facies determination. The direct core and geochemical analyses are the most common methods used for studying this quality. Due to the lack of this data and the high cost, the researchers ...
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One of the most essential factors involved in unconventional gas reserves for drilling and production is a suitable quality facies determination. The direct core and geochemical analyses are the most common methods used for studying this quality. Due to the lack of this data and the high cost, the researchers have recently resorted to the indirect methods that use the common data of the reservoir (including petro-physical logs and seismic data). One of the major problems in using these methods is that the complexities of these reproducible repositories cannot be accurately modeled. In this work, the quality of facies in shale gas is zoned using the deep learning technique. The applied method is long short-term memory (LSTM) neural network. In this scheme, the features required for zoning are automatically extracted and used to model the reservoir complexities properly. The results of this work show that zoning is done with an appropriate accuracy (86%) using the LSTM neural network, while it is 78% for a conventional intelligent MLP network. This specifies the superior accuracy of the deep learning method.
H. Sabeti; A. Moradzadeh; F. Doulati Ardejani; A. Soares
Abstract
Stochastic seismic inversion is a family of inversion algorithms in which the inverse solution was carried out using geostatistical simulation. In this work, a new 3D stochastic seismic inversion was developed in the MATLAB programming software. The proposed inversion algorithm is an iterative procedure ...
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Stochastic seismic inversion is a family of inversion algorithms in which the inverse solution was carried out using geostatistical simulation. In this work, a new 3D stochastic seismic inversion was developed in the MATLAB programming software. The proposed inversion algorithm is an iterative procedure that uses the principle of cross-over genetic algorithms as the global optimization technique. The model perturbation towards the objective function is performed recurring to direct sequential simulation and co-simulation. This new algorithm was applied to a synthetic dataset with and without noise. The results obtained for the inverted impedance were satisfactory in both cases. In addition, a real dataset was chosen to be applied by the algorithm. Good results were achieved regarding the real dataset. For the purpose of validation, blind well tests were done for both the synthetic and real datasets. The results obtained showed that the algorithm was able to produce inverted impedance that fairly matched the well logs. Furthermore, an uncertainty analysis was performed for both the synthetic and real datasets. The results obtained indicate that the variance of acoustic impedance is increased in areas far from the well location.
