DATE: Sunday, August 20, 2017
TIME: 08:00 AM - 05:00 PM
DATE: Tuesday, August 22, 2017
TIME: 03:00 PM - 05:00 PM
LOCATION:Main Auditorium (Building 20)
DATE: Wednesday, August 23, 2017
TIME: 04:15 PM - 05:15 PM
LOCATION:Lecture Hall 1 (2322), Engineering and Science Hall (Building 9)
Abstract:Oolitic grainstones can contain signiﬁcant hydrocarbon reserves. Due to variations in sedimentary processes and diagenetic alteration, the porosity and permeability values of these deposits are highly variable. We identiﬁed potential reservoir units in the Upper Khuﬀ Formation in central Saudi Arabia using an outcrop analog model by incorporating outcrop porosity and permeability information into oolitic grainstone facies in a three dimensional (3D) framework. Our results predict reservoir potential in subsurface Khuff equivalent and elsewhere. Six major surfaces representing fourth-order sequence boundaries were identiﬁed in outcrop, and these separate a ﬁve zones in the 3D outcrop model. In terms of depositional environments, these are deﬁned as Zones 1 and 2 (subtidal and sand sheets deposits in a shoal complex), Zones 3 and 4 (sand sheets and lenses deposits in a back shoal), and Zone 5 (tidal ﬂat). The population of lithofacies with in each zone in the 3D outcrop model was performed separately using a diﬀerent geostatistical algorithm. The resulting 3D facies model adequately illustrates the continuity of beds and fairly represents the stratigraphic architecture observed in outcrop. The 3D volume of the outcrop model was subdivided into three broad stratigraphic intervals. The lower interval (Zones 1 and 2) was deposited during a rapid sea-level rise at the Permian–Triassic transition and consists dominantly of oolitic units with oomoldic and interparticle porosity. The middle interval (Zones 3 and 4) was deposited during an interval of rapid sea-level ﬂuctuation and is characterized by the appearance of marine fauna after a long-term extinction in the lower Triassic interval. The dominant porosity types within this interval are moldic, interparticle, and intraskeletal. The upper interval (Zone 5) was deposited during a major sea-level regression. This interval contains a signiﬁcant amount of early diagenetic dolomite and is dominated by interparticle and intercrystalline porosity. The results demonstrate that including porosity values from outcrop measurements in a 3D outcrop facies model can provide more accurate visualizations of reservoir units. This methodology could be used to predict reservoir potential in analogous carbonate reservoirs.Biography:Hassan Eltom graduated from Khartoum University (Sudan) in 2001 with BSc degrees in Geology and Chemistry. He earned MSc and PhD degrees in Geology from King Fahd University of Petroleum and Minerals (KFUPM) in Dhahran, Saudi Arabia. His PhD is about an outcrop analog of the Arab-D reservoir in Saudi Arabia. Hassan has a total of 12 years of experience integrating field, lab, and modeling techniques to better understand past paleoenvironments and paleoceanography through study of ancient carbonate strata. Hassan experience includes four years with Schlumberger overseas as a Borehole Geologist and four years with KFUPM as a researcher. Currently, Hassan is working with Kansas Carbonate Interdisciplinary Carbonate Consortium (KICC) as a researcher, a post he holds since 2014. Hassan has been involved in and led research activities including the investigations of Paleozoic and Cenozoic carbonates of Saudi Arabia, Sudan, and Kansas. His research interests include carbonate sedimentology, stratigraphy and geochemistry and the characterization and modeling of reservoirs based on outcrop analog and 3-D geostatistical modeling.
DATE: Saturday, August 26, 2017
DATE: Wednesday, August 30, 2017
Abstract: The U.S. is now an exporter of natural gas, but for how long? My objective is to show how the summation of over 60,000 uncorrelated or weakly correlated random variables with arbitrary distributions leads to the emergence of a few Gaussians that describe with high delity, by play, the entire history of production in the U.S. gas mudrock plays. I considered the Utica, Woodford, Fayetteville, Barnett, Haynesville, Eagle Ford and Marcellus plays, and the Permian Basin. The history of production is described by a single Gaussian per play, while in those plays with more complex development (Barnett, Fayetteville and Haynesville) or consisting of several parts (Woodford), two or three Gaussians are sufficient for the almost perfect matches. The same Gaussian(s) then continues to predict the future production of the current set of wells, which is my base case. My prediction of the robust growth of future gas production is based in good part on the results of the Bureau of Economic Geology Sloan study, in which I participated. Here, I represent this future production growth by 1-6 consecutive Gaussians per play. The Eagle Ford and Permian plays produce mostly liquids but also signicant volumes of gas. The past and current developments in the major U.S. shale plays analyzed here provide 4.5 years of domestic gas consumption (145 Tscf), and the future production programs may add another 7 years (another 215 Tscf) or more if drilling in the Permian is even more vigorous than I predict. The current development will require 95,000 wells at a cost of $1.2 trillion or more. I assume that well attrition is balanced by well production increases. To my knowledge, no one has yet performed a similarly large and robust statistical analysis of gas production in almost all shale plays in the U.S. The Gaussians are such a strong emergent property of gas production from tens of thousands of wells that the ts of past production are picture-perfect with 3, 6, or 9 parameters per play. The theoretical footing and robustness of these Gaussians ensure that the future production from current wells is predicted in an optimal way in the least squares sense.Bio: Education Profile:Postdoctoral Fellow, Chemical Engineering Department, University of Minnesota, 1981-1983Fulbright Fellow, Chemical Engineering Department, University of Minnesota, 1978Ph.D. in Chemical Engineering, Silesian Technical University, 1979M.S. in Chemical Engineering, Silesian Technical University, 1974Research Interests:Professor Tad Patzek's research involves mathematical (analytic and numerical) modeling of earth systems with emphasis on multiphase fluid flow physics and rock mechanics. He also works on smart, process-based control of very large waterfloods in unconventional, low-permeability formations, and on the productivity and mechanics of hydrocarbon bearing shales.Patzek has co-designed and evaluated 7 field pilots of various oil recovery processes from waterflood, to steam and steam foam injection. More recently, Patzek got involved in human-machine interactions and safety culture in the offshore environment.In a broader context, Patzek works on the thermodynamics and ecology of human survival and energy supply schemes for humanity. He has participated in the global debate on energy supply schemes by giving hundreds of press interviews and appearing on the BBC, PBS, CBS, CNBC, ABC, NPR, etc., and giving invited lectures around the world.
DATE: Thursday, August 31 - Thursday, September 07, 2017
TIME: 12:00 AM - 12:00 PM
DATE: Sunday, September 10, 2017
DATE: Wednesday, September 13, 2017
Abstract:In the past, imaging of the near surface by seismic surveys usually was restricted to inverting just one type of arrival, e.g. refraction traveltimes for 2D P-velocity tomograms or dispersion curves for 1D S-velocity models. The advent of multigigaflop laptop computers, cheaper channel counts, and dense recording arrays now allow for the inversion of almost every type of arrival in the seismic records. In this presentation, I will show how the modern methods of seismic interferometry, waveform inversion, and multidimensional surfacewave inversion can be used to effectively invert almost every type of arrival for shallow seismic imaging. I will present examples that show how (a) "super virtual interferometry" can double more than the offset of useable first-arrivals in refraction inversion by enhancing the SNR of far-offset traces, (b) "full waveform inversion" inverts the diving waves and refractions to give high-resolution P-velocity images, (c) "parsimonious seismic interferometry" decreases the acquisition time of refraction and surface-wave surveys by at least one order-ofmagnitude, and (d) multidimensional inversion of surface-wave dispersion curves provides high-resolution estimates of the 2D shear-velocity tomogram to a depth of about the longest shear wavelength. I will present field data examples for hydrology applications, fault detection and earthquake hazards, and estimation of statics. All of these inversion methods can now be used for a single seismic survey with a sufficiently dense recording geometry.
Biography:Sherif M. Hanafy is a senior research scientist at KAUST. and is in charge of the geophysical field program (Seismology Lab). He teaches labs for geophysical field methods, seismic interferometry, traveltime tomography, early arrival tomography, data interpolation/extrapolation, and shallow application of resistivity and GPR methods.
He received his B.Sc. (1993) and M.Sc. (1996) degrees from Cairo University, Egypt in geophysics and then was awarded a Ph.D. from University of Kiel, Germany – Cairo University, Egypt (2002). He worked as an assistant professor at Cairo University (2002–2007). In 2004, he was awarded a one-year Fulbright scholarship at University of Utah. He went back to University of Utah for the second time as a post-doc (2007–2009), and in 2009, he moved to KAUST as a senior research scientist. In 2012, he was promoted to associate professor at Cairo University, Egypt.
Dr. Hanafy is mainly interested in shallow application of geophysics for geologic, engineering, environmental, and archaeological application. He uses several geophysical methods in his work including seismic, electric, and GPR methods.
DATE: Wednesday, September 20, 2017
Abstract: An inverse problem is the task often occurring in many branches of Earth sciences, where the values of some model parameters describing the Earth must be obtained given noisy observations made at the surface. In all applications of inversion, assumptions are made about the nature of the model parametrisation and data noise characteristics, and results can significantly depend on those assumptions. These quantities are often manually `tuned' by means of subjective trial-and-error procedures, and this prevents to accurately quantify uncertainties in the solution. A Bayesian approach allows these assumptions to be relaxed by incorporating relevant parameters as unknowns in the inference problem. Rather than being forced to make decisions on parametrization, the level of data noise and the weights between data types in advance, as is often the case in an optimization framework, the choice can be informed by the data themselves. Probabilistic sampling techniques such as transdimensional Markov chain Monte Carlo, allow sampling over complex posterior probability density functions, thus providing information on constraint, trade-offs and uncertainty in the unknowns. This presentation will present a review of transdimensional inference, and its application to different problems, ranging from Geochemistry to Solid Earth geophysics.
Biography: I am a CNRS researcher at the University of Lyon, France. I am interested in inverse problems in geophysics, and in particular in seismic imaging. I am interested in quantifying uncertainties and trade-offs, and exploring the level of resolution associated with different data types and inverse schemes. I have been mainly working on Bayesian (i.e. probabilistic) inverse methods where the solution is a probability density function describing the information we have about the Earth. After studying geophysics at the university of Strasbourg, I went to Australia for a PhD in seismology. After that, I did a postdoc at Berkeley, USA.
DATE: Sunday, September 24, 2017
TIME: 12:00 AM - 11:55 PM
DATE: Tuesday, September 26, 2017
TIME: 12:00 PM - 01:00 PM
LOCATION:Building 9, Level 2 Room 2325
ABSTRACT: Insights into the kinetics of complex processes can aid in discovering chemical conversion processes, improving energy production, synthesizing new materials, and remediating the environment. Design of chemical processes requires fundamental knowledge of how molecular structure of reactants affects product distribution and energy release. This presentation will cover the use of state-of-the-art comprehensive kinetic models together with high fidelity experimental measurements to study complex chemical processes. First, we will cover traditional applications of improving fuel combustion. Next, kinetic processes governing atmospheric air quality and its impact on climate modeling will be discussed. Finally, new research on simulating heterogeneous catalytic reactions will be presented. The research draws upon expertise in quantum chemistry, computational fluid dynamics, experimental methods, advanced diagnostics, and machine learning. We will see how engineers can utilize our tools to design better engines and fuels, improve urban air quality, and simulate catalytic reactors. BIOGRAPHY: Dr. Mani Sarathy is an Associate Professor of Chemical Engineering and Associate Director of the Clean Combustion Research Center (CCRC) at KAUST. Dr. Sarathy was previously a Postdoctoral Researcher in the Combustion Chemistry group at the U.S. Department of Energy Lawrence Livermore National Laboratory. He received his PhD and M.A.Sc. degrees in Environmental and Chemical Engineering at the University of Toronto and his B.A.Sc. in Environmental Engineering Chemical Specialization from the University of Waterloo. In 2015 and 2017, Dr. Sarathy was named a Clarivate Analytics (formerly Thomson Reuters) Highly Cited Researcher. His research interest is in developing sustainable energy technologies with decreased net environmental impact. A major thrust of his research is using chemical kinetic simulations to design fuels, engines, and reactors.
DATE: Wednesday, September 27, 2017
Abstract: Due to the growing global demand for energy and the relatively slow transition to sustainable energy sources, the combustion of carbon-based fuels will remain our major energy source for the coming decades. In order to achieve climate targets, transition technologies are required to reduce CO2 emissions during this period. Carbon Capture and Storage (CCS) is such a technology with a high potential to reduce greenhouse-gas emissions, and potentially even achieve a negative CO2 footprint – i.e. an active transfer of CO2 into the long-term carbon cycle. While for CO2 capture and transport, cost efficiency is the main driver for development, subsurface storage is focused on storage capacity and storage safety. With this in mind we are investigating plume migration and trapping mechanisms in the confined pore space of deep saline aquifers and depleted hydrocarbon reservoirs in order to assess the performance and risks of injection operations.
In the presentation, CCS will be discussed in relation to energy demand, ongoing injection operations and ‘Clean Fossil Fuels’. The presentation will illustrate the relevant subsurface fluid-displacement and trapping mechanisms. Special attention will be paid on relatively new developments in pore-scale physics. In the context of pore-scale fluid topology and processes, the analogy between capillary trapping and remobilization in CO2 sequestration operations and hydrocarbon phases in oil recovery will be discussed.
DATE: Thursday, September 28, 2017
LOCATION:Auditorium (Room 0215) between building 4 & 5
ABSTRACT: The field of electronic devices is one of the fastest growing fields. The rapid developments in this field enhanced the efficiency and capabilities of the electronic devices for different applications. Miniaturization of these devices and increasing their densities were two factors that played an important role in the developments of the electronic devices and their applications. However, improving these two factors accompanies with the problem of the generated heat in these devices which, in turn, affects their performance.
The significant amount of heat generated in the active region of the electronic device needs to be dissipated. Different methods are used to cool the electronic devices. One of these methods is to use a non-metallic high thermal conductivity materials. Wide bandgap semiconductors that possess a high thermal conductivity appear as suitable solution to overcome not only the heat dissipation problem but also to extend the operating temperature limit of the electronic device and eliminate the need for extra cooling source. Thus, thermal conductivity of these materials is a crucial parameter that needs to be studied and understood for better thermal management in such applications.
In this talk an overview of the thermal conductivity of wide bandgap semiconductors will be presented. The factors controlling the conductivity and the role played by each factor will be discussed briefly. Also calculated results for the thermal conductivity of wide bandgap semiconductors such as GaN and SiC will be shown and a comparison with available experimental data will be provided.