The X-ray diffraction has revealed that the polycrystalline hexagonal structured alpha-In2Se3 thin films grown at substrate temperature of 200degreesC with the unit cell parameters a=4.03degreesA and c=19.23degreesA b...
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The X-ray diffraction has revealed that the polycrystalline hexagonal structured alpha-In2Se3 thin films grown at substrate temperature of 200degreesC with the unit cell parameters a=4.03degreesA and c=19.23degreesA becomes polycrystalline hexagonal structured InSe with a unit cell parameters of a=4.00degreesA and c=16.63degreesA by Cd-doping. The analysis of the conductivity temperature dependence in the range 300-40 K revealed that the thermionic emission of charged carriers and the variable range hopping are the predominant conduction mechanism above and below 100 K, respectively. Hall measurements revealed that the mobility is limited by the scattering of charged carriers through the grain boundaries above 200 K and 120 K for the undoped and Cd-doped samples, respectively. The photocurrent (I-ph) increases with increasing illumination intensity (T) and decreasing temperature up to a maximum temperature of similar to100 K, below which I-ph is temperature invariant. It is found to have the monomolecular and bimolccular recombination characters at low and high illumination intensities, respectively. The Cd-doping increases the density of trapping states that changes the position of the dark Fermi level leading to the deviation from linearity in the dependence of I-ph on F at low illumination intensities.
Underground strata are reflected in various information sources in petroleum exploration, including good logging and drilling data. Real-time measurement parameters obtained from mud logging can provide data support f...
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Underground strata are reflected in various information sources in petroleum exploration, including good logging and drilling data. Real-time measurement parameters obtained from mud logging can provide data support for the early discovery of oil and gas resources and the prevention of safety accidents. It plays a forward-looking role in the drilling process. In this paper, we aim at the defection of fuzzy and random characteristics of the big data of drilling element parameters in the current drilling process. A new method named grey wolf optimization-support vector machine (GWO-SVM) is proposed by analyzing the relationship between logging data and formation to solve the serious problem of formation misjudgment. Using element content and Gamma-ray value, data mining is performed by a large number of real-time data obtained from the drilling site. The obtained information is used for comprehensive estimation and prediction of strata. First, the data is normalized, and then, the best zeta and sigma values are found through the optimization of gray wolf algorithm, next the SVM training is carried out, finally, the formation prediction model is established, and the error analysis of the results was conducted. In the paper, the algorithm model is subsequently applied to three actual wells. The GWO-SVM model based on drilling data is used to predict the formation, and the error analysis showed that the error range of the GWO-SVM algorithm is within 10%. Compared with the GWO-SVM, the model accuracy of SVM, Particle Swarm Optimization-Support Vector Machine (PSO-SVM) algorithm is lower 53% and 23%, respectively. The GWO-SVM has higher robustness, reliability, and achieves faster convergence speed, stronger generalization effect, and improves the identification accuracy of elements for the formation. The average accuracy of the GWO-SVM in stratum dynamic identification is 93.5%. This model is implemented to support the logging system to improve application strength.
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