An intelligent system to detect drilling problems through drilled cuttings return analysis

Cuttings return analysis is an important tool to detect and prevent problems during the petroleum well drilling process. Several measurements and tools have been developed for drilling problems detection, including mud logging, PWD and downhole torque information. Cuttings flow meters were developed in the past to provide information regarding cuttings return at the shale shakers. Their use, however, significantly impact the operation including rig space issues, interferences in geological analysis besides, additional personel required. This article proposes a non intrusive system to analyze the cuttings concentration at the shale shakers, which can indicate problems during drilling process, such as landslide, the collapse of the well borehole walls. Cuttings images are acquired by a high definition camera installed above the shakers and sent to a computer coupled with a data analysis system which aims the quantification and closure of a cuttings material balance in the well surface system domain. No additional people at the rigsite are required to operate the system. Modern Artificial intelligence techniques are used for pattern recognition and data analysis. Techniques include the Optimum-Path Forest (OPF), Artificial Neural Network using Multilayer Perceptrons (ANN-MLP), Support Vector Machines (SVM) and a Bayesian Classifier (BC). Field test results conducted on offshore floating vessels are presented. Results show the robustness of the proposed system, which can be also integrated with other data to improve the efficiency of drilling problems detection. Copyright 2010, IADC/SPE Drilling Conference and Exhibition.

Fast petroleum well drilling monitoring through optimum-path forest

Automatic inspection of petroleum well drilling has became paramount in the last years, mainly because of the crucial importance of saving time and operations during the drilling process in order to avoid some problems, such as the collapse of the well borehole walls. In this paper, we extended another work by proposing a fast petroleum well drilling monitoring through a modified version of the Optimum-Path Forest classifier. Given that the cutting's volume at the vibrating shale shaker can provide several information about drilling, we used computer vision techniques to extract texture informations from cutting images acquired by a digital camera. A collection of supervised classifiers were applied in order to allow comparisons about their accuracy and effciency. We used the Optimum-Path Forest (OPF), EOPF (Efficient OPF), Artificial Neural Network using Multilayer Perceptrons (ANN-MLP) Support Vector Machines (SVM), and a Bayesian Classifier (BC) to assess the robustness of our proposed schema for petroleum well drilling monitoring through cutting image analysis.

Redes neurais aplicadas em processos de usinagem da madeira

Pós-graduação em Engenharia Mecânica - FEG

EEG signal classification for epilepsy diagnosis via optimum path forest - A systematic assessment

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Pruning methods to MLP neural networks considering proportional apparent error rate for classification problems with unbalanced data

This article deals with classification problems involving unequal probabilities in each class and discusses metrics to systems that use multilayer perceptrons neural networks (MLP) for the task of classifying new patterns. In addition we propose three new pruning methods that were compared to other seven existing methods in the literature for MLP networks. All pruning algorithms presented in this paper have been modified by the authors to do pruning of neurons, in order to produce fully connected MLP networks but being small in its intermediary layer. Experiments were carried out involving the E. coli unbalanced classification problem and ten pruning methods. The proposed methods had obtained good results, actually, better results than another pruning methods previously defined at the MLP neural network area. (C) 2014 Elsevier Ltd. All rights reserved.

Social-spider optimization-based artificial neural networks training and its applications for Parkinson's disease identification

Evolutionary algorithms have been widely used for Artificial Neural Networks (ANN) training, being the idea to update the neurons' weights using social dynamics of living organisms in order to decrease the classification error. In this paper, we have introduced Social-Spider Optimization to improve the training phase of ANN with Multilayer perceptrons, and we validated the proposed approach in the context of Parkinson's Disease recognition. The experimental section has been carried out against with five other well-known meta-heuristics techniques, and it has shown SSO can be a suitable approach for ANN-MLP training step.

Preliminary diagnosis of ophtalmological diseases through machine learning techniques

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Effects of gamma-irradiation on caprolactam level from multilayer PA-6 films for food packaging: Development and validation of a gas chromatographic method

A gas chromatographic method to determine caprolactam in multilayer PA-6 films used for meat foodstuffs and cheese was developed and validated. A wide linear range (0.8-400 mu g/ml), RSD <= 4.1% and recovery higher than 90.0% were obtained for the chromatographic system, while precision and accuracy of the method showed RSD <= 3.8%, recovery from 95.5-100.0% and LOQ of 32 mu g/g. Irradiated (3, 7 and 12 kGy) and non-irradiated commercial films were analyzed. Most of them increased caprolactam levels with the increase of irradiation doses. (C) 2008 Elsevier Ltd. All rights reserved.

EFFECT of GAMMA IRRADIATION on CAPROLACTAM MIGRATION FROM MULTILAYER POLYAMIDE 6 FILMS INTO WATER FOOD SIMULANT and VALIDATION of THE METHOD

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Identification and migration of degradation compounds from irradiation of multilayer polyamide 6 films for meat foodstuffs and cheese

The aim of this work was to identify the degradation compounds produced during irradiation of multilayer polyamide 6 (PA-6) films and to study their migration into water and 95% ethanol food simulant. After irradiation of multilayer PA-6 films at 3, 7 and 12 kGy, degradation compounds were extracted using solid-phase microextraction, for which the time and temperature of extraction and stirring were optimized, and identified by gas chromatography-mass spectrometry. Caprolactam, 2-cyclopentylcyclopentanone and aldehydes, among other compounds, were identified in the headspace of the films. Polydimethylsiloxane was considered the best fiber for extraction. The optimum conditions of time, temperature and stirring to extract the compounds were 20 min, 80 degrees C and 225 rpm. For validation purposes, the compounds were quantified in water and 95% ethanol and the results showed high sensitivity, good precision and accuracy. Migration of compounds from irradiated and non-irradiated multilayer PA-6 films into water and 95% ethanol food simulants was carried out at 40 degrees C for 10 days. The method was efficient for the quantification of decaldehyde, 2-cyclopentylcyclopentanone and caprolactam that migrated from multilayer PA-6 films into food simulants.

Modeling of organic light-emitting diodes with graded concentration in the emissive multilayer

We model the electrical behavior of organic light-emitting diodes whose emissive multilayer is formed by blends of an electron transporting material, tris-(8-hydroxyquinoline) aluminum (Alq(3)) and a hole transporting material, N,N-'-diphenyl-N,N-'-bis(1,1(')-biphenyl)-4,4-diamine. The multilayer is composed of layers of different concentration. The Alq(3) concentration gradually decreases from the cathode to the anode. We demonstrate that these graded devices have higher efficiency and operate at lower applied voltages than devices whose emissive layer is made of nominally homogeneous blends. Our results show an important advantage of graded devices, namely, the low values of the recombination rate distribution near the cathode and the anode, so that electrode quenching is expected to be significantly suppressed in these devices. (C) 2004 American Institute of Physics.

Photoluminescence of barium titanate and barium zirconate in multilayer disordered thin films at room temperature

The emission of wide band photoluminescence showed a synergic effect on barium zirconate and barium titanate thin films in alternate multilayer system at room temperature by 488 nm exiting wavelength. The thin films obtained by spin-coating were annealed at 350, 450, and 550 degrees C for 2 h. The X-ray patterns revealed the complete separation among the BaTiO3 and BaZrO3 phases in the adjacent films. Visible and intense photoluminescence was governed by BaZrO3 thin films in the multilayer system. Quantum mechanics calculations were used in order to simulate ordered and disordered thin films structures. The disordered models, which were built by using the displacement of formers and modifier networks, showed a different symmetry in each system, which is in accordance with experimental photoluminescence emission, thus allowing to establish a correlation among the structural and optical properties of these multilayered systems.

Photoluminescent behavior of SrZrO3/SrTiO3 multilayer thin films

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Dielectric properties and microstructure of SrTiO3/BaTiO3 multilayer thin films prepared by a chemical route

Multilayer thin films with perovskite structures were produced by the polymeric precursor method. SrTiO3/BaTiO3 (STO/BTO) multilayers were deposited on Pt(111)/Ti/SiO2/Si(100) substrates by the spin-coating technique and heated in air at 700 degreesC. The microstructure and crystalline phase of the multilayered thin films were examined by field-emission scanning electron microscopy (FE-SEM), transmission electron microscopy (TEM), resolution-high transmission electron microscopy (HRTEM), atomic force microscopy (AFM) and X-ray diffraction. The SrTiO3/BaTiO3 multilayer thin films consisted of grainy structures with an approximate grain size of 60 nm. The multilayered thin films showed a very clear interface between the components. The SrTiO3/BaTiO3 multilayer thin films revealed dielectric constants of approximately 527 and loss tangents of 0.03 at 100 kHz. The dielectric constant calculated for this multilayer film system is the value of the sum of each individual component of the film, i.e. The total value of the sum of each SrTiO3 (STO) and BaTiO3 (BTO) layer. The multilayer SrTiO3/BaTiO3 obtained by the polymeric precursor method, also showed a ferroelectric behavior with a remanent polarization of 2.5 muC/cm(2) and a coercive field of 30 kV/cm. The multilayer films displayed good fatigue characteristics under bipolar stressing after application of 10(10) switching cycles. (C) 2001 Published by Elsevier B.V. B.V. All rights reserved.

Multilayer perceptron neural networks training through charged system search and its Application for non-technical losses detection

The non-technical loss is not a problem with trivial solution or regional character and its minimization represents the guarantee of investments in product quality and maintenance of power systems, introduced by a competitive environment after the period of privatization in the national scene. In this paper, we show how to improve the training phase of a neural network-based classifier using a recently proposed meta-heuristic technique called Charged System Search, which is based on the interactions between electrically charged particles. The experiments were carried out in the context of non-technical loss in power distribution systems in a dataset obtained from a Brazilian electrical power company, and have demonstrated the robustness of the proposed technique against with several others nature-inspired optimization techniques for training neural networks. Thus, it is possible to improve some applications on Smart Grids. © 2013 IEEE.