PPT-Determination of Reservoir Properties from XRF Elemental Da
Author : olivia-moreira | Published Date : 2017-04-16
Justin Besplug Ron Spencer and Tom Weedmark XRF Solutions wwwxrfsolutionsca ABSTRACT Portable XRay Fluorescence XRF instruments allow a large amount of data to
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Determination of Reservoir Properties from XRF Elemental Da: Transcript
Justin Besplug Ron Spencer and Tom Weedmark XRF Solutions wwwxrfsolutionsca ABSTRACT Portable XRay Fluorescence XRF instruments allow a large amount of data to be obtained rapidly with minimal sample preparation or drilling impact and at low cost Rock powders cuttings slabs or core faces can be analysed directly using this nondestructive technique XRF analyses provide highly precise and if calibrated properly accurate data on the bulk chemistry Proprietary normative mineral algorithms are applied in order to convert the elemental chemical data to mineralogy Mineral abundances determined from the XRF analyses correlate well with those obtained by XRay Diffraction thin section point counting and SEM analyses The vast majority of the data fall within the 5 envelope expected from the precision of the XRD analyses when compared with XRF determined mineralogy Mineralogy in the Montney is variable and the most abundant minerals are calcite dolomite quartz feldspar and . Measurement . and the Implications of the Gap Between Them. Qi . Chen* . (1), Colette L. . Heald. (1), Jose L. Jimenez (2), . Manjula. R.. . Canagaratna. (3), Qi Zhang (4), Ling-Yan He (5), Xiao-Feng Huang (5), Pedro . CBRFC . Stakeholder Forum. July 31, 2012. Model Data. There are ~90 reservoirs and over 150 diversions included in our hydrologic model.. We calibrate the model to ‘natural’ flow.. Historical reservoir and diversion data is used to calculate the natural flow.. . Fundamental Questions for an Analytical Chemist. What is in the sample?. How much is there? . What is the elemental composition?. [C, O, N, F, Cl, P, H, . etc.. ?] . ELEMENTAL ANALYSIS. . Chemical Methods. HIV Cure Research Training Curriculum. Scientific Leads. : Janet Siliciano. , PhD and . Robert Siliciano, . MDPhD. ,. Johns Hopkins School of Medicine. Community Leads. : Jeffrey Taylor, CARE; . Nasra. J. Toby . Minear. and Scott Wright. California Water Science Center. United States Geological Survey. Outline. Introduction. What is reservoir sedimentation?. How does fire affect sedimentation? . ‘3W’ model for reservoir sedimentation in California. Prepared by . Bibhabasu Mohanty. Dept. of Civil Engineering. SALITER, Ahmedabad. MODULE- III . Content…. . . Types. , Investigations, Site selection, Zones of storage, Safe yield. , . Reservoir capacity. Spectrum taken using . Amptek. XR-100CR 25mm. 2. X500µm X-Ray Detector (20µs shaping time) and . Amptek. MCA8000A Multichannel Analyzer. Limitations of XRF. -. can’t differentiate between different isotopes of the same element. Data collected at GSECARS, APS 13-ID-E with ~2 x 2 micron X-ray beam at 18 keV. . 1 x 1 mm area, 2 x 2 . m. m pixels, 10 ms per pixel (~45 minute acquisition).. Using XIA . xMAP. (1 . m. s peaking time) and Xspress3 electronics.. Eduard Fast. Content. Motivation. Why EM simulation. Basics. Signal Integrity. S-Parameters. Advanced Design Systems (ADS). Why ADS?. General Simulation process. Three key EM simulation technologies. Flood Routing By Reservoir Prafulla Pokhara 2008 UCE 157 Poornima Vashishtha 2008 UCE 422 Om Prakash 2008 UCE 420 Pankaj Khatri 2008 UCE 421 HINACHI ReservoirNUNOME ReservoirMUROU ReservoirKIZURiver Integrated Reservoir Management OfficeKIZURiverIntegrated Reservoir Management OfficeSYORENJI ReservoirOutline of YODO KIZU River Basin YOD iii920091009000880092009100890087009100880086009000890088008600850084008600860087008200830088008200820083008300830083008500860084008600HouseUpperHouseReservoirClaytonReservoirHapReservoirMac FarlaneRe What is in the sample?. How much is there? . What is the elemental composition?. [C, O, N, F, Cl, P, H, . etc.. ?] . ELEMENTAL ANALYSIS. . Chemical Methods. Types of Analysis:. Qualitative Analysis:. – . 2. Introduction. Many linear inverse problems are solved using a Bayesian approach assuming Gaussian distribution of the model.. We show the analytical solution of the Bayesian linear inverse problem in the Gaussian mixture case..
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