Predição em modelos de tempo de falha acelerado com efeito aleatório para avaliação de riscos de falha em poços petrolíferos

We considered prediction techniques based on models of accelerated failure time with random e ects for correlated survival data. Besides the bayesian approach through empirical Bayes estimator, we also discussed about the use of a classical predictor, the Empirical Best Linear Unbiased Predictor (EBLUP). In order to illustrate the use of these predictors, we considered applications on a real data set coming from the oil industry. More speci - cally, the data set involves the mean time between failure of petroleum-well equipments of the Bacia Potiguar. The goal of this study is to predict the risk/probability of failure in order to help a preventive maintenance program. The results show that both methods are suitable to predict future failures, providing good decisions in relation to employment and economy of resources for preventive maintenance.

An Intelligent System for Petroleum Well Drilling Cutting Analysis

Cutting analysis is a important and crucial task task to detect and prevent problems during the petroleum well drilling process. Several studies have been developed for drilling inspection, but none of them takes care about analysing the generated cutting at the vibrating shale shakers. Here we proposed a system to analyse the cutting's concentration at the vibrating shale shakers, which can indicate problems during the petroleum well drilling process, such that the collapse of the well borehole walls. Cutting's images are acquired and sent to the data analysis module, which has as the main goal to extract features and to classify frames according to one of three previously classes of cutting's volume. A collection of supervised classifiers were applied in order to allow comparisons about their accuracy and efficiency. We used the Optimum-Path Forest (OPF), Artificial Neural Network using Multi layer Perceptrons (ANN-MLP), Support Vector Machines (SVM) and a Bayesian Classifier (BC) for this task. The first one outperformed all the remaining classifiers. Recall that we are also the first to introduce the OPF classifier in this field of knowledge. Very good results show the robustness of the proposed system, which can be also integrated with other commonly system (Mud-Logging) in order to improve the last one's efficiency.

Artificial immune systems for classification of petroleum well drilling operations

This paper presents two approaches of Artificial Immune System for Pattern Recognition (CLONALG and Parallel AIRS2) to classify automatically the well drilling operation stages. The classification is carried out through the analysis of some mud-logging parameters. In order to validate the performance of AIS techniques, the results were compared with others classification methods: neural network, support vector machine and lazy learning.

Petroleum well drilling monitoring through cutting image analysis and artificial intelligence techniques

Petroleum well drilling monitoring has become an important tool for detecting and preventing problems during the well drilling process. In this paper, we propose to assist the drilling process by analyzing the cutting images at the vibrating shake shaker, in which different concentrations of cuttings can indicate possible problems, such as the collapse of the well borehole walls. In such a way, we present here an innovative computer vision system composed by a real time cutting volume estimator addressed by support vector regression. As far we know, we are the first to propose the petroleum well drilling monitoring by cutting image analysis. We also applied a collection of supervised classifiers for cutting volume classification. (C) 2010 Elsevier Ltd. All rights reserved.

Case-based reasoning in offshore well design

Petroleum well drilling is an expensive and risky operation. In this context, well design presents itself as a fundamental key to decrease costs and risks involved. Experience acquired by engineers is notably an important factor in good drilling design elaborations. Therefore, the loss of this knowledge may entail additional problems and costs. In this way, this work represents an initiative to model a petroleum well design case-based architecture. Tests with a prototype showed that the system built with this architecture may help in a well design and enable corporate knowledge preservation. (C) 2003 Elsevier B.V. B.V. All rights reserved.

Classification of petroleum well drilling operations using Support Vector Machine (SVM)

During the petroleum well drilling operation many mechanical and hydraulic parameters are monitored by an instrumentation system installed in the rig called a mud-logging system. These sensors, distributed in the rig, monitor different operation parameters such as weight on the hook and drillstring rotation. These measurements are known as mud-logging records and allow the online following of all the drilling process with well monitoring purposes. However, in most of the cases, these data are stored without taking advantage of all their potential. On the other hand, to make use of the mud-logging data, an analysis and interpretationt is required. That is not an easy task because of the large volume of information involved. This paper presents a Support Vector Machine (SVM) used to automatically classify the drilling operation stages through the analysis of some mud-logging parameters. In order to validate the results of SVM technique, it was compared to a classification elaborated by a Petroleum Engineering expert. © 2006 IEEE.

Classification of petroleum well drilling operations with a hybrid particle swarm/ant colony algorithm

This paper describes an investigation of the hybrid PSO/ACO algorithm to classify automatically the well drilling operation stages. The method feasibility is demonstrated by its application to real mud-logging dataset. The results are compared with bio-inspired methods, and rule induction and decision tree algorithms for data mining. © 2009 Springer Berlin Heidelberg.

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.

Failures in the rehabilitation treatment with removable partial dentures

Background. Despite being functional and having aesthetic benefits, the acceptance of patients regarding the use of removable partial dentures (RPDs) has been low. In part, this is due to the deleterious effects that causes discomfort to the patient. Success depends not only on the care expended by the patient, including daily care and oral hygiene, but also on common goals set by their professional and clinical staff, aiming beyond aesthetics, to incorporate issues of functionality and the well-being of patients. Methods and results. For rehabilitation treatment with RPDs to reach the desired level of success without damaging the support structure, all the steps (diagnose, cavity preparation, adaptation of the metal structures, functional of distal extension and posterior follow-up) in the rehabilitative treatment should be carefully developed. A literature review was carried out, searching through MEDLINE (PubMed) articles published between 1965 and December 2012 including clinical trials and reviews about the use of RPDs. Conclusions. This study describes factors that lead to failures and complications in oral rehabilitation through the use of RPDs and suggests possible solutions.

Friction loss correction in flowing well discharge tests

Artesian confined aquifers do not need pumping energy, and water from the aquifer flows naturally at the wellhead. This study proposes correcting the method for analyzing flowing well tests presented by Jacob and Lohman (1952) by considering the head losses due to friction in the well casing. The application of the proposed correction allowed the determination of a transmissivity (T = 411 m(2)/d) and storage coefficient (S = 3 x 10(-4)) which appear to be representative for the confined Guarani Aquifer in the study area. Ignoring the correction due to head losses in the well casing, the error in transmissivity evaluation is about 18%. For the storage coefficient the error is of 5 orders of magnitude, resulting in physically unacceptable value. The effect of the proposed correction on the calculated radius of the cone of depression and corresponding well interference is also discussed.

Immune correlate study on human Schistosoma japonicum in a well-defined population in Leyte, Philippines: I. Assessment of 'resistance' versus 'susceptibility' to S-japonicum infection

This study describes the categorical classification of 155 individuals living in an endemic village in Macanip, Leyte, Philippines as 'resistant' or 'susceptible' to Schistosoma japonicum infection using available exposure, infection and reinfection data collected from a 3-year water contact (WC) study. Epidemiological parameters including age, sex, and infection intensities in relation to observed reinfection patterns are also described. This classification was used in subsequent immunological studies described in two accompanying papers to identify protective immune mechanisms among resistant individuals induced by defined candidate vaccine molecules for S. japonicum. The study suggests that individuals who were most vulnerable to rapid reinfection were children belonging to the 5-14 age group. A drop in incidence at age group 15-19 and decreased intensity of infection starting at this age group and older (15+) suggests development of immunity. Controlling for the effect of the other variables, a multivariate analysis showed significant association for sex, in that females were more likely to be resistant. This implies that other than acquired immunity to infection, some age-dependent host factors may also play an important role in the overall changes of reinfection patterns seen in schistosomiasis japonica in this population. Crown Copyright (C) 2002 Published by Elsevier Science B.V. All rights reserved.

The paradox in ageing well: stories of older women in the Australian women's weeky

This Article does not have an abstract.

Influence of hysteresis on tidal capillary fringe dynamics in a well-sorted sand

Nielsen and Perrochet [Adv. Water Resour. 23 (2000) 503] presented experimental data for cyclic water movement in the vadose zone above an oscillating watertable. The response of the watertable to cyclic forcing was characterised by the ratios of the forcing head to watertable amplitudes and their associated phase lag. They found that their non-hysteretic Richards' equation model failed to represent the observed behaviour of these parameters. This paper explores the effect on the simulated capillary fringe dynamics (in terms of these parameters) of including varying degrees of hysteresis in the moisture retention curve used in a numerical model of their experiment. It is clear that hysteresis can indeed account for observed discrepancies between simulation and experiment and that the effect of hysteresis varies with the frequency of oscillation. The use of a single-valued mean retention curve, as advocated by some authors, fails to provide a match between the simulated and observed behaviour of the Nielsen and Perrochet parameters, but is shown to be adequate for predicting time-averaged soil moisture profiles. (C) 2003 Elsevier Ltd. All rights reserved.

Temperament in Bach’s Well-tempered clavier: a historical survey and a new evaluation according to dissonance theory

After a historical survey of temperament in Bach’s Well-Tempered Clavier by Johann Sebastian Bach, an analysis of the work has been made by applying a number of historical good temperaments as well as some recent proposals. The results obtained show that the global dissonance for all preludes and fugues in major keys can be minimized using the Kirnberger II temperament. The method of analysis used for this research is based on the mathematical theories of sensory dissonance, which have been developed by authors such as Hermann Ludwig Ferdinand von Helmholtz, Harry Partch, Reinier Plomp, Willem J. M. Levelt and William A. Sethares