Fernanda Vaz Alves Risso

Comparative studies of the stability of free and immobilized inulinase from Kluyveromyces marxianus NRRL Y-7571 in aqueous-organic solutions

Abstract - Enzymes have been extensively used in organic solvents to catalyze a variety of reactions of biological and industrial significance. In this work, the characteristics of free and immobilized inulinase were investigated in buffered solutions of butyl acetate. The influences of the organic solvent content on the optimal temperature and pH, the stabilities to temperature and pH and the kinetic parameters were systematically evaluated. The results showed that the organic solvent content had no effect on the optimal pH, either in the free or immobilized inulinase. For the immobilized enzyme, the optimal temperatures ranged from 55°C to 60°C, depending on the content of butyl acetate. At higher butyl acetate content, the stability of the immobilized enzyme increased for both pH and temperature. The organic solvent showed the tendency to increase the values of the kinetic parameters K m and v max for both free and immobilized inulinase. Keywords : Organic solvent; Inulinase; Stability; Kinetic parameters.

Risk Assessment of Petroleum Fields—Use of Numerical Simulation and Proxy Models

Abstract The development of petroleum fields is a complex task due to the high influence of uncertainties on E&P projects. During the appraisal and development phases, uncertainties related to geologic and fluid models play an important role, especially in offshore heavy oil fields due to the low economic return, limited flexibility, and importance of reservoir modeling. The flexibility is limited because of the necessity to design the production facilities based on a low amount of information. The reservoir modeling process is important because risk of field development projects is normally caused by a high uncertainty on the recovery factor. Due to the necessity of a more robust evaluation of recovery factor, risk assessment methodologies normally are integrated with reservoir simulation, which is the best available tool to predict reservoir performance. However, higher precision on prediction of reservoir behavior is normally associated with fine simulation grid and high computation effort. In this article, some alternatives are presented to improve the efficiency of risk assessment, considering precision and computation effort. Among these alternatives are (1) use of coarse models, (2) use of coarse models modified to reproduce fine grid results, (3) simplifications on the risk assessment procedure, and (4) use of proxy models based on statistical (experimental) design and response surface methodology. A general discussion, including each alternative, use of upscaling techniques, reduction of grid size, number of attributes, use of parallel computing, and use of proxy models are made based on previous publications and results of a case study. The methodology applied to quantify risk involves a sensitivity analysis in order to reduce the number of critical attributes and simulation of reservoir models obtained through the combination of these attributes. Afterward, a statistic treatment is used to evaluate the risk involved in the process. Based on a case study, it is shown that the use of faster simulation models and proxies can speed up risk assessment, but a few steps must be performed to guarantee the quality of the results.

Risk Assessment of Oil Fields Using Proxy Models: A Case Study

Reservoir studies commonly consider many scenarios, cases and realizations. However, reservoir simulation can be expensive. Statistical design has been used in reservoir engineering applications, including performance prediction, uncertainty modelling, sensitivity studies, upscaling, history matching and development optimization. If reservoir simulation studies are conducted with a statistical design, response surface models can estimate how the variation of input factors affects reservoir behaviour with a relatively small number of reservoir simulation models. In petroleum exploration and production, a decision has to consider the risk involved in the process which can be obtained by quantifying the impact of uncertainties on the performance of the petroleum field in question. The process is even more critical because most of the investments are realized during the phase in which the uncertainties are greater. The statistical design is efficient to quantify the impact of the uncertainties of the reservoirs in the production forecast and to reduce the number of simulations to obtain the risk curve. The main objective of this work is the application of the statistical design: Box-Behnken and Central Composite Design using different attributes ranges. To compare the precision of the results, different techniques are used. These are the Derivative Tree Technique by simulation flow, the Monte Carlo Technique and the Response Surface Methodology.

Comparison of Methodologies To Evaluate the Risk of Petroleum Fields

A risk analysis process can be applied to several p h ses of a petroleum field. The methodologies required in the decisionmaking process depend on the level of uncertainties , which vary according to the field phase. This work is foc used on the development phase and the decision process is based on probabilistic procedure to represent all possible s c narios of the reservoir. The uncertain attributes can be combined through derivative tree or Monte Carlo technique. The risk can be evaluated by the Net Present Value, which depends o the reservoir performance. The reservoir production pre diction can be obtained through numerical simulation or res pon e surface. Most of works consider only the applicatio n of these procedures and comparisons of these techniques are not well evaluated. The goal of this work is to apply the al ternative combinations of these techniques to petroleum field s and compare them to determine the result reliabilities. The combination of Monte Carlo and numerical simula tion is not viable, in some cases, due to the great numb er of simulations. The attribute combination by derivativ e tree can be an alternative, but can also yield a high number of simulation runs. Alternatives to speedup the proces s are to reduce the number of attributes and their discretiz ation levels or substitute the conventional reservoir modeling b y faster ones, such as the response surface, which is suited to valuate the impact of uncertainty on production forecasts. The contributions of this work are to: (1) to deter mine if the response surface can substitute the reservoir s imulator, (2) to evaluate the capability of the response surface to substitute the reservoir simulator in order to obtain the risk curves, (3) to provide a guide to select an adequate combination o f techniques according to the desired precision and ( 4) to determine if it is possible to reduce the number of simulation runs, maintaining the precision. Introduction Risk is always associated to a petroleum field, wit h minor or major intensity, depending on its life phase. In de velopment phase, the number of uncertainties is high, affecti ng strongly the financial results and requiring high investment . Demirmen (2001) states the risk associate in development dec isionmaking process involves suboptimal development and opportunity loss. For this reason, the decision-mak ing process in such phase must be probabilistic. Probabilistic approaches are common in exploration phase (Newendorp and Schu yler, 2000 and Rose, 2001). In development phase, the imp ortance of uncertainties increases significantly, mainly on the recovery factor, however probabilistic methodologies are not used frequently to assess the risk (Schiozer et al, 2004). Most of works present methodologies to assess risk in development phase and they present only illustrativ e examples of their application. Comparison of the performance of different risk analysis methodologies is not common in the literature. For this reason, the main goal of this work is to compare risk assessment methodologies in developmen t phase. The first step of a risk methodology is to combine th geological uncertainties. Two possible manners are: Monte Carlo and Derivative Tree techniques, resulting in many reservoir geological models. The second step is to calculate the value of some specific objective functions, suc h as Net Present Value and Cumulative Oil Production. These values can be obtained through numerical simulation flow o r faster simulation models. In the last step, the risk curve s are built through a statistical treatment (Figure 1). Figure 1. Risk assessment in development phase. SPE 107736 Comparison of Methodologies to Evaluate the Risk of Petroleum Fields Eliana L. Ligero, SPE, Fernanda V. Alves Risso, SPE, and Denis J. Schiozer, SPE, UNICAMP Uncertain Attributes Monte Carlo Technique Derivative Tree Technique Combination Objective Function Calculations Numerical Flow Simulation Risk Curves Risk Curves Statistical Treatment Faster Simulation Models Numerical Flow Simulation Risk Curves Risk Curves Statistical Treatment Faster Simulation Models