Mn-Schiff base complex has been covalently attached to the surface of Mobil Composite Material No. 41 (MCM-41) using a 1,4-diisocyanatobutane (DIC-4) as a binder. The prepared heterogeneous catalyst was characterized by X-ray diffraction pattern (XRD), Brunauer-Emmett-Teller (BET), Fourier transform infrared (FT-IR) spectroscopy, X-ray fluorescence analysis (XRF), inductively coupled plasma (ICP) spectrometry and Transmission Electron Microscopy (TEM). The BET results indicated that the surface area and pore size of MCM-41 were decreased after modification. The obtained catalyst was used as a highly efficient, heterogeneous and recyclable catalyst for the oxidation of a wide range of alkenes with hydrogen peroxide (H 2 O 2 ) as a green oxidant.The effect of various parameters such as catalyst and imidazole (co-catalyst) concentration, tempreture, and reaction time on the conversion effecincy and selectivity of the reaction have been studied. The catalyst was used in five consecutive experiment without any loss of activity, confirming the success of the anchoring process and the catalyst stability.
In this paper, we present how precise deep learning algorithms can distinguish loss circulation severities in oil drilling operations. Lost circulation is one of the costliest downhole problem encountered during oil and gas well construction. Applying artificial intelligence can help drilling teams to be forewarned of pending lost circulation events and thereby mitigate their consequences. Data-driven methods are traditionally employed for fluid loss complexity quantification but are not able to achieve reliable predictions for field cases with large quantities of data. This paper attempts to investigate the performance of deep learning (DL) approach in classification the types of fluid loss from a very large field dataset. Three DL classification models are evaluated: Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU) and Long-Short Term Memory (LSTM). Five fluid-loss classes are considered: No Loss, Seepage, Partial, Severe, and Complete Loss. 20 wells drilled into the giant Azadegan oil field (Iran) provide 65,376 data records are used to predict the fluid loss classes. The results obtained, based on multiple statistical performance measures, identify the CNN model as achieving superior performance (98% accuracy) compared to the LSTM and GRU models (94% accuracy). Confusion matrices provide further insight to the prediction accuracies achieved. The three DL models evaluated were all able to classify different types of lost circulation events with reasonable prediction accuracy. Future work is required to evaluate the performance of the DL approach proposed with additional large datasets. The proposed method helps drilling teams deal with lost circulation events efficiently. Article Highlights Three deep learning models classify fluid loss severity in an oil field carbonate reservoir. Deep learning algorithms advance machine learning a large resource dataset with 65,376 data records. Convolution neural network outperformed other deep learning methods.
Multiple machine learning (ML) and deep learning (DL) models are evaluated and their prediction performance compared in classifying five wellbore fluid-loss classes from a 20-well drilling dataset (Azadegan oil field, Iran). That dataset includes 65,376 data records with seventeen drilling variables. The dataset fluid-loss classes are heavily imbalanced (> 95% of data records belong to the less significant loss classes 1 and 2; only 0.05% of the data records belong to the complete-loss class 5). Class imbalance and the lack of high correlations between the drilling variables and fluid-loss classes pose challenges for ML/DL models. Tree-based and data matching ML algorithms outperform DL and regression-based ML algorithms in predicting the fluid-loss classes. Random forest (RF), after training and testing, makes only 35 prediction errors for all data records. Consideration of precision recall and F1-scores and expanded confusion matrices show that the RF model provides the best predictions for fluid-loss classes 1 to 3, but that for class 4 Adaboost (ADA) and class 5 decision tree (DT) outperform RF. This suggests that an ensemble of the fast to execute RF, ADA and DT models may be the best way to practically achieve reliable wellbore fluid-loss predictions. DL models underperform several ML models evaluated and are particularly poor at predicting the least represented classes 4 and 5. The DL models also require much longer execution times than the ML models, making them less attractive for field operations that require prompt information regarding rapid real-time decision responses to pending class-4 and class-5 fluid-loss events.
This paper presents a new large-scale propagation path loss model to design a fifth-generation (5G) wireless communication system for indoor environments. Simulations for the indoor environment, for all polarization at non-line-of-sight (NLOS) and line-of-sight (LOS), which are performed per meter over a distance of 47 m between each of the separated transmitter antenna (TX) and the receiver antenna (RX) positions to compare better the proposed extensive flexible path loss model with previous models.All the simulations are conducted at the Abu-rayhan buildings at the Amirkabir University of Technology. The results demonstrated that the simple presented model with a single parameter denoted ZMS can predict the expansive path loss over distance more accurately. The values of the path loss exponent (PLE) for the LOS scenario are simulated and achieved at 3.63, 1.81, and 3.42 for the V-H, V-V, and V-Omni antenna polarizations, and for NLOS is 6.11, 4.21, and 5.23 at the 28 GHz frequency for all the polarization antenna type V-H, V-V, and V-Omni, appropriately.
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