Characterization of operational safety in offshore oil wells

The main concern of activities developed in oil and gas well construction is safety. But safety during the well construction process is not a trivial subject. Today risk evaluation approaches are based in static analyses of existent systems. In other words, those approaches do not allow a dynamic analysis that evaluates the risk for each alteration of the context. This paper proposes the use of Quantitative and Dynamic Risk Assessment (QDRA) to assess the degree of safety of each planned job. The QDRA can be understood as a safe job analysis approach, developed with the purpose of quantifying the safety degree in entire well construction and maintenance activities. The QDRA is intended to be used in the planning stages of well construction and maintenance, where the effects of hazard on job sequence are important unknowns. This paper also presents definitions of barrier, and barriers integrated set (BIS), and a modeling technique showing their relationships. (c) 2006 Elsevier B.V. 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.

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.

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.

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.

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.

Digital radiography using digital detector arrays fulfills critical applications for offshore pipelines

Digital radiography in the inspection of welded pipes to be installed under deep water offshore gas and oil pipelines, like a presalt in Brazil, in the paper has been investigated. The aim is to use digital radiography for nondestructive testing of welds as it is already in use in the medical, aerospace, security, automotive, and petrochemical sectors. Among the current options, the DDA (Digital Detector Array) is considered as one of the best solutions to replace industrial films, as well as to increase the sensitivity to reduce the inspection cycle time. This paper shows the results of this new technique, comparing it to radiography with industrial films systems. In this paper, 20 test specimens of longitudinal welded pipe joints, specially prepared with artificial defects like cracks, lack of fusion, lack of penetration, and porosities and slag inclusions with varying dimensions and in 06 different base metal wall thicknesses, were tested and a comparison of the techniques was made. These experiments verified the purposed rules for parameter definitions and selections to control the required digital radiographic image quality as described in the draft international standard ISO/DIS 10893-7. This draft is first standard establishing the parameters for digital radiography on weld seam of welded steel pipes for pressure purposes to be used on gas and oil pipelines.

Design of concrete platforms offshore using the three-layer shell theory

The concrete offshore platforms, which are subjected a several loading combinations and, thus, requires an analysis more generic possible, can be designed using the concepts adopted to shell elements, but the resistance must be verify in particular cross-sections to shear forces. This work about design of shell elements will be make using the three-layer shell theory. The elements are subject to combined loading of membrane and plate, totalizing eight components of internal forces, which are three membrane forces, three moments (two out-of-plane bending moments and one in-plane, or torsion, moment) and two shear forces. The design method adopted, utilizing the iterative process proposed by Lourenco & Figueiras (1993) obtained from equations of equilibrium developed by Gupta (1896) , will be compared to results of experimentally tested shell elements found in the literature using the program DIANA.

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

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.

On chaotic behavior of a fixed offshore structure

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

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.

Rovearinids (stemless crinoids) in the Albian carbonates of the offshore Santos Basin, southeastern Brazil: stratigraphic, palaeobiogeographic and palaeoceanographic significance

Microfacies analysis of marine carbonates cored by Petrobras well 1-SPS-6 in the offshore Santos Basin (southeastern Brazil) has revealed a remarkable fossil assemblage of calpionellids (colomiellids), favusellids, hedbergellids, globigerinelloidids, buliminids, radiolarians, inoceramid prisms, roveacrinids, and saccocomids(?) preserved in lower Albian calcimudstones-wackestones of the lower part of the Guaruja Formation. This assemblage represents an allochtonous accumulation in a deep neritic to shallow bathyal hypoxic environment. Besides 'saccocomid-like' sections, the only determinable sections of roveacrinids are thecal plates of Poecilocrinus dispandus elongatus Peck, 1943. This species was previously only known from the Weno Formation of Texas. The Brazilian material extends its records farther south from at least the lower Albian, which then represents the earliest occurrence of this peculiar family in the South Atlantic region. Taking into account their Albian global distribution and the location of their oldest representative (Hauterivian near Alicante, Spain), the Roveacrinidae dispersed westward throughout all of Cretaceous Tethys. The Tethyan origin of Roveacrinidae is further evidence that, during late Aptian-Albian times, the northern South Atlantic (north of the Walvis-SBo Paulo Ridge) was supplied by a Tethyan water mass. (C) 2001 Elsevier B.V. Ltd. All rights reserved.

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.