(The talk will be given in English)

Speaker:     Dr. Alon Kipnis
                    Department of Statistics, Stanford University 

Sunday, March 25^th, 2018
15:00 - 16:00

Room 011, Kitot Bldg., Faculty of Engineering

Compressed sensing under optimal quantization

Abstract

A number of theoretical and experimental results have shown that generally speaking, compressed sensing with quantization can perform well if the signal is very sparse, the noise is very low, and the bit-rate is sufficiently large. However, a precise characterization of the fundamental tradeoffs between these quantities has remained elusive. 
Building upon ideas from statistical physics and random matrix theory, we provide single-letter formulas for the reconstruction error associated with optimal decoding under three specific quantization schemes: (1) estimate-and-compress source coding strategy where the signal is first estimated and then compressed with respect to the asymptotic distribution of finite subsets given the measurements. (2) compress-and-estimate where the measurements are quantized with respect to a Gaussian codebook and the sparse signal is estimated at the decoder, and (3) power-aware compress-and-estimate, where a Gaussian codebook is used in a rotated coordinate system. 
These quantization schemes are shown to be optimal in some problem regimes, and hence the aforementioned single-letter expressions provide explicit characterizations of the minimal mean-squared error as a function of the average quantization bit-rate, the spectral distribution of the sensing matrix, and the prior distribution of the signal. 

Short bio

Alon Kipnis received the B.Sc. degree in mathematics, the B.Sc. degree in electrical engineering and the M.Sc in mathematics, all from Ben-Gurion University of the Negev. He recently received the Ph.D degree in electrical engineering from Stanford University, and he is currently a postdoctoral scholar at the Department of Statistics there. His research focuses on the intersection of data compression, signal processing and machine learning.

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School of Mechanical Engineering Seminar
Monday, June 4, 2018 at 14:00
Wolfson Building of Mechanical Engineering, Room 206

Improved Oil Recovery of Mature Water Floods: Can we have a sizeable impact?

Dr. Carlos A. Glandt

Quantum Reservoir Impact (QRI), Houston TX

Mature waterfloods suffer from extended periods of ever increasing high water cuts. Although there are well established conformance technologies to arrest or mitigate these trends they have rarely been adopted across the field as part of a routine surveillance plan.

This presentation discusses two technologies that provide key information to design conformance jobs. They are Capacitance-Resistance Modeling (CRM) and interwell tracer tests. CRM, based on injection-production data only, provides a) interwell connectivities that are key to understand water distribution, and b) the ranking of injectors’ contribution to oil production, that immediately leads to injection optimization.  Interwell tracer tests provide fluid flow paths and swept volumes required to design in-depth conformance treatments.

Polymer flooding offers a more robust alternative to in-depth conformance. Polymer flooding mitigates both, poor sweep efficiency and unfavorable mobility ratios which are the main culprits of poor recovery.

The talk will address the impact of cost on the adoption of technologies to improve recovery factors and suggests means of lowering costs to make these technologies widely deployed. .

Biographical Note

Dr. Glandt has more than 36 years of Reservoir Engineering experience in areas such as Field Development, Heavy Oil, Waterflooding, EOR, Smart Wells, and others. He worked at Shell for 28 years both in the US and internationally, covering a wide range of technical assignments and managerial responsibilities. He was manager of EOR R&D, Smart Wells Global Implementation, Reservoir Engineering Skill Pool for the R&D Organization, and Technical Manager for the Cedar Creek Anticline asset in Montana.

After retiring from Shell Dr. Glandt was Director of Development of YPF, the leading Argentinean oil company, and is now Sr. Consultant for QRI of Houston with assignments in projects around the world, and of YPF’s leadership on the implementation of an aggressive tertiary recovery program for a wide range of fields in the portfolio.

He holds an Engineering Degree from the University of Buenos Aires and a PhD from Princeton University, both in Chemical Engineering.