Ehsan Ghazanfari

A Radio Propagation Model for Wireless Underground Sensor Networks

An accurate and simple radio propagation model for underground low-power devices such as wireless sensor nodes is introduced and its performance is evaluated by real wireless sensor nodes. The proposed model describes underground radio signal propagation that is proportional to

Subsurface Event Detection and Classification Using Wireless Signal Networks

e^{-2\alpha\rho}/\rho^2

Investigation of Generalized Relative Permeability Coefficients for Electrically Assisted Oil Recovery in Oil Formations

where

Sustainable Improvement of Clays Using Low-Carbon Nontraditional Additive

\rho

Subsurface monitoring using low frequency wireless signal networks

represents the distance and

Geomechanical Characterization of Shale Formations for Sustainable Production

\alpha

Subsurface geo-applications of wireless signal networks

represents the attenuation constant reflecting the soil properties. To evaluate the proposed underground radio propagation model, experiments measuring the radio signal strength with underground sensor nodes were conducted in various sub-surface conditions. Comparing the theoretical estimations of the underground radio propagation and the measured data, the theoretical model fits the measured data well within a 3.45dBm deviation or with an accuracy of 96.33\% on average.

Investigation of Construction Specification Effects on Energy Pile Efficiency

Subsurface environment sensing and monitoring applications such as detection of water intrusion or a landslide, which could significantly change the physical properties of the host soil, can be accomplished using a novel concept, Wireless Signal Networks (WSiNs). The wireless signal networks take advantage of the variations of radio signal strength on the distributed underground sensor nodes of WSiNs to monitor and characterize the sensed area. To characterize subsurface environments for event detection and classification, this paper provides a detailed list and experimental data of soil properties on how radio propagation is affected by soil properties in subsurface communication environments. Experiments demonstrated that calibrated wireless signal strength variations can be used as indicators to sense changes in the subsurface environment. The concept of WSiNs for the subsurface event detection is evaluated with applications such as detection of water intrusion, relative density change, and relative motion using actual underground sensor nodes. To classify geo-events using the measured signal strength as a main indicator of geo-events, we propose a window-based minimum distance classifier based on Bayesian decision theory. The window-based classifier for wireless signal networks has two steps: event detection and event classification. With the event detection, the window-based classifier classifies geo-events on the event occurring regions that are called a classification window. The proposed window-based classification method is evaluated with a water leakage experiment in which the data has been measured in laboratory experiments. In these experiments, the proposed detection and classification method based on wireless signal network can detect and classify subsurface events.

Creep Behavior of Shale Formations in Shale Gas Development

Evaluation of relative permeability coefficients is one of the key steps in reliable simulation of two-phase flow in porous media. An extensive body of work exists on evaluation of these coefficients for two-phase flow under pressure gradient. Oil transport under an applied electrical gradient in porous media is also governed by the principles of two-phase flow, but is less understood. In this paper, relative permeability coefficients under applied electric field are evaluated for a specific case of two- phase fluid flow in water-wet porous media, where the second fluid phase is oil. It is postulated that the viscous drag on the oil phase, exerted by the electro-osmotic flow of the water phase, is responsible for the transport of oil in the absence of a pressure gradient. Reliable prediction of the flow patterns necessitates accurate representation and determination of the relative permeability coefficients under the electrical gradient. The contribution of each phase to the flow is represented mathematically, and the relative permeability coefficients are evaluated through electro-osmotic flow measurements conducted on oil bearing rock cores.

Electrodialysis Treatment of Flow-Back Water for Environmental Protection in Shale Gas Development

Nontraditional low-carbon additives are widely used in the sustainable treatment of problematic soils for construction and pavement materials. This study investigated the mechanical and microstructural properties of white kaolin (low strength clay) and green bentonite (high swelling clay) treated with a low-carbon sodium silicate-based liquid additive. The mechanical tests included unconfined compressive strength (UCS), direct shear and one-dimensional compression tests. Microscale assessments, including a field emission scanning electron microscopic (FESEM) test, nitrogen-based Brunauer, Emmett, and Teller (N2-BET) surface area analysis and particle size analysis (PSA), were performed on the treated specimens to investigate the modification of soil structure, including soil fabric and interparticle forces. The performance of the proposed additive is demonstrated by the improvement of shear strength and compressibility of both tested soils. The optimum additive content was found to be 6%, and a significant improvement occurred in the first 7 days of curing. The mechanical property improvement is attributed to the formation of cementitious products and, subsequently, the modification of the soil structure. These cementitious products filled the pores and bonded the soil particles, resulting in an increase in interparticle forces. The sodium silicate-based additive can offer a low-carbon alternative to traditional additives such as cement and lime, which is significant from the engineering and environmental perspectives.