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.

Combining artificial intelligence and human problem solving: Proposing a new architecture for ITS's

This paper shows a comparative study between the Artificial Intelligence Problem Solving and the Human Problem Solving. The study is based on the solution by many ways of problems proposed via multiple-choice questions. General techniques used by humans to solve this kind of problems are grouped in blocks and each block is divided in steps. A new architecture for ITS - Intelligent Tutoring System is proposed to support experts' knowledge representation and novices' activities. Problems are represented by a text and feasible answers with particular meaning and form, to be rigorously analyzed by the solver to find the right one. Paths through a conceptual space of states represent each right solution.

Stability analysis of the attitude of artificial satellites subject to gravity gradient torque

Using a canonical formulation, the stability of the rotational motion of artificial satellites is analyzed considering perturbations due to the gravity gradient torque. Here Andoyer's variables are used to describe the rotational motion. One of the approaches that allow the analysis of the stability of Hamiltonian systems needs the reduction of the Hamiltonian to a normal form. Firstly equilibrium points are found. Using generalized coordinates, the Hamiltonian is expanded in the neighborhood of the linearly stable equilibrium points. In a next step a canonical linear transformation is used to diagonalize the matrix associated to the linear part of the system. The quadratic part of the Hamiltonian is normalized. Based in a Lie-Hori algorithm a semi-analytic process for normalization is applied and the Hamiltonian is normalized up to the fourth order. Once the Hamiltonian is normalized up to order four, the analysis of stability of the equilibrium point is performed using the theorem of Kovalev and Savichenko. This semi-analytical approach was applied considering some data sets of hypothetical satellites. For the considered satellites it was observed few cases of stable motion. This work contributes for space missions where the maintenance of spacecraft attitude stability is required.

Heat exchangers for cardioplegia systems: In vitro study of four different concepts

The aim of this work is the evaluation of four different heat exchangers used for myocardium during cardioplegic system in cardiac surgeries. Four types of shell and tube heat exchangers made of different exchange elements were constructed, as follows: stainless steel tubes, aluminium tubes, polypropylene hollow fiber, and bellows type. The evaluation was performed by in vitro tests of parameters such as heat transfer, pressure drop, and hemolysis tendency. The result has shown that all four systems tested were able to achieve the heat performance, and to offer low resistance to flow, and safety, as well as have low tendency to hemolysis. However, we can emphasize that the bellows type heat exchanger has a significant difference with regard to the other three types.

Decision-Making Tool for Knowledge-Based Projects in Offshore Production Systems

The development of an offshore field demands knowledge of many experts to choose the different components of an offshore production system. All the specialized parts of this knowledge are intrinsically related. The aim of this paper is to use Fuzzy Sets and knowledge-based systems to describe and formalize the phases of development of an offshore production system project, in order to share and to manage the required knowledge for carrying out a project, while at the same time proposing alternatives for the oil field configuration.

Harmonic identification using parallel neural networks in single-phase systems

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Electric power systems transient stability analysis by neural networks

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Comparison of artificial and natural, new and reused, brackish water for the larviculture of the freshwater prawn Macrobrachium rosenbergii in a recirculating system

Differences in culture duration, metamorphosis rate and the productivity in hatchery culture of M. rosenbergii using a closed system with natural and artificial brackish water were evaluated. Reuse of brackish water in more than one hatchery cycle was also evaluated. Natural and artificial brackish water constituted the two tested treatments, which were distributed in four independent recirculating systems (tank and respective biofilter). Four batches of cultures were conducted and the 2nd and 4th reused the water from the 1st and 3rd, respectively. Mean duration of the hatchery period was 28 d in natural brackish water and 31 d in artificial brackish water. The metamorphosis rate and the average productivity for the natural brackish water treatment were 74% and 60 postlarvae/ L. respectively, and values obtained with artificial brackish water were 55% and 44 postlarvae/L. The successful hatchery culture of M. rosenbergii in this specific artificial brackish water suggests its potential use in enterprises located far from the coast. Brackish water can be used in two consecutive cultures without a negative effect on productivity.

New approach of induction machine drive using an adaptive digital PID controller with gain planning based on artificial neural networks

The present work introduces a new strategy of induction machines speed adjustment using an adaptive PID (Proportional Integral Derivative) digital controller with gain planning based on the artificial neural networks. This digital controller uses an auxiliary variable to determine the ideal induction machine operating conditions and to establish the closed loop gain of the system. The auxiliary variable value can be estimated from the information stored in a general-purpose artificial neural network based on CMAC (Cerebellar Model Articulation Controller).

Impedance-based structural health monitoring with artificial neural networks

This paper presents a non-model based technique to detect, locate, and characterize structural damage by combining the impedance-based structural health monitoring technique with an artificial neural network. The impedance-based structural health monitoring technique, which utilizes the electromechanical coupling property of piezoelectric materials, has shown engineering feasibility in a variety of practical field applications. Relying on high frequency structural excitations (typically >30 kHz), this technique is very sensitive to minor structural changes in the near field of the piezoelectric sensors. In order to quantitatively assess the state of structures, multiple sets of artificial neural networks, which utilize measured electrical impedance signals for input patterns, were developed. By employing high frequency ranges and by incorporating neural network features, this technique is able to detect the damage in its early stage and to estimate the nature of damage without prior knowledge of the model of structures. The paper concludes with experimental examples, investigations on a massive quarter scale model of a steel bridge section and a space truss structure, in order to verify the performance of this proposed methodology.

Modeling and identification of fertility maps using artificial neural networks

The application of agricultural fertilizers using variable rates along the field can be made through fertility maps previously elaborated or through real-time sensors. In most of the cases applies maps previously elaborated. These maps are identified from analyzes done in soil samples collected regularly (a sample for each field cell) or irregularly along the field. At the moment, mathematical interpolation methods such as nearest neighbor, local average, weighted inverse distance, contouring and kriging are used for predicting the variables involved with elaboration of fertility maps. However, some of these methods present deficiencies that can generate different fertility maps for a same data set. Moreover, such methods can generate inprecise maps to be used in precision farming. In this paper, artificial neural networks have been applied for elaboration and identification of precise fertility maps which can reduce the production costs and environmental impacts.

Cariostatic effect of fluoride-containing restorative systems associated with dentifrices on root dentin

Objective. The aim of this in vitro study was to evaluate the interaction between two sources of fluoride (restorative systems and dentifrices) in inhibiting artificial root caries development. Methods. One hundred and eighty tooth segments were embedded in polyester resin, and sanded flat. Cylindrical cavities 1.0 mm-deep and 1.5 mm-diameter were prepared in root dentin and randomly restored by fluoride-containing restorative systems: Ketac-fil/Espe (Ke), Fuji II LC/GC Corp (Fj), F2000/3M (F2), Surefil/Dentsply (Su) or a control: Filtek Z250/3M (Z2). Ten experimental groups were made to test the association among the five restorative systems and two dentifrices: with F - (Sensodyne Baking Soda) or without F- (Sensodyne Original) (n = 18). After surface polishing, a 1 mm-wide margin around the restorations was demarcated and initial dentin surface Knoop microhardness values (KHNi) were obtained. The specimens were submitted to a pH-cycling model, and to applications of slurries of dentifrice. Afterwards the final dentin surface Knoop microhardness values (KHNf) were measured. Results. The differences between KHNi and KHNf, and the covariate KHNi were considered by the ANCOVA and Tukey's test (α = 0.05). The interaction between restorative system and dentifrice was statistically significant (p = 0.0026). All restorative systems provided some protection against artificial caries challenge when associated with the fluoride-containing dentifrice treatment. The means (standard deviation) of reductions in Knoop hardness values for systems associated with the fluoride-containing dentifrice were: Ke: 40.0(1.02)a, Fj: 41.9(1.02)b, F2: 43.3(1.04)c, Su: 43.5(1.00)c, Z2: 44.0(1.02)c; and with the non-fluoride-containing dentifrice were: Ke: 42.9(1.02)a, Fj: 44.7(1.01)b, F2: 45.2(1.09)bc, Su: 46.0(0.99)c, Z2: 46.6(0.99)c (statistical differences were expressed by different letters). Conclusion. The cariostatic effect shown by the fluoride-containing dentifrice could enhance that shown by Ketac-fil and Fuji II LC, and could mask that shown by F2000. © 2002 Elsevier Science Ltd. All rights reserved.

Transient stability analysis of electrical power systems using a neural network based on fuzzy ARTMAP

This work presents a methodology to analyze transient stability for electric energy systems using artificial neural networks based on fuzzy ARTMAP architecture. This architecture seeks exploring similarity with computational concepts on fuzzy set theory and ART (Adaptive Resonance Theory) neural network. The ART architectures show plasticity and stability characteristics, which are essential qualities to provide the training and to execute the analysis. Therefore, it is used a very fast training, when compared to the conventional backpropagation algorithm formulation. Consequently, the analysis becomes more competitive, compared to the principal methods found in the specialized literature. Results considering a system composed of 45 buses, 72 transmission lines and 10 synchronous machines are presented. © 2003 IEEE.

Tuning of fuzzy inference systems through unconstrained optimization techniques

This paper presents a new methodology for the adjustment of fuzzy inference systems. A novel approach, which uses unconstrained optimization techniques, is developed in order to adjust the free parameters of the fuzzy inference system, such as its intrinsic parameters of the membership function and the weights of the inference rules. This methodology is interesting, not only for the results presented and obtained through computer simulations, but also for its generality concerning to the kind of fuzzy inference system used. Therefore, this methodology is expandable either to the Mandani architecture or also to that suggested by Takagi-Sugeno. The validation of the presented methodology is accomplished through an estimation of time series. More specifically, the Mackey-Glass chaotic time series estimation is used for the validation of the proposed methodology.

ARISupport - Interaction support for augmented reality systems

The communication between user and software is a basic stage in any Interaction System project. In interactive systems, this communication is established by the means of a graphical interface, whose objective is to supply a visual representation of the main entities and functions present in the Virtual Environment. New ways of interacting in computational systems have been minimizing the gap in the relationship between man and computer, and therefore enhancing its usability. The objective of this paper, therefore, is to present a proposal for a non-conventional user interface library called ARISupport, which supplies ARToolKit applications developers with an opportunity to create simple GUI interfaces, and provides some of the functionality used in Augmented Reality systems. © Springer-Verlag Berlin Heidelberg 2005.