Sunghoon Chung

Evaluation of elastic properties and temperature effects in Si thin films using an electrostatic microresonator

Laterally driven microresonators were used to estimate the temperature-dependent elastic modulus of single-crystalline Si for microelectromechanical systems (MEMS). The resonators were fabricated through surface micromachining from silicon-on-glass wafers. They were moved laterally by alternating electrostatic force at a series of frequencies, and then a resonance frequency was determined, under temperature cycling in the range of 25/spl deg/C to 600/spl deg/C, by detecting the maximum displacement. The elastic modulus was obtained in the temperature range by Rayleighs energy method from the detected resonance frequency. At this time, the temperature dependency of elastic modulus was affected by surface oxidation as well as its intrinsic variation: a temperature cycle permanently reduces the resonance frequency. The effect of Si oxidation was analyzed for thermal cycling by applying a simple composite model to the measured frequency data; here the oxide thickness was estimated from the difference in the resonance frequency before and after the temperature cycle, and was confirmed by field-emission scanning electron microscopy. Finally, the temperature coefficient of the elastic modulus of Si in the direction was determined as -64/spl times/10/sup -6/[/spl deg/C/sup -1/]. This value was quite comparable to those reported in previous literatures, and much more so if the specimen temperature is calibrated more exactly.

Development of Pareto-based evolutionary model integrated with dynamic goal programming and successive linear objective reduction

This paper presents a new Pareto-based evolutionary model incorporated with preference-ordering and objective-dimension reduction to improve the multi-directional searches for multi-objective problems.It induces a convergence toward the Pareto-optimal front by adjusting aspiration levels allocated to objectives and by excluding redundant objectives during optimization.Its usefulness was validated for multi-objective test problems comparing to conventional single- and multi-objective optimization models. This study investigates the coupling effects of objective-reduction and preference-ordering schemes on the search efficiency in the evolutionary process of multi-objective optimization. The difficulty in solving a many-objective problem increases with the number of conflicting objectives. Degenerated objective space can enhance the multi-directional search toward the multi-dimensional Pareto-optimal front by eliminating redundant objectives, but it is difficult to capture the true Pareto-relation among objectives in the non-optimal solution domain. Successive linear objective-reduction for the dimensionality-reduction and dynamic goal programming for preference-ordering are developed individually and combined with a multi-objective genetic algorithm in order to reflect the aspiration levels for the essential objectives adaptively during optimization. The performance of the proposed framework is demonstrated in redundant and non-redundant benchmark test problems. The preference-ordering approach induces the non-dominated solutions near the front despite enduring a small loss in diversity of the solutions. The induced solutions facilitate a degeneration of the Pareto-optimal front using successive linear objective-reduction, which updates the set of essential objectives by excluding non-conflicting objectives from the set of total objectives based on a principal component analysis. Salient issues related to real-world problems are discussed based on the results of an oil-field application.

Multi-Objective History Matching Allowing for Scale-Difference and the Interwell Complication

This study presents a new multi-objective history matching model to predict the individual well performance. Typical single-objective history matching, reducing the linearly averaged form of different-scaled objectives, has not covered the individual well performance properly. Previous multi-objective history matching, which could demonstrate the individual performance, shows the poor applicability as the number of objective function increases. This work aims to develop the accurate and diversity-preserved methodology to accomplish the global optimization. The scheme consists of dynamic goal programming and successive linear objective reduction incorporated with non-dominated sorting genetic algorithm-II. Dynamic goal programming grants priorities to solutions satisfying the expectation values for the objective functions with goal adjustment. SLOR removes redundant objective functions at the fitness evaluation in genetic algorithm. For the case study of waterflood history matching, the model is less sensitive to the form of objective functions and gridblock size. This study proves that reflecting relativity of different performances is able to improve prediction ability of the conventional single- and multi-objective approaches. The model provides a reliable range of uncertainty from diversity-preserved concept. The developed multi-objective optimization algorithm can easily apply to solve the convergence problem and the unrealistic estimation caused by scale-difference and the complication among multi-objective functions.

Operation Strategy of Steam and Gas Push in the Presence of Top Water Thief Zone

The paper presents the injection strategy of steam and nitrogen for one kind of non-condensable gases on SAGP (Steam And Gas Push) process in the presence of top water-bearing formation in heavy oil reservoir, which optimizes the energetic value using ANN (Artificial Neural Network). Top water thief zone is problematic to cause the water influx and the heat loss, and thereby reduces significantly the bitumen production efficiency. The valid amount of both steam and nitrogen is essential in order to accomplish the minimum energetic value and the production efficiency. The authors verify the prediction accuracy of the developed ANN by showing an intensive correlation with the time-consuming reservoir simulations. The optimal scenario results the increment of bitumen recovery up to around 16.1% and the decrement of objective function up to 13.72% compared to the SAGP with constant injection strategy. Before the chamber reaching the top water-bearing formation, nitrogen concentration maintains low level for chamber enlargement while both high pressure and nitrogen concentration are used to block the water influx into the heated zone after contacting the thief zone. In the period of late production, it reduced the injection pressure to enhance the thermal efficiency.

Optimal Injector Placement Coupled Multi-objective Genetic Algorithm with a Black-oil Simulator in Waterflooding Project

Well placement optimization is the process to search for the optimal operating conditions of infill wells for improving recovery. Infill drilling is expected to generate the additional revenue, but requires an enormous initial investment. This study determines the optimal number of injection wells and their locations in waterflooding project. Multi-objective genetic algorithm based on non-dominated sorting and crowding distance sorting is adapted to find Pareto-solutions which minimize the cost and maximize the revenue simultaneously. Non-dominated sorting guarantees the convergence and crowding distance sorting maintains the diversity of Pareto-solutions. The result shows that the suggested approach can obtain production scenarios of good quality satisfying given objectives successfully.

Injection Strategy of Solvent-aided Thermal Process for Optimal Bitumen Production in Oil Sand Reservoirs

Solvent-aided thermal stimulation supplies a small quantity of hydrocarbon solvent to the injected steam to improve thermal recovery in oil sands reservoir. Injection pressure is one key control parameter, which determines the amount of solvent and steam mixture consumed and its optimum level dominates the economic efficiency of thermal method. The paper depicts the optimal strategy of operating pressures to achieve the maximum economic value, the scheme of which is based on artificial neural network (ANN). The multi-layer perceptron using backpropagation minimizes the objective value including bitumen production, steam injection, solvent retention, commodity price, and manufacturing cost. The numerical approach integrating with ANN shows accurate predictability similar to the time-consuming reservoir simulation. The application to the Athabasca oil sands reservoir confirms the enhanced results compared with constant injection scenario and proposes the optimal schedule of injection pressures that repeats the increment and the decrement until reaching the same pressure level between injection and production well. It maintains consistency of peak production rate on account of latent heat despite decreasing cycle amplitude. The developed model could be applicable to make up injection scenarios economically without any modification of production facilities.