Development of chemical vapor deposition diamond burrs using hot filament

The fabrication of boring tools (burrs) for dentistry with the use of a hot-filament chemical vapor deposition (CVD) system, to form the diamond abrading structure, is reported here. The diamond was synthesized from a methane/freon gas mixture diluted in hydrogen. Comparative drilling tests with conventional diamond burrs and the CVD diamond burrs in borosilicate glasses demonstrated a lifetime more than 20 times larger for the CVD diamond burrs. Also, heat flow experiments in dentine showed that the CVD diamond burrs induce temperature gradients of the same order as the conventional ones. These characteristics of the CVD diamond burrs are highly desirable for odontological applications where the burrs' lifetime and the low temperature processing are essential to the quality and comfort of the treatment. © 1996 American Institute of Physics.

Application of ARX neural networks to model the rate of penetration of petroleum wells drilling

Bit performance prediction has been a challenging problem for the petroleum industry. It is essential in cost reduction associated with well planning and drilling performance prediction, especially when rigs leasing rates tend to follow the projects-demand and barrel-price rises. A methodology to model and predict one of the drilling bit performance evaluator, the Rate of Penetration (ROP), is presented herein. As the parameters affecting the ROP are complex and their relationship not easily modeled, the application of a Neural Network is suggested. In the present work, a dynamic neural network, based on the Auto-Regressive with Extra Input Signals model, or ARX model, is used to approach the ROP modeling problem. The network was applied to a real oil offshore field data set, consisted of information from seven wells drilled with an equal-diameter bit.

Applying a genetic neuro-model reference adaptive controller in drilling optimization

Motivated by rising drilling operation costs, the oil industry has shown a trend toward real-time measurements and control. In this scenario, drilling control becomes a challenging problem for the industry, especially due to the difficulty associated with parameters modeling. One of the drillbit performance evaluators, the Rate Of Penetration (ROP), has been used as a drilling control parameter. However, relationships between operational variables affecting the ROP are complex and not easily modeled. This work presents a neuro-genetic adaptive controller to treat this problem. It is based on an auto-regressive with extra input signals, or ARX model and on a Genetic Algorithm (GA) to control the ROP. © [2006] IEEE.

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.

A genetic neuro-model reference adaptive controller for petroleum wells drilling operations

Motivated by rising drilling operation costs, the oil industry has shown a trend towards real-time measurements and control. In this scenario, drilling control becomes a challenging problem for the industry, especially due to the difficulty associated to parameters modeling. One of the drill-bit performance evaluators, the Rate of Penetration (ROP), has been used in the literature as a drilling control parameter. However, the relationships between the operational variables affecting the ROP are complex and not easily modeled. This work presents a neuro-genetic adaptive controller to treat this problem. It is based on the Auto-Regressive with Extra Input Signals model, or ARX model, to accomplish the system identification and on a Genetic Algorithm (GA) to provide a robust control for the ROP. Results of simulations run over a real offshore oil field data, consisted of seven wells drilled with equal diameter bits, are provided. © 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.

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.

Histomorphometrical and molecular evaluation of endosseous dental implants sites in humans: Correlation with clinical and radiographic aspects

Objective: To evaluate the correlations between clinical-radiographical aspects and histomorphometric-molecular parameters of endosseous dental implant sites in humans. Material and methods: The study sample consisted of bone implant sites from the jawbones of 32 volunteers, which were classified according to two different systems: (1) based only on periapical and panoramic images (PP); (2) as proposed by Lekholm & Zarb (L&Z). Bone biopsies were removed using trephine during the first drilling for implant placement. Samples were stained with haematoxylin-eosin (HE), and histomorphometric analysis was performed to obtain the following parameters: trabecular thickness (Tb.Th), trabecular number, bone volume density (BV/TV), bone specific surface (BS/BV), bone surface density and trabecular separation (Tb.Sp). In addition, immunohistochemistry analysis was performed on bone tissue samples for the proteins, Receptor activator of nuclear factor kappa-B (RANK), RANK ligand (RANKL), osteoprotegerin (OPG) and Osteocalcin (OC). Also, the determination of the relative levels of gene expression was performed using Reverse transcription-real-time Polymerase Chain Reaction (RT-PCR). Results: PP and L&Z classification systems revealed a moderate correlation with BV/TV, BS/BV, Tb.Th and Tb.Sp. L&Z's system identified differences among bone types when BV/TV, BS/BV, Tb.Th and Tb.Sp were compared. A weak correlation between PP/L&Z classifications and the expression of bone metabolism regulators (RANK, RANKL, OPG e OC) was found. The analysis of mRNA expression showed no difference between the bone types evaluated. Conclusions: Our results suggest that PP and L&Z subjective bone-type classification systems are related to histomorphometric aspects. These data may contribute to the validation of these classifications. Bone remodelling regulatory molecules do not seem to influence morphological aspects of the jawbone © 2011 John Wiley & Sons A/S.

Chemical analysis and Vickers hardness of orthodontic mini-implants

Orthodontic mini-implants are used in clinical practice to provide efficient and aesthetically-pleasing anchorage. AIM: To evaluate the hardness Vickers hardness and chemical composition of mini-implant titanium alloys from five commercial brands. METHODS: Thirty self-drilling mini-implants, six each from the following commercial brands, were used: Neodent NEO, Morelli MOR, Sin SIN, Conexão CON, and Rocky Mountain RMO. The hardness and chemical composition of the titanium alloys were performed by the Vickers hardness test and energy dispersive X-ray spectroscopy, respectively. RESULTS: Vickers hardness was significantly higher in SIN implants than in NEO, MOR, and CON implants. Similarly, VH was significantly higher in RMO implants than in MOR and NEO ones. In addition, VH was higher in CON implants than in NEO ones. There were no significant differences in the proportions of titanium and aluminum in the mini-implant alloy of the five commercial brands. Conversely, the proportion of vanadium differed significantly between CON and MOR/NEO implants. CONCLUSIONS: Mini-implants of different brands presented distinct properties of hardness and composition of the alloy.

Neural models for predicting hole diameters in drilling processes

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Neural models for predicting hole diameters in drilling processes

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Review of Applied Spatial Data Analysis with R by R. S. Bivand, E. J. Pebesma, and V. Gomez-Rubio

Most authors struggle to pick a title that adequately conveys all of the material covered in a book. When I first saw Applied Spatial Data Analysis with R, I expected a review of spatial statistical models and their applications in packages (libraries) from the CRAN site of R. The authors’ title is not misleading, but I was very pleasantly surprised by how deep the word “applied” is here. The first half of the book essentially covers how R handles spatial data. To some statisticians this may be boring. Do you want, or need, to know the difference between S3 and S4 classes, how spatial objects in R are organized, and how various methods work on the spatial objects? A few years ago I would have said “no,” especially to the “want” part. Just let me slap my EXCEL spreadsheet into R and run some spatial functions on it. Unfortunately, the world is not so simple, and ultimately we want to minimize effort to get all of our spatial analyses accomplished. The first half of this book certainly convinced me that some extra effort in organizing my data into certain spatial class structures makes the analysis easier and less subject to mistakes. I also admit that I found it very interesting and I learned a lot.

Influence of effective number of pulses on the morphological structure of teeth and bovine femur after femtosecond laser ablation

Femtosecond lasers have been widely used in laser surgery as an instrument for contact-free tissue removal of hard dental, restorative materials, and osseous tissues, complementing conventional drilling or cutting tools. In order to obtain a laser system that provides an ablation efficiency comparable to mechanical instruments, the laser pulse rate must be maximal without causing thermal damage. The aim of this study was to compare the different morphological characteristics of the hard tissue after exposure to lasers operating in the femtosecond pulse regime. Two different kinds of samples were irradiated: dentin from human extracted teeth and bovine femur samples. Different procedures were applied, while paying special care to preserving the structures. The incubation factor S was calculated to be 0.788 +/- 0.004 for the bovine femur bone. These results indicate that the incubation effect is still substantial during the femtosecond laser ablation of hard tissues. The plasma-induced ablation has reduced side effects, i.e., we observe less thermal and mechanical damage when using a superficial femtosecond laser irradiation close to the threshold conditions. In the femtosecond regime, the morphology characteristics of the cavity were strongly influenced by the change of the effective number of pulses. (C) 2012 Society of Photo-Optical Instrumentation Engineers (SPIE). [DOI: 10.1117/1.JBO.17.4.048001]

Stable water isotopes of precipitation and firn cores from the northern Antarctic Peninsula region as a proxy for climate reconstruction

In order to investigate the climate variability in the northern Antarctic Peninsula region, this paper focuses on the relationship between stable isotope content of precipitation and firn, and main meteorological variables (air temperature, relative humidity, sea surface temperature, and sea ice extent). Between 2008 and 2010, we collected precipitation samples and retrieved firn cores from several key sites in this region. We conclude that the deuterium excess oscillation represents a robust indicator of the meteorological variability on a seasonal to sub-seasonal scale. Low absolute deuterium excess values and the synchronous variation of both deuterium excess and air temperature imply that the evaporation of moisture occurs in the adjacent Southern Ocean. The delta O-18-air temperature relationship is complicated and significant only at a (multi)seasonal scale. Backward trajectory calculations show that air-parcels arriving at the region during precipitation events predominantly originate at the South Pacific Ocean and Bellingshausen Sea. These investigations will be used as a calibration for ongoing and future research in the area, suggesting that appropriate locations for future ice core research are located above 600 m a.s.l. We selected the Plateau Laclavere, Antarctic Peninsula as the most promising site for a deeper drilling campaign.