• Geophysics and Geophysical Exploration
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• v.8 no.2
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• pp.156-162
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• 2005

Particularly in research related to seawater intrusion the change of fluid electrical conductivity is one of major concerns, and effective monitoring can help to optimize a water pumping performance in coastal areas. Special considerations should be given to the mounting of sensors at proper depth during the monitoring design since the vertical distribution of fluid electrical conductivity is sensitive to the characteristics of seawater intrusion zone. This tells us the multi-channel electrical conductivity monitoring is of paramount consequence. It, however, is a rare event when this approach becomes routinely available in that commonly used commercial stand-alone type sensors are very expensive and inadequate for a long term monitoring of electrical conductivity or water level due to their restricted storage and difficulty of real-time control. For this reason, we have developed a real-time monitoring system that could meet these requirements. This system is user friendly, cost-effective, and easy to control measurement parameters - sampling interval, acquisition range, and others. And this devised system has been utilized for the electrical conductivity monitoring in boreholes, Yeonggwang-gun, Korea. Monitoring has been consecutively executed for 24 hours, and the responses of electrical conductivity at some channels have been regularly increased or decreased while pumping up water. It, with well logging data implemented before/after pumping water, verifies that electrical conductivity changes in the specified depths originate from fluid movements through sand layer or permeable fractured rock. Eventually, the multi-channel electrical conductivity monitoring system makes an effective key to secure groundwater resources in coastal areas.

• Geophysics and Geophysical Exploration
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• v.13 no.1
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• pp.80-87
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• 2010

This study was conducted to develop a reservoir modelling workflow to reproduce the heterogeneous distribution of effective permeability that impacts on the performance of SAGD (Steam Assisted Gravity Drainage), the in-situ bitumen recovery technique in the Athabasca Oil Sands. Lithologic facies distribution is the main cause of the heterogeneity in bitumen reservoirs in the study area. The target formation consists of sand with mudstone facies in a fluvial-to-estuary channel system, where the mudstone interrupts fluid flow and reduces effective permeability. In this study, the lithologic facies is classified into three classes having different characteristics of effective permeability, depending on the shapes of mudstones. The reservoir modelling workflow of this study consists of two main modules; facies modelling and permeability modelling. The facies modelling provides an identification of the three lithologic facies, using a stochastic approach, which mainly control the effective permeability. The permeability modelling populates mudstone volume fraction first, then transforms it into effective permeability. A series of flow simulations applied to mini-models of the lithologic facies obtains the transformation functions of the mudstone volume fraction into the effective permeability. Seismic data contribute to the facies modelling via providing prior probability of facies, which is incorporated in the facies models by geostatistical techniques. In particular, this study employs a probabilistic neural network utilising multiple seismic attributes in facies prediction that improves the prior probability of facies. The result of using the improved prior probability in facies modelling is compared to the conventional method using a single seismic attribute to demonstrate the improvement in the facies discrimination. Using P-wave velocity in combination with density in the multiple seismic attributes is the essence of the improved facies discrimination. This paper also discusses sand matrix porosity that makes P-wave velocity differ between the different facies in the study area, where the sand matrix porosity is uniquely evaluated using log-derived porosity, P-wave velocity and photographically-predicted mudstone volume.