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This paper presents the use of a radial basis function artificial neural network to estimate sensor readings exploring the analytical redundancy via auto-association. However, in order to guarantee optimal performance of the network, the training and optimization processes have been modified. In the conventional training algorithm, even if a stop criterion, such as summed squared error, is reached, one or more of the individual performance metrics, including: i) accuracy; ii) robustness; iii) spillover and iv) filtering of the neural network, may not be satisfactory while validating sensor measurements. Essentially, the proposed modification in the training algorithm is based on seeking to ensure that one or more of the metrics are met. This paper describes the proposed algorithm including all of its mathematical foundation. Afterward, a data set of a water injection pump for an oil and gas processing unit was used to train the RBF network using the conventional and the modified algorithm, and the performance of each was evaluated. Furthermore, the AAKR model is applied to the same dataset as a quality reference parameter. Finally, a comparison analysis of the developed models is presented for each of the performance metrics, as well as for overall effectiveness, demonstrating that the main advantage of the proposed approach is to obtain the estimation results equivalent or superior to the AAKR with shorter runtime and the disadvantage of having higher complexity during the model training.

This paper presents a real-world application of a mixed-integer receding horizon control in an onshore oil gathering network. The objective is to stabilize the operation of the gathering network by coordinating the automatic switching of pumps to avoid abrupt flow variations at the central station, minimizing pump switching and, at the same time, maintaining the level of fluid in the satellite stations under control. The applied technological solution easily integrates different systems, optimization tools and heuristic rules, allowing in-house development of complex applications.

This paper describes a novel technique and methodology to characterize the stress relaxation behavior of polymers. The shaft-hole coupling test (SHCT) characterizes the stress relaxation mechanics of polymers through measurements of the decrease in the stress between a metallic shaft and a drilled polymeric specimen over time. The main benefits of SHCT are the low implementation costs and reduced time for which the mechanical testing machine is in continuous use, by adopting a device that can be detached from the machine. The stress relaxation of polytetrafluoroethylene (PTFE) was identified by the SHCT over the course of several days, verifying the feasibility of the technique. In order to describe the viscoelastic behavior of the material, an optimization routine via the finite element method (FEM) was used to calibrate the parameters of the Parallel Rheological Framework (PRF) model. Furthermore, the FEM analysis was able to reproduce the mechanical behavior of the material successfully and the methodology was validated via comparison with a conventional stress relaxation test under uniaxial compression loading.

Principal component analysis (PCA) is a widespread and widely used in various areas of science such as bioinformatics, econometrics, and chemometrics among others. Once that PCA is based in the eigenvalues and the eigenvectors which are a very weak approach to high dimension systems with degrees of sparsity and in these situations the PCA is no longer a recommended procedure. Sparsity is very common in near infrared spectroscopy due to the large number of spectra required and the water absorption broad bands what makes these spectra very similar and with heavy sparsity in matrix dataset, demoting the precision and accuracy, in the multivariate modeling and within projections of data matrix in smaller dimensions. To overcoming these shortcomings the LASSO, a not PCA based method, model was applied to a NIR spectra dataset from Biodiesel and its performance was, statistically, compared with traditional multivariate modeling such as PCR and PLSR.

Oil production employing gas lift techniques enable the production of no natural flow wells and supply the energy lost in the reservoir caused by the field depletion, keeping the production in brown fields feasible. The multiphase flow conditions and the long pipes used to transport the fluids from the reservoir to the surface facilities, especially in deep and ultra-deepwater cases, may create unstable flow situations. Several publications in process control have discussed this problem since the 1980s, but the potential multivariable actions on the choke valve and gas lift flow have not been explored so far. In this paper the operating oil production system is treated through a nonlinear predictive control strategy. The strategy evaluation in a rigorous model (OLGA) shows the association between predictive capability and the integrated actuation in the manipulated variables results in an oil production increase and a partial or entire suppression of the instabilities in the multiphase flow. Furthermore, the rate of acting required on the valves is lower in the multivariable approach, allowing the use of slow choke valves as a final control element.

This work presents the development of an advanced control strategy using Model Predictive Control (MPC) for controlling the gas compression system of an offshore platform. It includes details about the complete phenomenological model of the system and of the software infrastructure developed to support the system implementation in real conditions. The proposed control structure has two main goals: (i) avoid unwanted regions of operation; and (ii) increase stability margins and availability. These goals are achieved by using a zone-control MPC and by adequately interacting with the regulatory control level. Although the proposed structure is general, this work exemplifies its application in a particular compression unit of a real offshore platform. Simulation results are presented in two different scenarios, one to test how the controller rejects a gas-load varying disturbance and another to analyze how the controller copes with an abnormal situation, losing real-time data of process variables or manipulated variables during operation. The good performance obtained in these two cases confirm the benefits provided by the proposed MPC strategy to the operation of the gas compression unit.

The natural gas produced in primary separation is passed through a compression system to be pressurized and conditioned before being sent to its final destination. The operation of that system needs to be safe and efficient to avoid equipment damage and reduce energy consumption. The stable and secure operation of the equipment in a compression system is provided, many times, by a classical regulatory control layer. In this work, we present a Model Predictive Control (MPC) strategy to provide setpoints for the regulatory control layer of a gas compression system, aiming to avoid excessive energy consumption, decrease the plant variability, and guarantee a stable and safe operation against load disturbances. The proposed method is tested in a digital twin of a typical industrial unit using a Dead-Time Compensator Generalized Predictive Controller (DTC-GPC). Some disturbances in the feed flow rate of gas were considered as case studies. The controller responded satisfactorily to these disturbances keeping the plant operation stable and returning the controlled variables in the desired operating range after a short time.

Downhole pressure is an important process variable in the operation of gas-lifted oil wells. The device installed in order to measure this variable is often called a Permanent Downhole Gauge (PDG). Replacing a faulty PDG is often not economically viable and to have an alternative estimate of the downhole pressure is an important goal. Using data from operating PDGs, this paper describes a number of issues dealt with in the development of soft sensors for several deepwater gas-lifted oil wells. Some of the tested models include nonlinear polynomials, neural networks, committee machines, unscented Kalman filters and filter banks. The variety of model classes used in addition to the diversity of oil wells considered brings to light some of the key-problems that have to be faced and reveal the strengths and weaknesses of each alternative solution. A major constraint throughout the work was the use of historical data, hence no specific tests were performed at any time. The aim of this work is to discuss the procedures, pros and cons of the tested solutions and to point to possible future directions of research.

This work describes a simplified dynamic model for control and real time applications in offshore deepwater and ultra-deepwater petroleum production systems. Literature about simplified dynamical models, capable of cover the global architecture of an offshore multiphase production system, is scarce. Hence, the proposed model integrates and adapts partial models available in the literature in order to generate a single model of the whole system. The model, designed to represent slugs generated by the casing heading and terrain/riser concomitantly, was evaluated by comparison with a traditional commercial simulator and was also implemented in two actual production systems. As a result, the model showed the capability of capturing complex dynamical behaviors, such as limit cycles, demonstrated to be numerically more stable than similar models in literature, fast enough to be used in real time applications and proved to be adherent to the commercial simulator and actual operating data from Petrobras production systems.

This work presents a modelling methodology for sensors and equipment condition monitoring developed during a research project to enhance dependability of pre-salt petroleum extraction platforms. The methodology aims to improve the capability of the auto-associative models applied for sensors monitoring in the last decades in nuclear power plants, chemical industry, refineries, gas transport and processing plants. However, actual operation problems or fault in equipment also may lead to false measurement error detection. This problem observed in the previous applications motivated the development of the improved method able to detect measurement errors and fault conditions in the process or equipment. This improvement has been obtained adding data (real or simulated) of the different conditions of operation, including the fault conditions (undesired data in the previous methodology). Therefore, the models become able to make accurate sensor estimation, even under fault conditions in the monitored process, and they also give a proper fault diagnoses about the measurement instruments and the process reducing false alarms compared to the traditional approaches. Also, some modelling challenges were observed during the development such as optimization of parameters, memory size and computing complexity. The methodology is demonstrated using simulated a process of a Petroleum Platform application. The achieved results showed a possible methodology to improve or replace the traditional approaches in the past application.

This work presents the use of radial basis function artificial neural network to estimate the sensors measurements, exploring the analytical redundancy existent among different sensors in a process. However, in order to guarantee good performance of the network the training and optimization process was modified. In the conventional training algorithm, although the stop criteria, such as summed squared error, is reached, one or more of the individual performance metrics of the neural network may not be satisfactory. The performance metrics considered are Accuracy (training error), Sensitivity matrix (sensors propagated error to the estimations) and Filtering matrix (sensor propagated noise to the estimations). The paper describes the proposed method including all the mathematical foundation. A dataset of a petroleum refinery is used to train a RBF (Radial Basis Function) network using the conventional and the modified method and the performance of both will be evaluated. Furthermore, AAKR (Auto-Associative Kernel Regression) model is used to the same dataset. Finally, a comparison study of the developed models will be done for each of the performance metrics, as well as for the overall effectiveness in order to demonstrate the superiority of the proposed approach.

Goal: Industry 4.0 enables the design of new models for process monitoring in which sensors, analyzers, and controls are positioned at different points in the process. The goal of this work is to present the modeling and control of a three-phase production separator with hydrocyclones to treat produced water on an oil platform of mature fields. Design / Methodology / Approach: The Methodology or approach used was to develop a model for the primary separator that allows its operation by means of a controller (fuzzy and PI) to manipulate the flow of discarded water, acting indirectly in the oil-in-water measure. Results: The results showed the consistency of the model for open loop simulations and the effectiveness of the controllers to comply with the discarding requirements for the closed-loop simulations. Limitations of the investigation: The limitation of the model and developed controllers is that they are only applied to platforms where water production exceeds the separators discharge capacity and the exceeding water can be offloaded to another equipment or to another platform. Practical implications: The main practical implications of this study are to maximize the flow of discarded water on mature field platforms, which produces elevated amounts of water, to conform the total oil and grease to the local law regulations. Additionally, it also increases oil production, with a higher limit of water production. Originality / Value: Compared to the previous authors, where the models of discarded flow is a function of the water-oil interface, this work developed a model that allowed the flow of the discarded water to be a function of its quality.

The short-term oil production optimization of offshore platforms which use continuous gas lift and consider several operational constraints is a challenging task, specially when decision variables include both continuous and integer ones. The intrinsic non-linearity of the Gas Lift Performance Curves (GLPC) naturally yields to Mixer-Integer Nonlinear Program formulation. Picewise linear approximations of the GLPC are usually applied in order to reduce the complexity of the optimization problem and to ensure an approximate solution, leading to Mixer-Integer Linear Program formulation. However, a comprehensive and comparative study of these two formulations for the oil production optimization problem is scarce in the literature. This paper provides a deep performance evaluation of these two approaches, showing the limitations of both strategies, and proposes a hybrid two-stage optimization strategy to achieve the global optimal operating condition which maximizes the oil production subject to safety, environmental and processing capacity constraints.

This work presents the use of radial basis function artificial neural network to estimate the sensors readings, exploring the analytical redundancy via auto association. However, in order to guarantee good performance of the network the training and optimization process was modified. In the conventional training algorithm, although the stop criteria, such as summed squared error, is reached, one or more of the individual performance metrics, including: i) accuracy; ii) robustness; iii) spillover and iv) filtering matrix of the neural network may not be satisfactory. The paper describes the proposed algorithm including all the mathematical foundation. A dataset of a petroleum refinery is used to train a RBF network using the conventional and the modified algorithm and the performance of both will be evaluated. Furthermore, AAKR model is used to the same dataset. Finally, a comparison study of the developed models will be done for each of the performance metrics, as well as for the overall effectiveness in order to demonstrate the superiority of the proposed approach.

The use of poorly treated water in hemodialysis centers may lead to fungal contamination, which poses a serious threat to immunologically debilitated hemodialysis patients. This study aimed to isolate and identify yeast species in the water of a Brazilian hemodialysis center by using classic microbiological techniques and Raman spectroscopy. For 12 months, a total of 288 water samples were collected from different points of the hemodialysis treatment distribution center. One hundred and forty-six yeast species were isolated and identified in the samples that tested positive for the presence of yeasts such as Candida parapsilosis (100 isolates, or 68.50%), C. guilliermondii (17 isolates, or 11.65%), Rhodotorula mucilaginosa (23 isolates, or 15.75%), R. glutinis (three isolates, or 2.05%), and Trichosporon inkin (three isolates, or 2.05%). Yeast susceptibility to the antifungal fluconazole was also assayed. Only two C. guilliermondii isolates were resistant to fluconazole: the minimal inhibitory concentrations were higher than 64 μg/mL. The different yeast species present in the water of a Brazilian hemodialysis center call for more effective water disinfection procedures in this unit. Raman spectroscopy is an excellent tool to identify yeast species and is potentially applicable in routine water monitoring in hemodialysis units.

Este trabalho trata da determinacao da producao de liquido em cada poco submarino de uma plataforma de producao de petroleo do tipo FPSO (Floating, Production, Storage and Offloading). A utilizacao de medidores de fluxo multifasicos e uma solucao cara e indisponivel na maioria das unidades produtoras e as medidas de variaveis de fundo (pressao e temperatura) possuem baixa confiabilidade. Alem disso, os sensores submersos, em geral, deixar de operar apos alguns anos. Assim, o objetivo desta pesquisa e desenvolver um algoritmo para estimar a vazao de producao em pocos de petroleo, com base somente nas medidas de variaveis de superficie (pressao, temperatura e posicao de valvula). O metodo proposto consiste em

A practical control algorithm for stabilizing flow in risers and oil production wells should meet several requirements. i) be simple, ii) able to operate with low-cost measurements and possibly contaminated with noise and iii) stabilize the flow without setting a value for the bottom pressure. An algorithm has been proposed which does not fix any reference for the bottom pressure. It uses as reference a value equal to zero for the derivative of the bottom pressure. This paper presents some changes in the algorithm in order to avoid the difficulties with derivatives and to simplify the tuning of its parameters. It also proposes a control methodology to suppress oscillations in the absence of automated production choke and downhole measurements.

The application of advanced control techniques in offshore oil production plants is a challenge. There are many changes in operational points in time, for example, the process is affected by the natural oil well's behavior dynamics. Besides that, limited instrumentation available has to be considered when thinking in oil optimization and control. In order to improve the scenario, taylor-made advanced control modules have been developed for those units. The present article will present development, implementation and results of anti-slug control for three platforms located at Campos and Santos basins. The controllers were designed to achieve improvement in operational stability and safety, decreasing in unscheduled compressor shutdown events, as well as increasing in operational efficiency.

This work presents the utilization of Model Predictive Control (MPC) as a tool to help the regulatory control of the compression system of an offshore oil and gas production unit in three importan tasks: to avoid the system to approximate the surge region to increase stability margins and availability and to minimize energy consumption and gas flaring. Although the proposed structure is general, the present work focus on the solution proposed for a particular compression unit of a real off-shore platform. The modeling of the process and the proposed MPC algorithm are discussed and some simulated results illustrate the advantages of the proposed strategy.

It is difficult to control and to manage wells’ start-up in offshore platforms. In order to solve this problem an intelligent system can play an important role, since available qualitative operator and design knowledge can be easily implemented to assist the operator during wells’ start-up. This paper describes the integration of an expert system associated with anti-slug control for well start-up. The intelligent system has many heuristic rules to implement the automation of the start-up procedures, like the opening choke valve while simultaneously respecting many constraints. Severe slugging flow regimes are one of the major disturbances for the operation of offshore production platforms, and can cause many unplanned shutdowns. Therefore, it’s important to combine start-up intelligent system with an anti-slug advanced control module for each well. The benefits are associated to reducing possibility of unplanned shutdowns during well start-up operational procedures, decreasing operators’ stress and also helping to minimizing impacts to the environment. A prototype was implemented in one platform with good results for a safe and efficient wells start-up procedure. This paper will present the development and results of this intelligent system for wells’ start-up and anti-slug control for offshore platforms.