Earticle

연구에서는 다중저류층에 시추된 수평정의 유정시험 자료를 해석하기 위해 층간유동이 고려된 이 론해 모델을 시각화하여 개발하였다. 이 모델은 층간유동을 고려할 수 있는 반사전이법을 이용한 일정유입모 델 개념의 압력평균법을 적용하였다. 이론해 모델을 통해 산출되는 압력자료를 활용하여 저류층의 특성에 따 라 달리 나타나는 압력커브의 유동구간에 대한 특징을 분석함으로써, 수평정 시험 자료의 해석 시 저류층의 특성이 압력거동에 미치는 영향을 이해할 수 있음은 물론 보다 정확한 저류층의 특성을 규명하는데 목적을 두 고자 한다. 본 연구의 시스템은 층간유동이 가능한 2층 구조의 저류층이며, 수평정은 하부층에 위치한다. 본 분석에서는 지층의 수직투과도, 수평투과도와 두께 및 유체 점성도의 크기에 따라 달리 산출되는 압력강하 자 료에 대한 분석을 실시하여 1차 원형유동, 선형유동, 2차 원형유동 구간의 압력곡선의 특징을 규명하였다.

This paper presents the visualized analytical model including cross-flow effect for analyzing the horizontal well test data obtained from multi-layered reservoirs. The model adapted pressure averaging method of uniform flux model with the aid of reflection and transmission method in order to consider the cross-flow effect. The objective of this study is to identify the flowing regimes by analyzing pressure curves acquired by horizontal well test, and hence it is able to characterize the reservoir better. The designed system of this study is two-layered reservoir with cross-flow and the horizontal well is located at the bottom layer. In this analysis, analytical model has been applied for the various values of vertical and horizontal permeabilities, thickness of the formation as well as fluid viscosity. As a result, we observed the characteristics of pressure curve, which are first radial flow, linear flow, and second radial flow period.

One of the most important and effort intensive activity of the entire software development process is software testing. The effort involved chiefly increases because of the need to obtain optimal test data out of the entire search space of the problem under testing. Software test data generation is one area that has seen tremendous research in terms of automation and optimization. Generating or identifying an optimal test set that satisfies a more robust adequacy criteria, like data flow testing, is still a challenging task. A number of heuristic and meta-heuristics like genetic algorithm (GA), Particle Swarm Optimization (PSO) have been applied to optimize the test data generation problem. GA, although more popular, has its own difficulties such as complex to implement and slow convergence rate. In this paper an Adaptive Particle Swarm Optimization (APSO) algorithm is applied to generate test data for data-flow dependencies of a program guided by a novel fitness function. Adaptive PSO is used because of its capability of balancing in exploration and exploitation. A new fitness function is designed based on the concepts of dominance relations, weighted branch distance for APSO to guide the search direction. A set of benchmark programs and four modules of Krishna Institute of Engineering and Technology (KIET), Enterprise resource planning (ERP) system were taken for the experimental analysis. The experimental results show that the proposed adaptive PSO based approach performed significantly better than random search, Genetic Algorithm and PSO in enhancing the convergence speed.

We can define software testing as a process or a series of processes, design to make sure that computer code does what it was actually design to do and it doesn’t do anything unintended. Two of the most important software testing techniques are white box testing and black box testing. In my paper I have described one of the main software testing technique that is white box testing. We can define it as a test case design method that uses the control structure of the procedural design to derive test cases. I have also described briefly the working process of white box testing technique and some of its most frequently used techniques that are control flow testing, data flow testing, branch testing, basis path testing and loop testing.