Today, geomechanics plays a crucial role in the oil industry, particularly in enhancing production and ensuring well stability. To achieve optimal results, accurate estimation of geomechanical parameters is essential....
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Today, geomechanics plays a crucial role in the oil industry, particularly in enhancing production and ensuring well stability. To achieve optimal results, accurate estimation of geomechanical parameters is essential. One of the low-cost and accurate methods for estimating geomechanical parameters is the use of intelligent methods. In this research, geomechanical parameters are estimated using conventional data logs using intelligent methods. The aim of this study is to introduce a new machine learning algorithm to estimate geomechanical parameters using conventional data logs in one of the hydrocarbon field wells in southwest Iran. In this article, the shear wave velocity and uniaxial compressive strength (UCS) were estimated using machine learning algorithms. Subsequently, other geomechanical parameters were calculated based on these estimated parameters derived from machine learning algorithms. For shear wave velocity (Vs) prediction using MLP and clm (CNN+LSTM+MLP) algorithms, First, effective features were selected using the Auto-encoder deep learning algorithm. The selected features for Vs input into the algorithms were Vp, RHOB, CALIPER, and NPHI, and then the Vs is estimated with MLP and clm algorithm. To evaluate the results, the model was assessed using metrics such as MAE, MAPE, MSE, RMSE, NRMSE, and R2 on the train, test, and blind datasets. The clm algorithm consistently demonstrated superior performance across all datasets, including training, testing, and blind data sets. The R2 values for blind data were $R_{MLP}<^>2 = 0.8727,$RMLP2=0.8727, $R_{clm}<^>2 = 0.9274$Rclm2=0.9274, respectively. These outputs are crucial for estimating subsequent studies. Next, Elastic Young's moduli and Poisson's ratio were calculated, and the dynamic brittleness index was computed using dynamic Young's modulus and Poisson's ratio. Subsequently, UCS values were predicted using machine learning algorithms. Since there were 12 laboratory core samples of UCS available, UCS w
According to previous researches, Persian consonants have been divided into seven categories based on viseme. It led to several consonants being placed in one category. Detecting between consonants in one category is ...
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ISBN:
(纸本)9781665433655
According to previous researches, Persian consonants have been divided into seven categories based on viseme. It led to several consonants being placed in one category. Detecting between consonants in one category is so hard because the spots for the production of these consonants are the same. The forms of lips do not change at the time of production;these consonants are hardly distinguishable. The major challenge is to recognize the differences between lip shapes in one category. The purpose of this study is to recognize differences between bilabial consonants such as /p/, /b/, and /m/ in a word that composed of consonant/vowel called CV by computer vision. For the first time, this study attempts to distinguish these consonants. Proper pronunciations of words are required to identify consonants. Therefore, a database has formed based on the videos of the speech therapists. Generally, this kind of process is including 1-lip detection, 2-lip feature extraction, and 3-classification systems for the diagnosis of consonants. In this paper, consonants recognition in a category based on lip shape using the clm algorithm for lip detection is presented. Geometric algorithms for feature extraction and DTW and equalizer as a classification system are proposed. Although this study is open because we could identify differences among consonants in just one class, we could reach remarkable CV video results for the first time. We could aim for acceptable results with reasonable accuracy for bilabial consonants detection. The principle purpose of this study is to improve lip-reading systems in security issues and help hearing-impaired people in interaction with their surroundings. The results of this paper can have a positive effect on speech systems.
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