Magnetic NanoMappers

ABSTRACT recorded by a receiver antenna. This trace or waveform con-tains a first arrival signal as well as other signals eventuallyproduced from reflections or refractions of the radiated waves.The waves are affected in terms of traveltime and amplitude byvariations in their propagation speed due to losses caused bytraveling through different media. This effect is governed bythe dielectric permittivity (), magnetic permeability () andelectrical conductivity () of each medium. As the EM wavestravel from transmitter to receiver, the time delay of the firstarrival signal peak recorded at the receiver is used to determinethe velocity of wave propagation. The difference in time delayswith respect to a given medium can be inverted to build a to-mographic representation of the medium9-11.

The review of prior works discussing traveltime tomogra-phy showed that all approaches were based on mapping elec-trical conductivity or permittivity. None considered mappingvariations in magnetic permeability. In our approach, we ex-tended the prior works in a new way by employing variationsin magnetic permeability to generate new fluid imaging capa-bilities using Magnetic Nano-Particles (MNPs).

Magnetic NanoMappers (MNM) is a new approach ex-ploiting the use of MNPs as contrast agents for mapping theflood front inside hydrocarbon reservoirs. This approach em-ploys EM wave traveltime tomography coupled with MNPs tomap the subsurface and so enable real-time monitoring of theinjected water in reservoirs. It can also be used as a tool to locate bypassed oil, monitor the oil-water contact, and detectsuper-K zones and fracture corridors prior to early waterbreakthrough at producing wells. The MNM research pro-gram is a multidisciplinary solution that comprises the iterativeparallel development of chemical materials (the MNPs), hard-ware including EM sources, receiver antenna arrays and dataacquisition components as well as software, including signalprocessing, forward modeling and inversion.

This article reports progress to date on the road to develop-ing the MNM program, which will be subsequently deployedin real reservoirs. In the lab, EM waves were used to success-fully map a container of high permeability MNPs buriedwithin a 2,000 liter laboratory demonstration reservoir modelof water and sand that simulated field conditions. The first arrival traveltimes of EM waves passing through the air, wetsand, water and MNPs were measured and processed to generate

The ability to map injected fluids in hydrocarbon reservoirswith high resolution is a key goal for reservoir engineering andoptimization. Saudi Aramco is developing tools and method-ologies to map the flood front, locate bypassed oil, monitor theoil-water contact, and detect super-K zones and fracture corri-dors prior to early water breakthrough at producing wells. Theuse of Magnetic NanoMappers (MNM) is a new approach ex-ploiting Magnetic Nano-Particles (MNPs) as contrast agentsfor mapping the flood front inside the hydrocarbon reservoir.This approach takes advantage of the fact that the speed ofelectromagnetic (EM) waves slows down when they passthrough magnetic media. Localizing MNPs within an injectedfluid could provide a detailed map of the fluids movements.Lab tests have recently demonstrated the capability of MNMto locate MNP volumes hidden within a 2,000 liter tank (reser-voir model) with high resolution. This article will outline theMNM concept, laboratory test bed, results and future plans.

INTRODUCTION

Tomography is a noninvasive imaging technique that allowsthe visualization of a slice or section of the internal structuresof an object by using penetrating radiation. The technique isbased on the mathematical principle of tomographic recon-struction, first developed by Johann Radon in the early 20th

century1. Traveltime tomography is widely used in geophysicalstudies to image subsurface velocity variation, mainly for seis-mic waves. It uses first arrival traveltime information from thetransmitted wave as input data to construct earth structure andvelocity models2.

Traveltime tomography measurements can be accomplishedusing different kinds of waves, such as acoustic or electromag-netic (EM) waves. The basic theory of cross-well EM tomographyhas been studied and detailed in many papers3-5. Also, severaltypes of equipment have been developed for cross-hole EM tomography6. Most of this equipment uses a low frequencycontrolled source EM (CSEM) method and system to imagesubsurface and subsea conductivity7, 8.

To conduct traveltime measurements using EM waves, a signal is launched into a medium by a source antenna and is

40 FALL 2013 SAUDI ARAMCO JOURNAL OF TECHNOLOGY

Illuminating the Reservoir: Magnetic NanoMappers

Authors: Abdullah A. Al-Shehri, Dr. Erika S. Ellis, Jesus M. Felix Servin, Dr. Dmitry V. Kosynkin, Dr. Mazen Y. Kanj and Dr. Howard K. Schmidt

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an accurate 1D image of the MNP volume within the lab scalereservoir. 3D imaging and inversion experiments using thesame test bed are currently ongoing. The next step is todemonstrate the concept in shallow wellbores in the field. Thisarticle will outline the MNM concept, experimental test bed,results and discussion.

MAGNETIC NANO-PARTICLES (MNPS)

MNPs are the enabling element in MNM technology. They areused as contrast agents due to their super paramagnetic (high) properties. Once they are injected (with the fluid) into thefracture/reservoir, they will significantly slow the propagationof EM waves between the transmitter and receiver as the wavespass through the front. A matrix of traveltimes collected overthe entire reservoir should differentiate between sand/rock, injection fluid and MNP-loaded volumes. We expect to use theresulting matrix of time delays, with inversion, to create a 3Dimage of the flood front.

The MNPs were selected because of the ease of preparingthem in large amounts, their high chemical stability in water inthe absence of oxygen and their high magnetic permeability,Figs. 1 and 2. We adapted the preparation procedure describedin Lu, et al. (2007)12, to prepare a mixture of MNPs at a con-centration of 10,000 ppm.

THE CONCEPT OF THE MNM PROGRAM

The MNM approach capitalizes on the MNPs super paramag-netic property to delay the propagation of EM waves whilepassing through the injected fluid. EM waves travel at c =3.0108 m/s in a vacuum, but they slow down substantiallywhen they pass through a medium and interact with the atoms

of the medium. This interaction denotes the permittivity and/orpermeability of the medium. Equation 1 describes the speed ofthe EM waves in a given medium.

(1)

where c is the speed of light in a vacuum, V is the speed in themedium, r is the relative magnetic permeability of themedium, and r is the relative electrical permittivity of themedium.

According to the above equation, as the EM waves passthrough the MNP concentration with high , the propagationspeed will decrease, showing an increased time delay in the received signal along the MNP front. Figure 3 illustrates thetransmitter receiver array configuration of MNM across afluid injected in the reservoir and the resulting time delay as theEM waves pass through the MNP front. A pulsed transmitteris located in a borehole to emit the EM waves. The radiatedwaves propagate through the reservoir and are detected at thereceiving array located in a parallel borehole. The first peak arrival time information (first significant received signal peakfrom the receiver array) is used to produce a matrix of travel-time vs. antenna position throughout the reservoir, which can

SAUDI ARAMCO JOURNAL OF TECHNOLOGY FALL 2013 41

Fig. 1. TEM image of MNPs.

Fig. 2. Super paramagnetic MNPs are attracted to a magnet outside the sample jar.


be processed by 3D inversion software to produce the spatialtomography maps11.

3D TOMOGRAPHY OF RESERVOIRS USING MNM

As previously mentioned inversion software is needed toprocess and interpret the time delay measurements producedfrom a MNM test and to produce tomographic maps of theflood front. A tomographic inversion method that uses first arrival traveltime information is the appropriate method to analyze MNM collected data and surveys. There are many inversion methods developed to extract the first arrival travel-time and amplitude spectra information from cross-hole radarmeasurements to reconstruct electromagnetic velocity and attenuation distribution in earth materials. These methods in-clude straight-ray tomography13, curved-ray tomography14

and traveltime tomography11. Since the goal of MNM is tomap the variations in magnetic permeability using first arrivaltraveltime information, the traveltime tomographic inversionmethod will be used.

EXPERIMENTAL TEST BED

The experimental setup used a 2,000 liter tank half filled withwet sand as a reservoir model. In addition, a PVC pipe wasplaced through the center of the tank to mimic the boreholefor the transmission source, as depicted in Fig. 4. The tank wasdivided into four quadrants, three of them containing a buriedfive gallon plastic container each (diameter of 27 cm) filled withdifferent media: air, water and MNPs. The last quadrant wasempty, containing wet sand only. For each quadrant, the totaldistance from the borehole (transmitter) to the volume sidewas 13.5 cm. The distance from the opposite side of the vol-ume to the outside wall of the tank was also 13.5 cm. There-fore, for shots directly through the volumes, the EM wavetraveled through 13.5 cm of wet sand and 27 cm of volumemedium plus an additional 13.5 cm of wet sand to the receiverantenna (a total distance of 54 cm). This is depicted in Fig. 5.

An in-house built 1 kV spark gap with 3 cm loop was usedto generate 2 GHz pulsed EM waves with a wavelength of 15cm, Figs. 6a and 6b. A single loop of 3 cm magnetic wire


antenna was attached on top of and normal to the face of thespark gap to record the magnetic portion of the transmissionwaveform. The spark gap and its antenna were fixed to amoveable sliding plate attached to a wooden slat placed downthe center of the PVC pipe (borehole) to easily change the posi-tion of the transmission source. The receiver antenna wasmade of a 3 cm single loop of magnetic wire and placed out-side the tank, positioned 90 radially from the spark gap faceto obtain the maximum far field magnetic signal. An AgilentDSO7104 oscilloscope capable of 4 GHz time capture wasused to monitor transmission and receiver waveforms. Lab-view 2010 was used to control the scope and capture wave-form data. MatLab software was used to filter and process the

Fig. 4. 2,000 liter tank (reservoir model).

Fig. 5. Top-down schematic of volume placement in tank.

Fig. 3. Schematic cross section of the transmitter receiver array configurationacross a fluid injected in the reservoir and the corresponding delay in the receivedwaveform as the EM waves pass through the MNP front.



data; it includes a first arrival peak picking routine. For eachdata set, 100 shots from the scope were captured and averagedto improve the signal to noise ratio.

The experiment was performed in two phases. The firstphase of the experiment was to determine the time delaysbased on the different media contained in the buried volumes.For these tests, the transmitter and receiver antennas werefixed on the tank with the EM waves shooting directly throughthe middle of the buried volume (i.e., the signal passingthrough both wet sand and the volume medium). This datashowed the basic differential time delays for air, sand, waterand MNPs, which inversely compared their respective materialproperties (permittivity and permeability).

The second phase of the experiment was to create a 1D

image of a single quadrant of the tank to find the buried vol-ume with respect to the vertical position of the transmitter andreceiver. In this case, the transmitter (spark gap) and receiverantenna positions were varied over 10 vertical positions downthe tank in 10 cm increments, starting with air, movingthrough the buried volume and then going below the volumethrough wet sand only. This data presented a 1D vertical im-age of the MNP volume based on the time delay differences asthe transmitter and receiver moved vertically down the tank.Figure 7 shows a schematic of the 1D imaging experiment.

RESULTS AND DISCUSSION

The first phase results show fundamental time delay differ-ences and corresponding material properties for four differentmedia: air, sand, water and MNPs. Figure 8 (top) shows thesignal from the spark gap transmitter antenna (Tx), and Fig. 8(bottom) shows the entire received signals shot from Txthrough the center of each buried volume for each of the fourtank quadrants. The red dotted line represents the beginning ofthe transmission pulse (time = 0). The first peak for each wave-form was determined by a statistical Matlab subroutine and isshown for each quadrant (medium) in Fig. 8 (bottom). Thetime at which the first peak appears in Rx is the time delay forthe transmitted EM wave to travel through 27 cm of wet sandplus 27 cm of volume medium. Note that although the entireTx and Rx waveforms are shown in Fig. 8, the area of interestis the first peak of the Rx past Tx time = 0. The rest of thewaveform is ignored for traveltime tomography.

Fig. 6a. In-house built 1 kV spark gap with 3 cm loop used as a pulsed DCtransmission source.

Fig. 6b. Actual photo of the in-house built 1 kV spark gap.

Fig. 7. Experimental schematic of 1D MNP volume imaging in the lab scalereservoir.


Table 1 shows the measured time delays (first peak fromFig. 8 waveforms) and calculated time delays for EM wavestraveling through the air, wet sand, water and MNP volumesin addition to the wet sand surrounding the volumes. The EMwaves traveled a distance of 54 cm; part of it was within thevolume medium, while the other part was in the wet sand sur-rounding the volume on both sides, as previously illustrated inFig. 5. Comparative time delay values for each medium werecalculated based on Eqns. 1 and 2 using the published valuesof and for air (1, 1.3), water (1, 80), wet sand (1, 25) andMNP (2, 80), respectively.

t = d__

V (2)

where t is traveltime, V is the speed in the medium, and d isthe distance.

The of wet sand was chosen as 25 for our calculation, frompublished values that vary from 20 to 30 depending on the typeof sand15. The of MNPs was the same as for water, 80, whilethe could not be measured due to the large paramagneticproperties of the fluid, but it was estimated to be 2.

It is noted that the delay for the air volume quadrant was

smaller than expected because the calculated time was a line-of-sight estimate neglecting multipath, refraction and air-shorteffects. Such effects are apparently non-negligible in the case ofthe air volume. In this case, the EM waves should have trav-eled through two different media (wet sand and immersed airvolume) in three stages: from the transmitter through 13.5 cmof wet sand, then through the immersed air volume, and finallythrough 13.5 cm of wet sand from the other side, Fig. 5. Sub-sequently, it seems that it traveled through the shortest path.For the other three quadrants and for the entire air medium inthe top of the tank, the measured and expected delays are ingeneral agreement. This data show that the lab scale systemcan accurately model propagation speeds of EM wavesthrough selected medium based on differences in their and .

To verify known medium permeabilities with measured timedelays, a back calculation of Eqns. 1 and 2 was used to solvefor . The calculated values are shown in Table 2.

It is obvious that the back calculated for the air volume is


Fig. 8. The Tx signal (top) and the Rx signal (bottom) for each of the four mainquadrants showing different measured arrival times for air, sand, water andMNPs.

Fig. 9. Vertical position vs. arrival time for the MNP quadrant of the tankshowing a 1D image of the MNP volume.

MediumTime

Delay (ns) Measured

Time Delay (ns) Calculated

Air only through 54 cm near top of the tank

2 2

Air volume (27 cm) plus wet sand (27 cm)

3.5 5

Wet sand (54 cm) 9.5 8-10

Water volume (27 cm) plus wet sand (27 cm)

12 12

MNP volume (27 cm) plus wet sand (27 cm)

24.5 16

Table 1. Measured vs. calculated time delays of different media in lab scale system

Medium Calculated from Measured

Time Delay

Air only 1.0

Air volume 1.1

Water volume 1.0

Wet sand 1.0

MNP volume 6.2

T Table 2. The back calculated values of from measured time delays in thereservoir model



higher than the known permeability (Eqn. 1) for the same reason as given for the difference between calculated andmeasured time delay, as was seen in Table 1.

The second phase of the experiment was to image the vol-ume of MNPs vertically through the tank, starting with the airin the empty space at the top of the tank, moving downthrough the volume and finally moving through the wet sandunderneath the volume. Figure 9 illustrates the vertical posi-tion of the transmitter and receiver vs. arrival times for theMNP quadrant of the tank. The first five stations correspondto wave propagation through air only. Stations 6 and 7 showthe time delays getting longer as the wave starts movingthrough the neck, tapering off the volume, while the largesttime delay (24.5 ns) occurs at Station 8 when the wave movesthrough the entire 27 cm diameter of the volume. Station 9 atthe interface of the bottom of the volume with the wet sand isreflected in the time delay as the wave moves partially throughthe MNPs and partially through wet sand. At Station 10, thewave travels through the wet sand only, with the same time de-lay as obtained in the first phase of the experiment at 9.5 ns.The plotted data thereby revealed a 1D image of the volume ofMNPs through the received time delays.

The success in accurately differentiating time delays with re-spect to different reservoir-like model media and the ability tocreate a 1D image of the MNPs using traveltimes demonstratethe concept of using MNPs in the injected fluids to spatiallymap the flood front inside the reservoir.

CONCLUSION

Lab tests have demonstrated the capability of using traveltimetomography to differentiate between different media in a 2,000liter tank (reservoir model). The first arrival traveltimes of EMwaves passing through air, wet sand, water and MNPs wereaccurately measured and processed to generate a 1D image ofthe container within the lab scale reservoir at good resolution.This achievement is a big step forward on the road to exhibitingthe concept in shallow wellbores in the field. The next phaseinvolves 3D vertical imaging of the tank quadrants using theMNM system and specialized bh_tomo software to automatefirst peak picking, data sequencing and inversion to create anaccurate 3D image of the lab scale reservoir. The first field testin shallow wellbores is planned for the second quarter of 2013.

ACKNOWLEDGMENTS

The authors would like to thank the management of SaudiAramco for their permission to publish this article. We wouldalso like to acknowledge the valuable assistance received fromJim J. Funk and Mohammed H. Subahi.

This article was presented at the SPE Middle East Oil andGas Show and Exhibition, Manama, Bahrain, March 10-13,2013.

REFERENCES

1. Radon, J.: On Determining Functions from Their IntegralValues along Certain Manifolds, translated by P.C. Parksin 1986, IEEE Transactions on Medical Imaging, Vol. 5,No. 4, November 12, 2007, pp. 170-176.

2. Brzostowski, M.A.: 3D Tomographic Imaging of Near-Surface Seismic Velocity and Attenuation, Geophysics,Vol. 57, No. 3, 1992, pp. 396-403.

3. Zhou, Q.: Audio-Frequency Electromagnetic Tomographyfor Reservoir Evaluation, Ph.D. thesis, Lawrence BerkeleyLaboratory, University of California, Earth SciencesDivision, October 1989, p. 175.

4. Nekut, A.G.: Electromagnetic Ray-Trace Tomography,Geophysics, Vol. 59, No. 3, March 1994, pp. 371-377.

5. Yu, L. and Edwards, R.N.: On Crosswell Diffusive Time-Domain Electromagnetic Tomography, GeophysicalJournal International, Vol. 130, No. 2, August 1997, pp.449-459.

6. Takasugi, S., Miura, Y. and Arai, E.: Conceptual Designof an Electromagnetic Tomography System, Journal ofApplied Geophysics, Vol. 35, Nos. 2-3, September 1, 1996,pp. 199-207.

7. Wilt, M., Lee, K., Alumbaugh, D., Morrison, H.F., Becker,A., Tseng, H.W. and Torres-Verdin, C.: CrossholeElectromagnetic Tomography: A New Technology for OilField Characterization, The Leading Edge, Vol. 14, No. 3,March 1995, pp. 173-177.

8. Constable, S.: Ten Years of Marine CSEM forHydrocarbon Exploration, Geophysics, Vol. 75, No. 5,2010, pp. A67-A81.

9. Zhou, C.G., Liu, L. and Lane, J.W.: Nonlinear Inversionof Borehole-Radar Tomography Data to ReconstructVelocity and Attenuation Distribution in Earth Materials,Journal of Applied Geophysics, Vol. 47, Nos. 3-4, 2001,pp. 271-284.

10. Farmani, M.B., Keers, H. and Kitterd, N.O.: Time-Lapse GPR Tomography of Unsaturated Water Flow in anIce-Contact Delta, Vadose Zone Journal, Vol. 7, No. 1, 2008, pp. 272-283.

11. Giroux, B., Gloaguen, E. and Chouteau, M.: bh_tomo a Matlab Borehole Georadar 2D Tomography Package, Computers & Geosciences, Vol. 33, No. 1, January 2007,pp. 126-137.

12. Lu, H.M., Zheng, W.T. and Jiang, Q.: Saturation Magnetization of Ferromagnetic and Ferromagnetic Nanocrystals at Room Temperature, Journal of Physics D: Applied Physics, Vol. 40, No. 2, January 21, 2007, pp.320-325.


13. Schmalholz, J., Stoffregen, H., Kemna, A. and Yaramanci,U.: Imaging of Water Content Distribution Inside a Lysimeter Using GPR Tomography, Vadose Zone Journal, Vol. 3, No. 4, November 2004, pp. 1,106-1,115.

14. Hanafy, S. and Al Hagrey, S.A.: Ground Penetrating Radar Tomography for Soil Moisture Heterogeneity, Geophysics, Vol. 71, No. 1, January 2006, pp. 9-18.

15. Martinez, A. and Byrnes, A.P.: Modeling Dielectric-Constant Values of Geologic Materials: An Aid to Ground Penetrating Radar Data Collection and Interpretation, Current Research in Earth Sciences, Bulletin 247: part 1, 2001.


BIOGRAPHIES

Abdullah A. Al-Shehri joined SaudiAramco in 2002 as a CommunicationsEngineer. He first worked with theCommunication Engineering &Technical Support Department.Abdullah undertook a number ofadvanced development projects as well

as the design and implementation of the latest technologiesrelated to satellite and wireless communications systems.

In late 2009, he moved to the Exploration andPetroleum Engineering Center Advanced ResearchCenter (EXPEC ARC) and joined the in situ sensing andintervention focus area of the Reservoir EngineeringTechnology Team. Abdullah participated in industryleading research on nanotechnology to employ the conceptof sending nano-agents (Resbots) through the reservoirto collect data for engineering functions. Also, he workedon the Magnetic NanoMappers research program in aneffort to develop new technology for tracking flood front inthe reservoir.

Abdullah received his B.S. degree from King FahdUniversity of Petroleum and Minerals (KFUPM), Dhahran,Saudi Arabia, in 2002, and his Applied Science M.S. degreefrom Concordia University, Montreal, Quebec, Canada,both in Electrical Engineering.

Dr. Erika S. Ellis is a PetroleumEngineer working in Saudi AramcosReservoir Engineering Groupresearching nano and micro electro-mechanical systems (NEMS/MEMS) tohelp illuminate oil reservoirs. Prior tojoining the company in 2013, she

spent 9 years at Argonne National Laboratory in Chicago,IL, developing thick-film gas micro-sensors for a variety ofapplications. Erika spent the last 14 years in R&D inDallas, TX, developing and characterizing new materialsand process integration schemes for MEMS applicationsfor Fortune 500 semiconductor companies.

She received her B.S. degree in Applied Physics fromLewis University, Romeoville, IL, and her M.S. degree inApplied Physics from Northern Illinois University, Dekalb,

as the design and imp

spent 9 years at Argo

IL. Erika then received her Ph.D. degree in MaterialsScience and Engineering from the University of Texas atArlington, TX.

Jesus M. Felix Servin has been workingwith Saudi Aramcos ReservoirEngineering Technology Team focusedon in situ sensing since February 2012.In this short time, he has made majorcontributions in the ongoing success ofthe Magnetic Nano-Mappers project,

including hardware design and in-house fabrication, instru-mentation, computer programming and data processing.

Jesuss interests include the development of nano-scalestrategies for reservoir illumination and electromagneticmethods for reservoir description and monitoring.

He received his B.S. degree in Engineering Physics fromInstituto Tecnologico y de Estudios Superiores de Monterrey,Monterrey, Mexico, and a M.S. degree in Chemical andBiological Engineering from King Abdullah University ofScience and Technology, Thuwal, Saudi Arabia.

Dr. Dmitry V. Kosynkin is a PetroleumEngineer in Saudi Aramcos ReservoirEngineering Technology Division.Before joining Saudi Aramco, heworked as a Research Scientist at RiceUniversity, Houston, TX, studyingsynthesis and applications of hybrid

nanomaterials.Dmitry received his M.S. degree in Chemistry from M.V.

Lomonosov Moscow State University, Moscow, Russia, in1989 and then received his Ph.D. degree in Organic Chemistryfrom the University of Houston, Houston, TX, in 1997.

Dr. Mazen Y. Kanj is a PetroleumEngineering Specialist with theReservoir Engineer Technology Teamof the Exploration and PetroleumEngineering Center AdvancedResearch Center (EXPEC ARC). He isthe focus area champion on reservoir

in situ sensing and intervention. Before joining SaudiAramco in 2003, Mazen held a Senior Scientist positionwith the Poromechanics Institute of the University ofOklahoma, Norman, OK. He was an invited member ofthe Poromechanics Committee of the American Society ofCivil Engineers and an Associate Editor for the Society ofPetroleum Engineers SPE Journal.

Mazen received his B.S. and M.S. degrees from theAmerican University of Beirut, Beirut, Lebanon, and aPh.D. degree from the University of Oklahoma, Norman,OK, all in Civil Engineering.

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Dr. Howard K. Schmidt is a PetroleumEngineering Consultant with theReservoir Engineering TechnologyTeam of the Exploration andPetroleum Engineering Center Advanced Research Center (EXPECARC). He leads the Magnetic

NanoMappers project within the In-Situ Sensing andIntervention (ISSI) focus area. Prior to joining SaudiAramco, Howard was at Rice University where he servedas Senior Research Fellow in the Chemical andBiomolecular Engineering Department and ExecutiveDirector of the Carbon Nanotechnology Laboratory. Whilethere, Howard also served as the founding SeniorNanotechnology Advisor to the Advanced EnergyConsortium (AEC).

He received his B.S. degree in Electrical Engineering in1980, and his Ph.D. degree in Chemistry in 1986, bothfrom Rice University, Houston, TX.

Howard has 50 peer-reviewed publications and a dozenissued patents.

N M j


Magnetic NanoMappers

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