President and CEO, Shlomo Orr, Ph.D., P.Eng
, has over 33 years of extensive consulting, research, and project management experience in the field of Hydrogeology and Water Resources
. He earned a PhD in Hydrology and Water Resources with a minor in Soil and Water Sciences, and BSc and MSc in Civil Engineering.
Dr. Orr's background includes modeling, planning, and controlling complex subsurface flow and transport phenomena. He has a broad background in conceptual and computational-numerical modeling of fluid flow and solute transport in saturated and unsaturated porous and fractured formations, including stochastic models that account for uncertainties and provide the basis for risk assessment.
Dr. Orr has taught undergraduate and graduate courses in Groundwater Hydrology, Hydraulic Engineering, and Geostatistics, and advised MS and PhD students (as an Assistant Professor in Civil Engineering, and thereafter). He also taught a course on fluid dynamics in heterogeneous heaps for the Society of Mining Engineering (SME). He has solved some of the most complex problems in his field; has innovated substantially, and has published his advanced works on modeling and optimization in top journals, while serving as a peer reviewer, himself. His toolbox spans from simple analytical models to most detailed, complex, numerical, stochastic 3D models, as well as advanced machine learning models. Dr. Orr has been a reviewer of major books and top journals in hydrology and water resources, and has served in a special committee of SPE (Society of Petroleum Engineers) on Advanced Analytics. He was advising top scientists at the University of Saskatchewan on approaches to Integrated Nuclear Environmental Research and Training (INERT), and he has been serving as a Technical Advisor for SoilVision Systems (an advanced geo-software company). He has done breakthrough modeling work and evaluations of risk assessment models for the NRC (Nuclear Regulatory Commission).
- Dr. Orr provides expert witness services for cases involving Hydrology, Water Resources, and Environmental Risk
. He served as an expert reviewer on advisory boards of the US Army, Air Force, and Navy, on environmental projects. On the other side of the fence, he has conducted major environmental permit applications/investigations for special complex mining operations.
Dr. Orr's services are available to counsel representing plaintiff and defendant and include site review, written reports, depositions, and trial testimony as needed.
Areas of Expertise
View Dr. Orr's Consulting Profile
|Hydrogeology & GeohydrologyGroundwater ContaminationSubsurface Flow & Transport ModelingAquifer RemediationSoils and vadose zone hydrologyOil and Gas (multiphase flow)Petroleum HydrocarbonsNAPL and DNAPLVOC transportAcid Mine Drainage||SeepageDrainageWellhead ProtectionSoil Vapor IntrusionDewatering / DepressurizationLandfill MonitoringGeostatistics and stochastic modelingMonte Carlo SimulationsRisk-AssessmentGeothermal, Fracking, and Waste Injection|
This report is the third (and final) report in a series that addresses issues related to hydrologic uncertainty assessment at decommissioning sites. Analyses in the first two reports in this series emphasized the application of relatively simplified models of subsurface flow and transport. Because of their relative computational speed, such simplified models are particularly attractive when the impact of uncertainty in flow and transport needs to be evaluated. These same simplifications, however, have the potential to provide unrepresentative estimates of dose and its uncertainty. Such misrepresentation may have important consequences for decisions based on the dose assessments. The significance of this concern was evaluated by comparing results from uncertainty assessments conducted on a test case using a simplified modeling approach and a more complex/ realistic modeling approach.
Flow in heaps and dumps is essentially a two-phase flow phenomenon, though for many applications it could be simplified as unsaturated flow. Unlike saturated flow (typical of groundwater flow) where permeability is independent of other hydraulic parameters, unsaturated flow permeability depends on the degree of saturation and/or on capillary pressure. Several recent field studies suggest that flows in heaps and dumps tend to concentrate in preferred pathways, bypassing much of the ore. Different preferential flow phenomena are triggered, promoted, and influenced by different heap structures, by pretreatment, by the composition of the leach solution, and by application rates and schedules. The structure of heaps and dumps is determined and affected by each and every stage of their construction - from blasting to crushing to conveying and stacking of the material.
In our 1st article (Orr, 2000), we investigated different flow and transport phenomena that could significantly reduce leaching recovery. The combination of such understanding with advanced flow and transport modeling establishes the link between cause and effect, thereby directing operators to optimal design and construction of new heaps. Modeling of flow and transport in heaps could also point to a unique change in application method or rate that would maximize leaching enhancement of an existing heap under existing situation and leaching history. The use of such a model is cost effective in that it can simulate multiple scenarios of alternative leaching enhancement methods. By simulating a large number of irrigation scenarios, such a model can point to optimal and/or most promising alternatives for a particular heap.
Despite remarkable new developments in stochastic hydrology and adaptations of advanced methods from operations research, stochastic control, and artificial intelligence, solutions of complex real-world problems in hydrogeology have been quite limited. The main reason is the ultimate reliance on first-principle models that lead to complex, distributed-parameter partial differential equations (PDE) on a given scale. While the addition of uncertainty, and hence, stochasticity or randomness has increased insight and highlighted important relationships between uncertainty, reliability, risk, and their effect on the cost function, it has also (a) introduced additional complexity that results in prohibitive computer power even for just a single uncertain/random parameter; and (b) led to the recognition in our inability to assess the full uncertainty even when including all uncertain parameters.