• Journal of Mechanical Science and Technology
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• 제19권8호
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• pp.1582-1596
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• 2005

Groundwater flow and behavior have to be investigated based on heterogeneous subsurface formation since the homogeneity assumption of this formation is not valid. Over the past twenty years, stochastic approach and Monte Carlo technique have been utilized very efficiently to understand the groundwater flow behavior. However, these techniques require lots of computational and numerical efforts according to the various researchers' comments. Therefore, utilizing new techniques with much less computational efforts such as Artificial Neural Network (ANN) in the prediction of the stochastic behavior for the groundwater based on heterogeneous subsurface formation is highly appreciated. The current paper introduces the ANN technique to investigate and predict the stochastic behavior of a well draw down in a confined aquifer based on subsurface heterogeneous hydraulic conductivity. Several ANN models are developed in this research to predict the unsteady two dimensional well draw down and its stochastic characteristics in a confined aquifer. The results of this study showed that ANN method with less computational efforts was very efficiently capable of simulating and predicting the stochastic behavior of the well draw down resulted from the continuous constant pumping in the middle of a confined aquifer with subsurface heterogeneous hydraulic conductivity.

• Spatial Information Research
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• 제8권2호
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• pp.301-315
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• 2000

지반조사 자료는 구조물을 시공하거나 설계하기 위해 필요한 기본자료이며 지층의 구성, 토질의 종류 등 기반을 구별하는 정보를 포함한다. 매년 대량으로 발생하는 지반조사 자료들은 현장의 특성을 정확하게 반영하기 때문에 지반조사 자료를 이용하여 지반을 분석한다면, 기존의 지질도나 토양도보다 뛰어난 결과를 얻을 수 있다. 따라서 비균질하고 비정형화된 지반의 특성을 고려하여 서로구별하는 특징을 추출하고, 현장의 특성을 정확하게 반영하는 지반분석 시스템이 필요하다. 본 논문은 지반조사 정보 시스템이 관리하고 있는 시추공의 지층구성 정보와 위치 근접성을 바탕으로 시추공을 클러스터링하여 지반의 구성을 분석하는 지반분석시스템의 설계 및 구현에 대해 전반적으로 기술하였다. 개발된 지반분석시스템은 지반조사 데이터베이스의 시추공 정보를 이용하여 지반이 가지고 있는 특성 정보를 추출하고, 이를 이용하여 유사한 특성 및 위치 근접성을 갖는 시추공의 집합으로 클로스터링하여 사용자에게 정확한 지반구성 정보를 제공하는 시스템이다. 또한, 수치지도의 사용으로 지리적 위치와 지역·지형에 대한 지반구조의 특성조사를 가능하게 하며, 지반조사를 필요로 하는 지역에 대한 지반의 유추가 가능하여 경제적 효과를 볼 수 있다. 따라서, 지반조사 데이터로부터 다양한 종류의 정보를 얻을 수 있으며 지질도나 토양도보다 정확한 지반특성을 제공할 수 있다.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2010년도 추계 학술발표회
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• pp.790-799
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• 2010

The Upper Devonian Grossmont Formation in Alberta, Canada reserves an estimated 50 billion cubic meters of bitumen and possess about 1/6 of the total bitumen resources in northern Alberta. However, unlike the overlying Athabasca oil sands, non conventional bitumen resources has not been developed as yet. The carbonate rocks of Grosmont Formation have been subject to various stages of diagenesis, including dolomatization and karstification with a strong effect on the distribution of porosity and permeability, which resulted in highly heterogeneous reservoirs. An extensive fracture logging and mapping was performed on total of six boreholes located in the study area to explore the characteristics of fracture geometry system and the subsurface structures of carbonates reservoir that holds bitumen. Fractal dimension was used as a measure of the statistical homogeneity of the fractured rock masses. The applicability of random Cantor dust, Dc, as a fractal parameter was examined systematically. The statistical homogeneity of fractured carbonates rock masses was investigated in the study area. The structural domains of the rock masses were delineated depthwise according to estimated Dc. The major fracture orientation was dominated by horizontal beddings having dip of $0-20^{\circ}$. Also, fractures having high dip angles existed with relatively low frequency. Three dimensional fracture network modeling for each structural domain has been performed based on fracture orientation and intensity, and some representative conceptual models for carbonates reservoir in the study area has been proposed. The developed subsurface conceptual models will be used to capture the geomechanical characteristics of the carbonates reservoir.