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Accurate pore pressure knowledge is particularly critical in drilling, such as drilling safety (avoidance of blowouts and well control lost-time incidents). Pore pressure prediction in carbonate reservoir due to the fact that the pore system and fractures are more complex than the clastic reservoir, also the effectiveness of pore pressure on drilling parameters like weight on bit that has direct effects on drilling rate is very important. As well as, the pore pressure and rock strength parameters have a special place in geomechanical studies. In this study, pore pressure was calculated using the porosity and effective stress in the carbonate reservoir. The porosity was calculated with three methods: neutron–density, neutron–sonic and sonic. The rock strength parameters (Bulk and Shear modulus) were calculated using P and S waves obtained from dipole shear sonic imager log. The calculated pore pressure was compared with modular formation dynamics tester (MDT), WOB and rock strength parameters. The result of the study showed that among available methods, predicted pore pressure with sonic porosity has the best matched to MDT data. Moreover, pore pressure has an inverse relationship to WOB and rock strength parameters, so that with increasing pore pressure of both WOB and rock strength parameters were decreasing. The comparison among predicted pore pressure, mud weight and MDT data was showing that the predicted pore pressure has high accuracy and can use it for geomechanical studies and having a safe drilling operation. [ABSTRACT FROM AUTHOR]

This paper presents an improved nature-based algorithm, namely multivariable modified teaching learning based optimization (MM-TLBO) algorithm, as in an iterative process can estimates the best values for the model parameters in a multi-objective problem. The algorithm works in two computational phases: the teacher phase and the learner phase. The major purpose of the MM-TLBO algorithm is to improve the value of the learners and thus, improving the value of the model parameters which leads to the optimal solution. The variables of each learner (model) are the radius (R), depth (h), shape factor (q), density contrast (q) and axis location (x0) parameters. We apply MM-TLBO and TLBO methods for the residual gravity anomalies caused by the buried masses with a simple geometry such as spheres, horizontal and vertical cylinders. The efficiency of these methods are also tested by noise corruption synthetic data, as the acceptable results were obtained. The obtained results indicate the better performance the MM-TLBO algorithm than the TLBO algorithm. We have utilized the MM-TLBO for the interpretation of the six residual gravity anomaly profiles from Iran, USA, Sweden and Senegal. The advantage of the MM-TLBO inversion is that it can estimates the best solutions very fast without falling into local minimum and reaches to a premature convergence. The considered primary population for the synthetic and real gravity data are thirty and fifty models. The results show which this method is able to achieve the optimal responses even if a small population of learners had been considered. [ABSTRACT FROM AUTHOR]

Deformation mechanisms are largely unknown for superelastic carbon nanotube (CNT) aerogels, and this hampers materials design efforts. The CNT network in the cell walls is typically crosslinked or connected by a thermoset polymer phase. In order to create a recyclable superelastic aerogel, unmodified single or multi-walled CNTs were dispersed in water by adding to aqueous carboxymethyl cellulose (CMC) solution. Directional freeze-drying was used to form honeycombs with cell walls of random-in-the-plane CNTs in CMC matrix. Cell wall morphology and porosity were studied and related to CNT type and content, as well as elastic or plastic buckling of the cell walls under deformation. CMC acts as a physical crosslinker for the CNTs in a porous cell wall. Aerogel structure and properties were characterized before and after recycling. The conductivity of the composite aerogel with a density of 10 kg/m 3 , 99% porosity and 50 wt % single-walled CNT exceeds 0.5 S/cm. The potential of these superelastic and conductive aerogels for applications such as mechanoresponsive materials was examined in cyclic conductivity tests at different strains. This opens a new route for recyclable superelastic CNT composite aerogels, avoiding material loss, chemical treatment or addition of other components. [ABSTRACT FROM AUTHOR]

This work aims at understanding the excellent ability of nanocelluloses to disperse carbon nanomaterials (CNs) in aqueous media to form long-term stable colloidal dispersions without the need for chemical functionalization of the CNs or the use of surfactant. These dispersions are useful for composites with high CN content when seeking water-based, efficient, and green pathways for their preparation. To establish a comprehensive understanding of such dispersion mechanism, colloidal characterization of the dispersions has been combined with surface adhesion measurements using colloidal probe atomic force microscopy (AFM) in aqueous media. AFM results based on model surfaces of graphene and nanocellulose further suggest that there is an association between the nanocellulose and the CN. This association is caused by fluctuations of the counterions on the surface of the nanocellulose inducing dipoles in the sp2 carbon lattice surface of the CNs. Furthermore, the charges on the nanocellulose will induce an electrostatic stabilization of the nanocellulose-CN complexes that prevents aggregation. On the basis of this understanding, nanocelluloses with high surface charge density were used to disperse and stabilize carbon nanotubes (CNTs) and reduced graphene oxide particles in water, so that further increases in the dispersion limit of CNTs could be obtained. The dispersion limit reached the value of 75 wt % CNTs and resulted in high electrical conductivity (515 S/cm) and high modulus (14 GPa) of the CNT composite nanopapers. [ABSTRACT FROM AUTHOR]

We develop a new method of using feed-forward back-propagation (FFBP) neural networks to simultaneously estimate shape factor and depth of gravity anomalies. The advantages compared to neural network methods are the following: no pre-assumptions are made on source shape, the FFBP neural network estimates both depth and shape factor of source bodies and, once trained, works well for any new data in the training space, without repeating the initial calculations. The optimum number of neurons in the hidden layer was achieved with a novel multi-start algorithm. The FFBP model after training with suitable data sets and testing with different levels of noisy data is more robust than non-linear least squares minimization methods, especially for data with higher noise levels. The FFBP was tested for two sets of gravity field data over a major container terminal at Freeport, Grand Bahama, and a cavity anomaly at the Medford site, Florida, USA. The estimated parameters of the cavities agree well with the actual values. [ABSTRACT FROM AUTHOR]

The presence of "ill-posed samples" specifically in low-volume datasets leads to accuracy decrement in the learning procedure and the generalization of neural networks. Such samples can be caused by various reasons such as noise contamination, corrupted sensors, or even, the complex distribution of physical properties governing the problem. The peak ground acceleration (PGA) datasets are definitely among the last mentioned. Focusing on speed and accuracy, a method for calculating earthquake magnitude based on the PGA data recorded at a single station along with hypocentral information has been presented in this research. Here, after training a deep neural network, the regression errors of the training data samples are clustered into two groups, namely well and ill posed using the grey wolf optimization algorithm. Instead of being removed, the data samples with low learning rates are then modified using samples selected from the other cluster in a fusional form. Then, two separate models are used and trained independently for the clusters. Next, in addition to the routine procedure of network generalization, every new sample is first checked whether is more likely to belong to which group of the clustered data, and after processing, the corresponding trained model is used. The results of the experiments show that using the proposed method results in magnitude calculation with an error order of less than 0.212 units of moment magnitude with a probability of more than 99.7%, which is superior to the conventional methods some of which were reviewed in this research. [ABSTRACT FROM AUTHOR]

One of the most challenging topics in analyzing multi-dimensional geo-spatial data such as geophysical data-sets is detecting outlier data. The issue mainly originates from the difficulty in describing "normality" or "abnormality" due to the complexity of the relationships between the data elements. Considerable number of methods have been proposed and applied for detecting outliers whether they are assumed to be noise, anomalies within the data-set or simply isolated events. A new outlier detection method reached from automatic training of Local Linear Model Tree (LOLIMOT) network, and based on the data selected by K-Nearest Neighborhood (KNN) search is proposed in this research. The procedure of selecting data pairs is through decile analysis using distances calculated during KNN data grouping. Experiment on a synthetic 12 cluster 3D data-set is indicative of the method's robust performance where calculated Cumulative Error Percentage (CEP) is 13% for the method whereas the nearest value for the KNN is 19%. Also, by applying the method on a micro-gravimetric data and an earthquake catalogue related to the north Zagros- west Alborz, and based on the output of the analyses performed, the superiority of the method in outlier detection was confirmed. [ABSTRACT FROM AUTHOR]

Grouping of earthquakes into distinct clusters is applied to improve mechanism identification and pattern recognition for active seismicity in a region. One of the important issues concerning earthquake data clustering is determining the optimum number of clusters (ONC) at the early stages of algorithms. In this paper a robust method based on K-nearest neighbor search (KNNS) is presented to achieve three goals: improving output accuracy, improving output stability, and adding the ability to weight the features used in ONC determination. By introducing a new formula, the proposed method utilizes the error calculated for clustered data based on the similarity between the members in each cluster. An outlier attenuation algorithm is also used to improve the performance of the method. Both the Krzanowski–Lai Index (KLI) and the silhouette coefficient (SC), as two conventional methods, were used to compare the results and evaluate the performance. Experiments on synthetic data sets verified the effectiveness of the method, with considerable differences found. The clustering of a real earthquake catalogue related to the seismogenic province of Zagros in Persia using our proposed methodology suggests using 13-cluster analysis for clustering based on the spatiotemporal features with the same weights, and seven-cluster analysis for a case where priority is given only to the spatial parameters of the epicenters. Under the same circumstances, the KLI and SC methods suggest three and 18 clusters, respectively. The results of the experiments on synthetic data sets indicate that the proposed method is quantitatively more stable and more accurate than the other two methods. [ABSTRACT FROM AUTHOR]

The mechanical properties of cellulose nanofibrils network structure are essential for their applications in functional materials. In this work, an adhesive peptide consisting of just 11 amino acid residues with a hydrophobic core sequence of FLIVI (F – phenylalanine, L – leucine, I – isoleucine, V – valine) flanked by three lysine (K) residues was adsorbed to 2,2,6,6-Tetramethyl-1-piperidinyloxy radical (TEMPO) oxidized cellulose nanofibrils (TO-CNF). Composite films were prepared by solution casting from water suspensions of TO-CNF adsorbed with the adhesive peptide. The nanofibrils network structure of the composite was characterized by atomic force microscopy (AFM). The structure of the peptide in the composites and the interactions between TO-CNF and the peptide were studied by Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD). The mechanical properties of the composites were characterized by tensile tests and dynamic mechanical analysis (DMA). With 6.3 wt.% adhesive peptide adsorbed onto TO-CNF, the composite showed a modulus of 12.5 ± 1.4 GPa, a tensile strength of 344.5 ± (15.3) MPa, and a strain to failure of 7.8 ± 0.4%, which are 34.4%, 48.8%, and 23.8% higher than those for neat TO-CNF, respectively. This resulted in significantly improved toughness (work to fracture) for the composite, 77% higher than that for the neat TO-CNF. [ABSTRACT FROM AUTHOR]

Investigating the effect of process parameters on material removal rate and surface roughness is very important for process planing in wire electro-discharge machining. In this study, wire electro-discharge machining of cementation alloy steel 1.7131 is experimentally studied, then linear regression model and feedforward backpropagation neural network were established to predict surface roughness and material removal rate for effective machining. The full factorial experiment was chosen for experiments. Experiments were performed under different cutting conditions of pulse current, frequency of pulse, wire speed, and servo speed. The optimized neural network with the best performance for prediction had eight neurons in the hidden layer, capability with 0.773 % overall mean prediction error, while 2.547 % errors was revealed by regression model. Totally, the comparison of the results showed that the neural network is more robust with better accuracy. [ABSTRACT FROM AUTHOR]