Computing Thermal Neutron Decay Time Environmental Effects Using Monte Carlo Techniques

1986 ◽  
Vol 1 (01) ◽  
pp. 35-42 ◽  
Author(s):  
W.E. Preeg ◽  
H.D. Scott
Keyword(s):  
Monte Carlo ◽  
Thermal Neutron ◽  
Environmental Effects ◽  
Decay Time ◽  
Neutron Decay ◽  
Monte Carlo Techniques

The Thermal Neutron Decay Time Log

1970 ◽  
Vol 10 (04) ◽  
pp. 365-379 ◽  
Author(s):  
J.S. Wahl ◽  
W.B. Nelligan ◽  
A.H. Frentrop ◽  
C.W. Johnstone ◽  
R.J. Schwartz
Keyword(s):  
Thermal Neutron ◽  
Time Constant ◽  
Decay Time ◽  
High Energy ◽  
Decay Time Constant ◽  
Neutron Decay ◽  
Fast Neutrons ◽  
Neutron Density ◽  
Thermal Neutrons ◽  
Open Hole

Abstract Thermal Neutron Decay Time (TDT) logging tools in 3-3/8 and 1-11/16-in. diameters have been developed for detection and evaluation of water saturation in cased holes. These tools utilize a system of movable and expandable detection time-gates which are automatically adjusted as the log is being run. The two principal detection gates are positioned in time after the neutron burst according to an optimization criterion. An additional gate, delayed until most of the decay has taken place, permits correction for background. This place, permits correction for background. This Scale Factor gating method provides, in each bed, a thermal-decay-time measurement of maximum statistical precision consistent with removal of borehole effects present in the early part of the decay period Increased reliability is afforded by use of digital techniques. Thermal neutron decay time tools employ capture-gamma-ray detection. This choice was based on an extensive series of experiments made to compare gamma-ray detection and direct detection of thermal neutrons. Measurements of thermal neutron decay time constant are affected by local changes in neutron density in the vicinity of the sonde, caused by flow of neutrons by diffusion from one medium to another. The measured decay time constant (T meas) of neutron density at any point may differ, therefore, from the intrinsic decay time constant (T int) produced by absorption alone. The basic physics of neutron diffusion and absorption is reviewed. When the borehole and the formation have different decay time constants and diffusion coefficients, diffusion couples the two regions. Consideration of such effects sheds light on the conditions required for reduction of borehole effects on measured values of the decay time constant. The choice of source-detector spacing is affected. and, for accurate quantitative interpretation, departure curves are required. Departure curves are presented showing the effects of varying cement thickness, casing diameter. and casing fluids Illustrative log examples are shown. Introduction The Thermal Neutron Decay Time (TDT) log provides a determination of the time constant for provides a determination of the time constant for the decay of thermal neutrons in the formation. Hence, it reflects primarily the neutron absorptive properties of the formation. These properties are properties of the formation. These properties are useful in formation evaluation. The most important area of application is in logging cased hole. Because chlorine is by far the strongest thermal neutron absorber of the common earth elements, the TDT log responds largely to the amount of NaCl present in the formation water. As a result, this present in the formation water. As a result, this log resembles the usual open-hole resistivity logs and is easily correlatable with them. When information on lithology and porosity is known or is provided by open-hole logs, a log of neutron provided by open-hole logs, a log of neutron absorption properties permits the solution of a wide variety of problems: saturation determination, oil-water contact location, detection of gas behind casing, etc. Measurements of the thermal neutron decay time constant are made by first irradiating the formation with a pulse of high-energy neutrons from a neutron generator in the sonde, and then, a short time after the neutron source is turned off, determining the rate at which the thermal neutron population decreases. After each neutron burst, the high-energy neutrons are quickly slowed down to thermal velocities by successive collisions with the nuclei of elements in the formation and borehole. The relative number of thermal neutrons remaining in the formation is measured during detection intervals which follow each burst. Between each burst and the beginning of the first detection interval is a delay time which permits the originally fast neutrons to reach thermal permits the originally fast neutrons to reach thermal energy and allows "early" borehole effects to subside. SPEJ p. 365

Die Reabsorption in Anthracenkristallen im Temperaturbereich 4 < T < 300 K

1978 ◽  
Vol 33 (6) ◽  
pp. 672-683
Author(s):  
S. Ganz
Keyword(s):  
Experimental Data ◽  
Monte Carlo ◽  
Computer Program ◽  
Decay Time ◽  
Fluorescence Spectra ◽  
Wave Length ◽  
Monte Carlo Techniques

Abstract Fluorescence spectra and decay curves of pure anthracene crystals in dependence of the emission wave length have been investigated using different exciting frequencies. A computer program using Monte Carlo Techniques is described which simulates absorption, emission, reflection and reabsorption of photons in a crystal. This program allows the calculation of fluorescence spectra and decay curves. By comparing the results of this program with experimental data, it is possible to correct the decay time for reabsorption and to calculate the reabsorption coefficient in dependence of temperature.

Accurate Thermal-Neutron Decay Time Measurements Using the Far Detector of the Dual-Spacing TDT A Laboratory Study

1979 ◽  
Vol 19 (01) ◽  
pp. 59-66 ◽  
Author(s):  
William B. Nelligan ◽  
Stephen Antkiw
Keyword(s):  
Thermal Neutron ◽  
Cross Section ◽  
Decay Time ◽  
Neutron Capture ◽  
Water Saturation ◽  
Systematic Errors ◽  
Neutron Decay ◽  
Thermal Neutron Capture ◽  
Capture Cross ◽  
Laboratory Measurements

Abstract To improve the accuracy of saturation-change determinations in reservoirs, thermal-neutron-decay time was measured in the laboratory with a long-spacing TDT (trade mark) device. The far detector of a TDT-K sonde was used in 17.1 and 30.4% porosity sandstone formations for several formation-fluid salinities ranging from 0 to 247,000 ppm NaCl. A 7-in. (17.8-cm) casing cemented in a 10-in. (25.4-cm) borehole was used with and without a 2 7/8-in. (7.3-cm) tubing centered in a gravel pack. Complete decay curves are constructed from measurements made in successive channels of a multichannel analyzer. Values of formation intrinsic decay time calculated from nuclear-capture crosssections are compared with decay times measured with the far detector using the Scale Factor TM gating system. Results show That far-detector measurements are less influenced by diffusion and indicate the usefulness of the large source-detector spacing for determination of changes in water saturation () from logs run at different times in the history of a producing well. Although errors in values calculated producing well. Although errors in values calculated for are reduced by using values measured with the far detector, oar analysis shows that further improvement in accuracy can be obtained by using the correction data derived from the laboratory measurements. Introduction In the TDT time-lapse technique, changes in water saturation, Sw, are determined from the corresponding changes in the measured formation thermal-neutron-capture cross-section. The relationship used to find the saturation change, in an oil-bearing formation is ....................(1) where is the macroscopic thermal-neutron-capture cross-section of the formation at the time of a first measurement, is the cross-section at the time of a subsequent measurement, is the formation porosity, and and are the macroscopic porosity, and and are the macroscopic thermal-neutron-capture cross-sections of formation water and oil, respectively. It is clear from Eq. 1 that it is not necessary to know, the cross-section of the rock matrix. Furthermore, the accuracy of is not affected by systematic errors in the measured values of, if these errors are the same for the two measurements, and . When the errors in and are not equal, however, the effect on the result for from Eq. 1 may be significant. Therefore, for use in secondary- and tertiary-recovery programs, it is of interest to look for ways of improving accuracy available in the present state of the art. One possibility is to use a longer source-detector spacing for the TDT sonde. A longer spacing (632 mm) is available by using the far detector of the TDT-K tool. however, in most cases in practice, it is the near-detector recording that is shown for the curve on the log. This is because the higher counting rate available at this detector (spacing of 343 mm) is needed to obtain an acceptable statistical validity with a single pass in the well. The measurements with the longer spacing are useful when statistical uncertainty is reduced by recording slowly or by conducting several runs and averaging them. To investigate performance with the longer spacing, laboratory measurements determined the changes in systematic error caused by changes in the of the formation fluid when all other parameters were constant. We found that parameters were constant. We found that measurements with the far detector of the TDT-K sonde had smaller systematic errors that were less sensitive to changes in the formation fluid than those from the near detector. SPEJ P. 59

Bayesian Estimation of DSGE Models

2015 ◽  
Author(s):  
Edward P. Herbst ◽  
Frank Schorfheide
Keyword(s):  
Monte Carlo ◽  
Sequential Monte Carlo ◽  
Likelihood Function ◽  
Academic Research ◽  
Dynamic Stochastic General Equilibrium ◽  
Computational Techniques ◽  
Dsge Models ◽  
Monte Carlo Techniques ◽  
Dynamic Stochastic ◽  
Theoretical Foundations

Dynamic stochastic general equilibrium (DSGE) models have become one of the workhorses of modern macroeconomics and are extensively used for academic research as well as forecasting and policy analysis at central banks. This book introduces readers to state-of-the-art computational techniques used in the Bayesian analysis of DSGE models. The book covers Markov chain Monte Carlo techniques for linearized DSGE models, novel sequential Monte Carlo methods that can be used for parameter inference, and the estimation of nonlinear DSGE models based on particle filter approximations of the likelihood function. The theoretical foundations of the algorithms are discussed in depth, and detailed empirical applications and numerical illustrations are provided. The book also gives invaluable advice on how to tailor these algorithms to specific applications and assess the accuracy and reliability of the computations. The book is essential reading for graduate students, academic researchers, and practitioners at policy institutions.

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