• Journal of Mechanical Science and Technology
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• v.19 no.8
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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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• v.8 no.2
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• pp.301-315
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• 2000

Every, year, a great amount of site investigation data is collected on site to obtain sufficient conditions. Investigation of subsurface conditions is prerequisite to the design and construction of structures and also provides information on ground properties such as geologic formation and types of soil. This data set, which portrays real representation of ground conditions over the existing geologic and soil maps, could be further utilized for analyzing the subsurface conditions. It is therefore necessary to develope a subsurface exploration analysis system which is able to extract the valuable information from the heterogeneous, non-normalized subsurface investigation data. This paper presents the overall design scheme and implementation on a subsurface exploration analysis system. The analysis system employs one of data set such as bore-hole data. The clustering technique employed in the developed system makes a large volume of bore-hole data into several groups in terms of ground formation and geographical vicinity. As a result of clustering, each group or cluster consists of bore-hole data with similar characteristics of subsurface and geographical vicinity. In addition, each clustered data is displayed on digital topographical map with different color so that the analysis of site investigation data could be performed in more sensible ways.

• Proceedings of the Korean Geotechical Society Conference
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• 2010.09a
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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.