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.

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.

Effect of Drilling Technique on the Early Integration of Plateau Root Form Endosteal Implants: An Experimental Study in Dogs

Purpose: This study tested the hypothesis that early integration of plateau root form endosseous implants is significantly affected by surgical drilling technique.Materials and Methods: Sixty-four implants were bilaterally placed in the diaphysial radius of 8 beagles and remained 2 and 4 weeks in vivo. Half the implants had an alumina-blasted/acid-etched surface and the other half a surface coated with calcium phosphate. Half the implants with the 2 surface types were drilled at 50 rpm without saline irrigation and the other half were drilled at 900 rpm under abundant irrigation. After euthanasia, the implants in bone were nondecalcified and referred for histologic analysis. Bone-to-implant contact, bone area fraction occupancy, and the distance from the tip of the plateau to pristine cortical bone were measured. Statistical analyses were performed by analysis of variance at a 95% level of significance considering implant surface, time in vivo, and drilling speed as independent variables and bone-to-implant contact, bone area fraction occupancy, and distance from the tip of the plateau to pristine cortical bone as dependent variables.Results: The results showed that both techniques led to implant integration and intimate contact between bone and the 2 implant surfaces. A significant increase in bone-to-implant contact and bone area fraction occupancy was observed as time elapsed at 2 and 4 weeks and for the calcium phosphate-coated implant surface compared with the alumina-blasted/acid-etched surface.Conclusions: Because the surgical drilling technique did not affect the early integration of plateau root form implants, the hypothesis was refuted. (C) 2011 American Association of Oral and Maxillofacial Surgeons J Oral Maxillofac Surg 69: 2158-2163, 2011

Força crítica em nado atado para avaliação da capacidade aeróbia e predição de performances em nado livre

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

Validity of the running anaerobic sprint test for assessing anaerobic power and predicting short-distance performances

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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.

An ontology based for drilling report classification

This paper presents an application of an ontology based system for automated text analysis using a sample of a drilling report to demonstrate how the methodology works. The methodology used here consists basically of organizing the knowledge related to the drilling process by elaborating the ontology of some typical problems. The whole process was carried out with the assistance of a drilling expert, and by also using software to collect the knowledge from the texts. Finally, a sample of drilling reports was used to test the system, evaluating its performance on automated text classification.

Development of intelligent systems for well drilling and petroleum production

Domains where knowledge representation is too complex to be described analytically and in a deterministic way is very common in the petroleum industry, particularly in the field of exploration and production. In these domains, applications of artificial intelligence techniques are very suitable, especially in cases where the preservation of corporate and technical knowledge is important. The Laboratory for Research on Artificial Intelligence Applied to Petroleum Engineering (LIAP) at Unicamp, has, during the last 10 years, dedicated research efforts to build intelligent systems in well drilling and petroleum production fields. In the following sections, recent advances in intelligent systems, under development in the research laboratory, are described. (C) 2001 Published by Elsevier B.V. B.V.

Comparative analysis of drilling frequencies in recent brachiopod-mollusk associations from the Southern Brazilian Shelf

Over 14,000 specimens-5,204 brachiopods, 9,137 bivalves, and 178 gastropods-acquired from 30 collecting stations (0 to 45 m depth) in the Ubatuba and Picinguaba bays, southern Brazil, were compared for drilling frequencies. Beveled (countersunk) circular-to-subcircular borings (Oichnus-like drill holes) were found in diverse bivalves but also in the rhynchonelliform brachiopod Bouchardia rosea-a small, semi-infaunal to epifaunal, free-lying species that dominates the brachiopod fauna of the southern Brazilian shelf. Drill holes in bivalve mollusks and brachiopods are comparable in their morphology, average diameter, and diameter range, indicating attacks by a single type of drilling organism. Drill holes in brachiopods were rare (0.4%) and found only at five sampling sites. Drillings in bivalves were over 10 times as frequent as in brachiopods, but the average drilling frequency was still low (5.6%) compared to typical boring frequencies of Cenozoic mollusks. Some common bivalve species, however, were drilled at frequencies up to 50 times higher than those observed for shells of B. rosea from the same samples. Due to scarcity of drilled brachiopods, it is not possible to evaluate if the driller displayed a nonrandom (stereotyped) site, size, or valve preference. Drilled brachiopods may record (1) naticid or muricid predation, (2) predation by other drillers, (3) parasitic drillings, and (4) mistaken or opportunistic attacks. Low drilling frequency in brachiopods is consistent with recent reports on ancient and modern examples. The scarcity of drilling in brachiopods, coupled with much higher drilling frequencies observed in sympatric bivalves, suggests that drilling in brachiopods may have been due to facultative or erroneous attacks. The drilling frequencies observed here for the brachiopod-bivalve assemblages are remarkably similar to those reported for Permian brachiopod-bivalves associations. This report adds to the growing evidence for an intriguing macroecological stasis: multiple meta-analytical surveys of present-day and fossil rhynchonelliform brachiopods conducted in recent years also point to persistent scarcity and low intensity of biotic interactions between brachiopods and drilling organisms throughout their evolutionary history.

EFFECT OF FENCAMFAMINE ON AVOIDANCE PERFORMANCES OF RATS

The effects of fencamfamine (1.0 and 5.0 mg/kg, ip, single dose) on an inhibitory task were studied in rats (N = 15 per group). Post-training treatment with fencamfamine (1.0 mg/kg) significantly increased avoidance latency from 23 +/- 3 to 146 +/- 28 and 170 +/- 33 s for training day 1 and day 7, respectively, indicating an enhancement of retention. However, retention was significantly reduced with a high dose of fencamfamine (5.0 mg/kg). These results demonstrate that fencamfamine caused a reproducible dose-related increase and reduction in avoidance latency.

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.