This work is focused on the synthesis of the fructooligosaccharides (FOS) from sucrose using free inulinase from Kluyveromyces marxianus NRRL Y-7571 in aqueous and aqueous-organic systems. The most significant variables for the aqueous-organic system were identified using a fractional factorial design. The evaluated variables were the temperature, pH, sucrose concentration, inulinase activity, aqueous/organic ratio, and the polyethylene glycol concentration. The use of sequential experimental design methodology was shown to be very useful in the optimization of the FOS synthesis by inulinase either in aqueous or aqueous-organic systems. For the aqueousorganic system, the maximum Y FOS reached was 16.7± 1.1 wt.% with the following operational conditions: temperature of 40°C, enzyme activity of 4 U mL −1 , organic solvent/total system ratio of 25/100, pH of 6.0, and sucrose concentration of 55%. In the aqueous system, the maximum conversion obtained was 12.8±1.0 wt.% under the following conditions: 40°C, pH 5.0, 55% sucrose, and inulinase activity 4 U mL −1 .
-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.
New techniques for reservoir development are essential for dealing with the complexities of geological models. In this sense, numerical simulation is the tool used to define the quality of a production strategy. However, the process to define variables such as well numbers, completion layer, open timeline and operational conditions demand several simulations due to the high time consumption and computational effort. Sub-optimal results can be obtained from manual processes. Automatic processes can mitigate this problem but the computational effort is increased as a result of the number of simulations generated in the process. In order to minimize this problem, this paper proposes an assisted procedure for its automatic part using proxy models to accelerate the process. Furthermore, due to the reduced time to evaluate options, it allows a better evaluation of the solution space using better optimization techniques. Proxies have been used in important applications such as risk analysis and history matching but the use for definition of production strategy is not common. The proposed methodology involves the following components: statistical methods, experimental planning to generate the response surface methodology for the generation of proxies and consistency checking. The results show that it is possible to apply proxy models for this type of problem and they can identify the best production strategies, reducing the computational effort during the process. The suggested procedure is to use proxies in the automatic part of the assisted procedure used in the optimization process. The main contribution of this work was the demonstration that proxy models can be used for the definition of production strategies, bringing an additional option to the decision analysis process linked with petroleum field development. Introduction The main activity in reservoir engineering is the planning of strategies and economic evaluation for the development and management of petroleum fields. The numerical simulation is useful in a definition of a production strategy during the appraisal and development phases, 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 requirements to design the production facilities based on the low amount of information. The use of reservoir simulation has several constraints, such as: high number of blocks, variables and attributes which are time-consuming for the simulation and processes analysis, mainly in complex fields. Besides the high number of possibilities in a production strategy definition and the computational time linked with the numerical simulation, the process can be slow, forcing simplifications in the optimization process and consequently, decreasing the probability of finding better solution for the problem. Proxy models can be used as an auxiliary tool to deal with some of these constraints. This technique can simplify models with lower confidence levels in some outputs and an alternative for the numerical simulator in several procedures that do not require a higher precision in the results and a reduced number of simulations. Proxy models have been used in reservoir engineering applications, including uncertainty modeling, sensitivity analysis, history matching (Peng and Gupta, 2003 and Risso, 2007), risk assessment (Risso et al. 2007), performance prediction, upscaling (Schiozer et al., 2008) and development optimization (Venkataraman, 2000). In this study, proxy models have been used in an assisted procedure for production optimization, involving an integration of automatic and manual parts, for a definition of production strategies, bringing an additional option to the decision analysis process linked with petroleum field development. Figure 1 presents the general idea of proxy model application used in this 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. Introduction In petroleum exploration and production, a decision has to consider the risk involved in the process(1). Decision and risk analysis can be integrated with a wide variety of engineering and economic applications in the oil and gas industry, including economic evaluation of oil and gas reserves, reservoir modelling and simulation, seismic interpretation, petrophysical analysis and others(2). Decision analysis applied to petroleum field development plans are always strongly related to risk due to the uncertainties present in the process. There are many uncertainties that can influence the success of an E&P project. The most common uncertainties are due to the geological model, the recovery factor and the economic model(3). Quantification of uncertainty in reservoir performance is an important part of proper economic evaluation. The uncertainty in our understanding of a given reservoir performance arises from the uncertainty in the information we have about the attributes that control reservoir performance (permeability, oil water contact, etc.). In a risk methodology, it is possible to combine the geological uncertainties by using the Monte Carlo technique to estimate the range of uncertainty of some objective functions. These values can be obtained through numerical simulation flow or proxy models. The statistical theory, and especially the statistical (experimental) design approach, is well-suited to determine the most uncertain parameters to evaluate the impact of uncertainty on production forecasts, and to help making decisions during the reservoir's development(4). If reservoir simulation studies are conducted with a statistical design, response surface models can estimate how the variation of input attributes affects reservoir behaviour with a relatively small number of reservoir simulation models. Response surface models can test the relative importance of the attributes statistically(5). Because response surfaces are accurate and simple to evaluate, they are efficient proxies for reservoir simulators.
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