Relevant bibliographies by topics / Well Log Analysis

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Well Log Analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 5 February 2022

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Journal articles on the topic "Well Log Analysis"

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KOK, MUSTAFA VERSAN, BAHADIR GOKCAL, and GIZEM ERSOY. "Reservoir Analysis by Well Log Data." Energy Sources 27, no. 5 (2005): 399–404. http://dx.doi.org/10.1080/00908310490441791.

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Buoro, Alvaro Bueno, and João B. C. Silva. "Ambiguity analysis of well‐log data." GEOPHYSICS 59, no. 3 (1994): 336–44. http://dx.doi.org/10.1190/1.1443595.

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Ambiguity in a geophysical interpretation problem is the possibility of accepting more than one solution caused either by solution nonuniqueness or instability. Nonuniqueness is related to the existence of more than one solution regardless of the precision of observations. On the other hand, instability is related to the acceptance of different solutions producing data fittings within the expected observational errors. We studied the ambiguity in the inversion of well‐logging data using a method based on the analysis of a finite number of acceptable solutions, which are ordered, in the solution space, according to their contributions to the overall ambiguity. The analysis of the parameter variations along these ordered solutions provides an objective way to characterize the most ambiguous parameters. Because this analysis is based on the geometry of an ambiguity region, empirically estimated by a finite number of alternative solutions, it is possible to analyze the ambiguity due not only to errors in the observations, but also to discrepancies between the interpretation model and the true geology. Moreover, the analysis can be applied even in the case of a nonlinear interpretation model. The analysis was performed with recorded data, and compared with the analysis using singular value decomposition, leading to comparable results. Following the determination of the most ambiguous parameters, a reparameterization is possible by grouping these parameters into a single parameter leading to a simpler interpretation model and, therefore, to a drastic reduction in the ambiguity.
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Nguyen, Nha Xuan. "LITHOLOGY ANALYSIS PROGRAM BASED ON WELL LOG DATA." Science and Technology Development Journal 12, no. 6 (2009): 104–10. http://dx.doi.org/10.32508/stdj.v12i6.2259.

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So far lithological analysis based on well logging data has been has been carried out manually and its results have been plotted by Excel. Analytical results depend on subjective assessment and analytical process requires more time and labor. The paper is to present an automatical program for lithological analysis of well logging data and a program establishing graph of analytical results using AutoCAD and MapInfor software.
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Ashida, Yuzuru, and Akira Itoh. "Well log analysis by use of neural network." Journal of the Japanese Association for Petroleum Technology 61, no. 4 (1996): 300–310. http://dx.doi.org/10.3720/japt.61.300.

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Zou, Ying, and Laurence R. Bentley. "Time-lapse well log analysis, fluid substitution, and AVO." Leading Edge 22, no. 6 (2003): 550–66. http://dx.doi.org/10.1190/1.1587675.

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Hayat, Umar, Aamir Ali, Ghulam Murtaza, et al. "Classification of Well Log Data Using Vanishing Component Analysis." Pure and Applied Geophysics 177, no. 6 (2019): 2719–37. http://dx.doi.org/10.1007/s00024-019-02374-2.

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Robinson, Joseph E. "Correcting well-log information for computer processing and analysis." Computers & Geosciences 12, no. 4 (1986): 493–98. http://dx.doi.org/10.1016/0098-3004(86)90064-6.

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Sari, Tri Wulan, and Sujito Sujito. "LITHOLOGY INTERPRETATION BASED ON WELL LOG DATA ANALYSIS IN “JS” FIELD." Applied Research on Civil Engineering and Environment (ARCEE) 1, no. 01 (2019): 31–37. http://dx.doi.org/10.32722/arcee.v1i01.1955.

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Reservoir lithology types in a prospect zone of hydrocarbon can be known through well log data analysis, both qualitatively and quantitatively. Lithology interpretation based on qualitatively well log data analysis, has been successfully carried out by K-1 and K-3 well log data on JS Field, West Natuna basin, Riau Islands.Main focus of the research is types of lithology indicated by response the petrophysical well data log of Lower-Middle Miocene Arang Formation. Arang Formation was deposited immediately on top Barat formation and depositional environment in this formation is transitional marine - marine. Petrophysics log shows well data are log gamma ray, resistivity, neutron porosity, density, and sonic. The limitation of study are on four types lithology, they are coal, sand, sally sand, and shale. Lithology on well K-1 dominate by shale, there is thin intersection between sand and coal. The well of K-1 have sand thickest around six meter. While on well K-3 Petrophysics log data shows thin intersection between coal, sand, shaly sand, and dominated by shale. The thickest Sand have thickness 29 meter, and thicker than on K-1 well. The result in this study, the formation dominated by types of lithology “shale”.
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Gairola, Gaurav S., and E. Chandrasekhar. "Heterogeneity analysis of geophysical well-log data using Hilbert–Huang transform." Physica A: Statistical Mechanics and its Applications 478 (July 2017): 131–42. http://dx.doi.org/10.1016/j.physa.2017.02.029.

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Hamdi, Z., M. S. Momeni, B. Meyghani, et al. "Oil well compressive strength analysis from sonic log; a case study." IOP Conference Series: Materials Science and Engineering 495 (June 7, 2019): 012077. http://dx.doi.org/10.1088/1757-899x/495/1/012077.

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Dissertations / Theses on the topic "Well Log Analysis"

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Bertl, Brooks R. "Projective well log analysis : Plummer Field, Greene County, Indiana." Virtual Press, 1992. http://liblink.bsu.edu/uhtbin/catkey/834523.

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The purpose of this investigation is to determine the effectiveness of projective well log analysis based upon data collected from Plummer Field located in Greene County, Indiana. Projective well log analysis consists of analyzing spontaneous potential (SP) logs from existing oil and gas wells in order to determine SP gradients that may be applied to locate other undiscovered hydrocarbon accumulations. Projective well log analysis was developed in 1963 by S.J. Pirson, however, the specific parameters employed in the Plummer Field investigation were developed in 1988 by Dr. R.H. Fluegeman in order to apply to the geologic conditions in southwestern Indiana.The results of this investigation indicate that SP gradients can be interpreted to determine hydrocarbon production potential in Plummer Field with a 62% to 73% certainty. Given the petroleum industry exploration success rate of 3% to 20%, it is believed that the SP gradients established in Plummer Field can be used to identify economical hydrocarbon accumulations in areas of similar geology such as other portions of the Illinois Basin and the Michigan Basin.<br>Department of Geology
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Li, Huitang. "Production log analysis and statistical error minimization." Digital version:, 2000. http://wwwlib.umi.com/cr/utexas/fullcit?p9992850.

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Pereira, Janaina Luiza Lobato. "Permeability prediction from well log data using multiple regression analysis." Morgantown, W. Va. : [West Virginia University Libraries], 2004. https://etd.wvu.edu/etd/controller.jsp?moduleName=documentdata&jsp%5FetdId=3368.

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Thesis (M.S.)--West Virginia University, 2004.<br>Title from document title page. Document formatted into pages; contains xiii, 82 p. : ill. (some col.), maps. Vita. Includes abstract. Includes bibliographical references (p. 41).
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Wong, Kok W. "A neural fuzzy approach for well log and hydrocyclone data interpretation." Curtin University of Technology, School of Electrical and Computer Engineering, 1999. http://espace.library.curtin.edu.au:80/R/?func=dbin-jump-full&object_id=10344.

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A novel data analysis approach that is automatic, self-learning and self-explained, and which provides accurate and reliable results is reported. The data analysis tool is capable of performing multivariate non-parametric regression analysis, as well as quantitative inferential analysis using predictive learning. Statistical approaches such as multiple regression or discriminant analysis are usually used to perform this kind of analysis. However, they lack universal capabilities and their success in any particular application is directly affected by the problem complexity.The approach employs the use of Artificial Neural Networks (ANNs) and Fuzzy Logic to perform the data analysis. The features of these two techniques are the means by which the developed data analysis approach has the ability to perform self-learning as well as allowing user interaction in the learning process. Further, they offer a means by which rules may be generated to assist human understanding of the learned analysis model, and so enable an analyst to include external knowledge.Two problems in the resource industry have been used to illustrate the proposed method, as these applications contain non-linearity in the data that is unknown and difficult to derive. They are well log data analysis in petroleum exploration and hydrocyclone data analysis in mineral processing. This research also explores how this proposed data analysis approach could enhance the analysis process for problems of this type.
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Rewari, Gaurav. "Parametric inversion from well log data : a performance and robustness analysis." Thesis, Massachusetts Institute of Technology, 1993. http://hdl.handle.net/1721.1/12754.

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Thesis (Elec. E.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1993.<br>Includes bibliographical references (leaves 138-139).<br>by Gaurav Rewari.<br>Elec.E.
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Fuchs, Sven. "Well-log based determination of rock thermal conductivity in the North German Basin." Phd thesis, Universität Potsdam, 2013. http://opus.kobv.de/ubp/volltexte/2013/6780/.

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In sedimentary basins, rock thermal conductivity can vary both laterally and vertically, thus altering the basin’s thermal structure locally and regionally. Knowledge of the thermal conductivity of geological formations and its spatial variations is essential, not only for quantifying basin evolution and hydrocarbon maturation processes, but also for understanding geothermal conditions in a geological setting. In conjunction with the temperature gradient, thermal conductivity represents the basic input parameter for the determination of the heat-flow density; which, in turn, is applied as a major input parameter in thermal modeling at different scales. Drill-core samples, which are necessary to determine thermal properties by laboratory measurements, are rarely available and often limited to previously explored reservoir formations. Thus, thermal conductivities of Mesozoic rocks in the North German Basin (NGB) are largely unknown. In contrast, geophysical borehole measurements are often available for the entire drilled sequence. Therefore, prediction equations to determine thermal conductivity based on well-log data are desirable. In this study rock thermal conductivity was investigated on different scales by (1) providing thermal-conductivity measurements on Mesozoic rocks, (2) evaluating and improving commonly applied mixing models which were used to estimate matrix and pore-filled rock thermal conductivities, and (3) developing new well-log based equations to predict thermal conductivity in boreholes without core control. Laboratory measurements are performed on sedimentary rock of major geothermal reservoirs in the Northeast German Basin (NEGB) (Aalenian, Rhaethian-Liassic, Stuttgart Fm., and Middle Buntsandstein). Samples are obtained from eight deep geothermal wells that approach depths of up to 2,500 m. Bulk thermal conductivities of Mesozoic sandstones range between 2.1 and 3.9 W/(m∙K), while matrix thermal conductivity ranges between 3.4 and 7.4 W/(m∙K). Local heat flow for the Stralsund location averages 76 mW/m², which is in good agreement to values reported previously for the NEGB. For the first time, in-situ bulk thermal conductivity is indirectly calculated for entire borehole profiles in the NEGB using the determined surface heat flow and measured temperature data. Average bulk thermal conductivity, derived for geological formations within the Mesozoic section, ranges between 1.5 and 3.1 W/(m∙K). The measurement of both dry- and water-saturated thermal conductivities allow further evaluation of different two-component mixing models which are often applied in geothermal calculations (e.g., arithmetic mean, geometric mean, harmonic mean, Hashin-Shtrikman mean, and effective-medium theory mean). It is found that the geometric-mean model shows the best correlation between calculated and measured bulk thermal conductivity. However, by applying new model-dependent correction, equations the quality of fit could be significantly improved and the error diffusion of each model reduced. The ‘corrected’ geometric mean provides the most satisfying results and constitutes a universally applicable model for sedimentary rocks. Furthermore, lithotype-specific and model-independent conversion equations are developed permitting a calculation of water-saturated thermal conductivity from dry-measured thermal conductivity and porosity within an error range of 5 to 10%. The limited availability of core samples and the expensive core-based laboratory measurements make it worthwhile to use petrophysical well logs to determine thermal conductivity for sedimentary rocks. The approach followed in this study is based on the detailed analyses of the relationships between thermal conductivity of rock-forming minerals, which are most abundant in sedimentary rocks, and the properties measured by standard logging tools. By using multivariate statistics separately for clastic, carbonate and evaporite rocks, the findings from these analyses allow the development of prediction equations from large artificial data sets that predict matrix thermal conductivity within an error of 4 to 11%. These equations are validated successfully on a comprehensive subsurface data set from the NGB. In comparison to the application of earlier published approaches formation-dependent developed for certain areas, the new developed equations show a significant error reduction of up to 50%. These results are used to infer rock thermal conductivity for entire borehole profiles. By inversion of corrected in-situ thermal-conductivity profiles, temperature profiles are calculated and compared to measured high-precision temperature logs. The resulting uncertainty in temperature prediction averages < 5%, which reveals the excellent temperature prediction capabilities using the presented approach. In conclusion, data and methods are provided to achieve a much more detailed parameterization of thermal models.<br>Die thermische Modellierung des geologischen Untergrundes ist ein wichtiges Werkzeug bei der Erkundung und Bewertung tiefliegender Ressourcen sedimentärer Becken (e.g., Kohlenwasserstoffe, Wärme). Die laterale und vertikale Temperaturverteilung im Untergrund wird, neben der Wärmestromdichte und der radiogenen Wärmeproduktion, hauptsächlich durch die Wärmeleitfähigkeit (WLF) der abgelagerten Gesteinsschichten bestimmt. Diese Parameter stellen die wesentlichen Eingangsgrößen für thermische Modelle dar. Die vorliegende Dissertation befasst sich mit der Bestimmung der Gesteins-WLF auf verschiedenen Skalen. Dies umfasst (1) laborative WLF-Messungen an mesozoischen Bohrkernproben, (2) die Evaluierung und Verbesserung der Prognosefähigkeit von Mischgesetzten zur Berechnung von Matrix- und Gesamt-WLF sedimentärer Gesteine, sowie (3) die Entwicklung neuer Prognosegleichungen unter Nutzung bohrlochgeophysikalischer Messungen und multivariater Analysemethoden im NGB. Im Nordostdeutschen Becken (NEGB) wurden für die wichtigsten geothermischen Reservoire des Mesozoikums (Aalen, Rhät-Lias-Komplex, Stuttgart Formation, Mittlerer Buntsandstein) Bohrkerne geothermischer Tiefbohrungen (bis 2.500 m Tiefe) auf Ihre thermischen und petrophysikalischen Eigenschaften hin untersucht. Die WLF mesozoischer Sandsteine schwankt im Mittel zwischen 2,1 und 3,9 W/(m∙K), die WLF der Gesteinsmatrix hingegen im Mittel zwischen 3,4 und 7,4 W/(m∙K). Neu berechnete Werte zur Oberflächenwärmestromdichte (e.g., 76 mW/m², Stralsund) stehen im Einklang mit den Ergebnissen früherer Studien im NEGB. Erstmals im NDB wurde für das mesozoisch/känozoischen Intervall am Standort Stralsund ein in-situ WLF-Profil berechnet. In-situ Formations-WLF, für als potentielle Modelschichten interessante, stratigraphische Intervalle, variieren im Mittel zwischen 1,5 und 3,1 W/(m∙K) und bilden eine gute Grundlage für kleinskalige (lokale) thermische Modelle. Auf Grund der in aller Regel nur eingeschränkt verfügbaren Bohrkernproben sowie des hohen laborativen Aufwandes zur Bestimmung der WLF waren alternative Methoden gesucht. Die Auswertung petrophysikalischer Bohrlochmessungen mittels mathematischer-statistischer Methoden stellt einen lang genutzten und erprobten Ansatz dar, welcher in seiner Anwendbarkeit jedoch auf die aufgeschlossenen Gesteinsbereiche (Genese, Geologie, Stratigraphie, etc.) beschränkt ist. Daher wurde ein leicht modifizierter Ansatz entwickelt. Die thermophysikalischen Eigenschaften der 15 wichtigsten gesteinsbildenden Minerale (in Sedimentgesteinen) wurden statistisch analysiert und aus variablen Mischungen dieser Basisminerale ein umfangreicher, synthetischer Datensatz generiert. Dieser wurde mittels multivariater Statistik bearbeitet, in dessen Ergebnis Regressionsgleichungen zur Prognose der Matrix-WLF für drei Gesteinsgruppen (klastisch, karbonatisch, evaporitisch) abgeleitet wurden. In einem zweiten Schritt wurden für ein Echtdatenset (laborativ gemessene WLF und Standardbohrlochmessungen) empirische Prognosegleichungen für die Berechnung der Gesamt-WLF entwickelt. Die berechneten WLF zeigen im Vergleich zu gemessenen WLF Fehler zwischen 5% und 11%. Die Anwendung neu entwickelter, sowie in der Literatur publizierter Verfahren auf den NGB-Datensatz zeigt, dass mit den neu aufgestellten Gleichungen stets der geringste Prognosefehler erreicht wird. Die Inversion neu berechneter WLF-Profile erlaubt die Ableitung synthetischer Temperaturprofile, deren Vergleich zu gemessenen Gesteinstemperaturen in einen mittleren Fehler von < 5% resultiert. Im Rahmen geothermischer Berechnungen werden zur Umrechnung zwischen Matrix- und Gesamt-WLF häufig Zwei-Komponenten-Mischmodelle genutzt (Arithmetisches Mittel, Harmonische Mittel, Geometrisches Mittel, Hashin-Shtrikman Mittel, Effektives-Medium Mittel). Ein umfangreicher Datensatz aus trocken- und gesättigt-gemessenen WLF und Porosität erlaubt die Evaluierung dieser Modelle hinsichtlich Ihrer Prognosefähigkeit. Diese variiert für die untersuchten Modelle stark (Fehler: 5 – 53%), wobei das geometrische Mittel die größte, quantitativ aber weiterhin unbefriedigende Übereinstimmungen zeigt. Die Entwicklung und Anwendung mischmodelspezifischer Korrekturgleichungen führt zu deutlich reduzierten Fehlern. Das korrigierte geometrische Mittel zeigt dabei, bei deutlich reduzierter Fehlerstreubreite, erneut die größte Übereinstimmung zwischen berechneten und gemessenen Werten und scheint ein universell anwendbares Mischmodel für sedimentäre Gesteine zu sein. Die Entwicklung modelunabhängiger, gesteinstypbezogener Konvertierungsgleichungen ermöglicht die Abschätzung der wassergesättigten Gesamt-WLF aus trocken-gemessener WLF und Porosität mit einem mittleren Fehler < 9%. Die präsentierten Daten und die neu entwickelten Methoden erlauben künftig eine detailliertere und präzisere Parametrisierung thermischer Modelle sedimentärer Becken.
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Saggaf, Muhammad M. "An integrated seismic and well log analysis for the estimation of reservoir properties." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/8852.

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Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Earth, Atmospheric, and Planetary Sciences, 2000.<br>Includes bibliographical references (p. 391-399).<br>We present an integrated approach for characterizing the reservoir and estimating its properties both at the well locations and in the inter-wen regions. Such an approach can be an invaluable tool for attaining a detailed, consistent, and complete characterization of the reservoir, as not only does it incorporate all major sources of information that shape our understanding of the reservoir, including core descriptions, well Jogs, seismic data, and a priori knowledge of the geological setting of the region, but also it develops means for utilizing these sources of information in a unified manner that gives rise to a coherent framework for relating these sources of information to yield an integrated reservoir model. We analyze the different components of this approach, develop methodologies for improving the prediction accuracy of each, and link the mechanisms across these components to achieve an accurate and consistent characterization of the reservoir. The issues we tackle in this thesis can be broadly divided into four categories: enhancement of the seismic resolution, estimation of the reservoir properties at the well locations, characterizing the reservoir in the inter-well regions, and pre-processing the data to remedy any incompleteness or inconsistency.<br>by Muhammad M. Saggaf.<br>Ph.D.
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Butterfield, Andrei. "Characterization of a Utica Shale Reflector Package Using Well Log Data and Amplitude Variation with Offset Analysis." Wright State University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=wright1401462908.

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Mohshin, Mohammad. "Analysis of Well Log Data and a 2D Seismic Reflection Survey in the vicinity of London, Ohio." Wright State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=wright1502304040084834.

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DePriest, Keegan. "PETROPHYSICAL ANALYSIS OF WELLS IN THE ARIKAREE CREEK FIELD, COLORADO TO DEVELOP A PREDICTIVE MODEL FOR HIGH PRODUCTION." OpenSIUC, 2019. https://opensiuc.lib.siu.edu/theses/2609.

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All the oil and gas wells producing in the Arikaree Creek Field, Colorado targeted the Spergen Formation along similar structures within a wrench fault system; however, the wells have vastly different production values. This thesis develops a predictive model for high production in the field while also accounting for a failed waterflood event that was initiated in 2016. Petrophysical analysis of thirteen wells show that high producing wells share common characteristics of pay zone location, lithology, porosity and permeability with one another and that the Spergen Formation is not homogenous. Highly productive wells have pay zones in the lower part of the formation in sections that are dolomitized, and have anonymously high water saturation. This is likely related to the paragenesis of the formation that dolomitized the lower parts of the formation, increasing porosity and permeability, but leaving the pay zones with the high water saturation values. This heterogeneity likely accounts for the failed waterflood. Results show that the important petrophysical components for highly productive wells are the location of the payzone within the reservoir, porosity, permeability and water saturation. Additionally, homogeneity is crucial for successful waterflooding, which was not present.
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Books on the topic "Well Log Analysis"

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McCoy, R. L. Applied well log analysis. Gulf Pub. Co., Book Division, 1985.

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Crain, E. R. The log analysis handbook. Edited by Ganz Christl I. PennWell, 1986.

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Geologic log analysis using computer methods. American Association of Petroleum Geologists, 1994.

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Image processing in well log analysis. International Human Resources Development Corp., 1986.

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Kerzner, Mark G. Image Processing in Well Log Analysis. Springer Netherlands, 1986. http://dx.doi.org/10.1007/978-94-009-4670-5.

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Applied open-hole log analysis. Gulf Pub. Co., Book Division, 1986.

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Open-hole log analysis and formation evaluation. International Human Resources Development Corp., 1985.

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Log analysis of subsurface geology: Concepts and computer methods. Wiley, 1986.

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Cased-hole log analysis and reservoir performance monitoring. International Human Resources Development Corp., 1985.

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Paillet, F. L. Geophysical well log analysis of fractured granitic rocks at Atikokan, Ontario, Canada. Dept. of the Interior, U.S. Geological Survey, 1987.

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Book chapters on the topic "Well Log Analysis"

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Sinha, Mihir K., and Larry R. Padgett. "Conventional Well Log Analysis." In Reservoir Engineering Techniques Using Fortran. Springer Netherlands, 1985. http://dx.doi.org/10.1007/978-94-009-5293-5_3.

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Kerzner, Mark G. "Blocking: Curve Shape Analysis." In Image Processing in Well Log Analysis. Springer Netherlands, 1986. http://dx.doi.org/10.1007/978-94-009-4670-5_8.

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Fedi, Maurizio, Donato Fiore, and Mauro La Manna. "Regularity Analysis Applied to Well Log Data." In Fractal Behaviour of the Earth System. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/3-540-26536-8_4.

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Kerzner, Mark G. "Coloring a Computed Dipmeter Log: Feature Identification." In Image Processing in Well Log Analysis. Springer Netherlands, 1986. http://dx.doi.org/10.1007/978-94-009-4670-5_7.

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Bateman, Richard M. "Well and Field Monitoring." In Cased-Hole Log Analysis and Reservoir Performance Monitoring. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-2068-6_14.

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Kerzner, Mark G. "An Overview." In Image Processing in Well Log Analysis. Springer Netherlands, 1986. http://dx.doi.org/10.1007/978-94-009-4670-5_1.

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Kerzner, Mark G. "An Algorithm to Implement the Minimization Principle." In Image Processing in Well Log Analysis. Springer Netherlands, 1986. http://dx.doi.org/10.1007/978-94-009-4670-5_10.

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Kerzner, Mark G. "An Algorithm to Implement the Modified Minimization Principle." In Image Processing in Well Log Analysis. Springer Netherlands, 1986. http://dx.doi.org/10.1007/978-94-009-4670-5_11.

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Kerzner, Mark G. "Forms of Activity Function." In Image Processing in Well Log Analysis. Springer Netherlands, 1986. http://dx.doi.org/10.1007/978-94-009-4670-5_12.

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Kerzner, Mark G. "The Minimization Principle." In Image Processing in Well Log Analysis. Springer Netherlands, 1986. http://dx.doi.org/10.1007/978-94-009-4670-5_2.

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Conference papers on the topic "Well Log Analysis"

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Heemink, G. H., W. T. Bryant, and T. L. Langenbuch. "Well Log Analysis Spreadsheet." In Petroleum Industry Application of Microcomputers. Society of Petroleum Engineers, 1987. http://dx.doi.org/10.2118/16498-ms.

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Richard, Vincent, and Jean Brac. "Wavelet analysis using well log information." In SEG Technical Program Expanded Abstracts 1988. Society of Exploration Geophysicists, 1988. http://dx.doi.org/10.1190/1.1892398.

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Fiordelisi, A., R. Berto, F. Brambilla, and M. Casini. "Advanced Well-Log Analysis in Geothermal Wells for Fracture Identification." In 64th EAGE Conference & Exhibition. European Association of Geoscientists & Engineers, 2002. http://dx.doi.org/10.3997/2214-4609-pdb.5.c038.

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Moghadasi, Leili, Ehsan Ranaee, Fabio Inzoli, and Alberto Guadagnini. "Petrophysical Well Log Analysis through Intelligent Methods." In SPE Bergen One Day Seminar. Society of Petroleum Engineers, 2017. http://dx.doi.org/10.2118/185922-ms.

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Aadireddy, Prabhakar, and George Coates. "A Real-Time Well Site Log Analysis Application Using MRI Logs." In SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers, 2000. http://dx.doi.org/10.2118/63264-ms.

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Alimohammadi, Hamzeh, Saman Mahmoudi, and Shengnan Chen. "Single and Multi-Well Synthetic Well Log Generation using Multivariate Analysis." In Abu Dhabi International Petroleum Exhibition & Conference. Society of Petroleum Engineers, 2020. http://dx.doi.org/10.2118/203283-ms.

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Avseth, P., A. -J. Van Wijngaarden, and N. Skjei. "Best practise for well log rock physics analysis." In 65th EAGE Conference & Exhibition. European Association of Geoscientists & Engineers, 2003. http://dx.doi.org/10.3997/2214-4609.201405691.

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Bergman, Doron L., Mark J. Henning, Pallav Sarma, and Ian Hunt. "Applying Statistical Learning to Quantitative Well Log Analysis." In SPE Reservoir Characterisation and Simulation Conference and Exhibition. Society of Petroleum Engineers, 2017. http://dx.doi.org/10.2118/186044-ms.

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Zou, Ying, and Laurence R. Bentley. "Time‐lapse well log analysis, fluid substitution and AVO." In SEG Technical Program Expanded Abstracts 2002. Society of Exploration Geophysicists, 2002. http://dx.doi.org/10.1190/1.1817011.

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Hayatdavoudi, A. "Well Completion in Directional Wells with Emphasis on Log Analysis, Petrophysics, and Core Mineral Analysis." In SPE Western Regional/AAPG Pacific Section Joint Meeting. Society of Petroleum Engineers, 2003. http://dx.doi.org/10.2118/83476-ms.

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Reports on the topic "Well Log Analysis"

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Watney, W. L., J. H. Doveton, and W. J. Guy. Development and demonstration of an enhanced spreadsheet-based well log analysis software. Final report, May 1998. Office of Scientific and Technical Information (OSTI), 1998. http://dx.doi.org/10.2172/296691.

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Collett, T. S., and M. W. Lee. Electrical-resistivity well-log analysis of gas hydrate saturations in the JAPEX/JNOC/GSC et al. Mallik 5L-38 gas hydrate production research well. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2005. http://dx.doi.org/10.4095/220858.

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Wang, L., D. S. Edwards, A. Bailey, et al. Well log data analysis and interpretation on the pre-Carboniferous succession in Waukarlycarly 1, Canning Basin, Western Australia. Geoscience Australia, 2021. http://dx.doi.org/10.11636/record.2021.003.

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Mwenifumbo, C. J., and A. L. Mwenifumbo. Analysis of Well Logs from the Nechako Basin. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2010. http://dx.doi.org/10.4095/285399.

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MYERS, D. A. Analysis and Summary of Historical Dry Well Gamma Logs for S Tank Farm 200 West. Office of Scientific and Technical Information (OSTI), 1999. http://dx.doi.org/10.2172/798651.

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SYDNOR, H. A. Analysis and Summary Report of Historical Dry Well Gamma Logs for the 241-B Tank Farm 200 East. Office of Scientific and Technical Information (OSTI), 2000. http://dx.doi.org/10.2172/803932.

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SYDNOR, H. A. Analysis and Summary Report of Historical Dry Well Gamma Logs for the 241-T Tank Farm 200 West. Office of Scientific and Technical Information (OSTI), 2000. http://dx.doi.org/10.2172/803944.

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Saldanha, Ian J., Wangnan Cao, Justin M. Broyles, et al. Breast Reconstruction After Mastectomy: A Systematic Review and Meta-Analysis. Agency for Healthcare Research and Quality (AHRQ), 2021. http://dx.doi.org/10.23970/ahrqepccer245.

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Objectives. This systematic review evaluates breast reconstruction options for women after mastectomy for breast cancer (or breast cancer prophylaxis). We addressed six Key Questions (KQs): (1) implant-based reconstruction (IBR) versus autologous reconstruction (AR), (2) timing of IBR and AR in relation to chemotherapy and radiation therapy, (3) comparisons of implant materials, (4) comparisons of anatomic planes for IBR, (5) use versus nonuse of human acellular dermal matrices (ADMs) during IBR, and (6) comparisons of AR flap types. Data sources and review methods. We searched Medline®, Embase®, Cochrane CENTRAL, CINAHL®, and ClinicalTrials.gov from inception to March 23, 2021, to identify comparative and single group studies. We extracted study data into the Systematic Review Data Repository Plus (SRDR+). We assessed the risk of bias and evaluated the strength of evidence (SoE) using standard methods. The protocol was registered in PROSPERO (registration number CRD42020193183). Results. We found 8 randomized controlled trials, 83 nonrandomized comparative studies, and 69 single group studies. Risk of bias was moderate to high for most studies. KQ1: Compared with IBR, AR is probably associated with clinically better patient satisfaction with breasts and sexual well-being but comparable general quality of life and psychosocial well-being (moderate SoE, all outcomes). AR probably poses a greater risk of deep vein thrombosis or pulmonary embolism (moderate SoE), but IBR probably poses a greater risk of reconstructive failure in the long term (1.5 to 4 years) (moderate SoE) and may pose a greater risk of breast seroma (low SoE). KQ 2: Conducting IBR either before or after radiation therapy may result in comparable physical well-being, psychosocial well-being, sexual well-being, and patient satisfaction with breasts (all low SoE), and probably results in comparable risks of implant failure/loss or need for explant surgery (moderate SoE). We found no evidence addressing timing of IBR or AR in relation to chemotherapy or timing of AR in relation to radiation therapy. KQ 3: Silicone and saline implants may result in clinically comparable patient satisfaction with breasts (low SoE). There is insufficient evidence regarding double lumen implants. KQ 4: Whether the implant is placed in the prepectoral or total submuscular plane may not be associated with risk of infections that are not explicitly implant related (low SoE). There is insufficient evidence addressing the comparisons between prepectoral and partial submuscular and between partial and total submuscular planes. KQ 5: The evidence is inconsistent regarding whether human ADM use during IBR impacts physical well-being, psychosocial well-being, or satisfaction with breasts. However, ADM use probably increases the risk of implant failure/loss or need for explant surgery (moderate SoE) and may increase the risk of infections not explicitly implant related (low SoE). Whether or not ADM is used probably is associated with comparable risks of seroma and unplanned repeat surgeries for revision (moderate SoE for both), and possibly necrosis (low SoE). KQ 6: AR with either transverse rectus abdominis (TRAM) or deep inferior epigastric perforator (DIEP) flaps may result in comparable patient satisfaction with breasts (low SoE), but TRAM flaps probably increase the risk of harms to the area of flap harvest (moderate SoE). AR with either DIEP or latissimus dorsi flaps may result in comparable patient satisfaction with breasts (low SoE), but there is insufficient evidence regarding thromboembolic events and no evidence regarding other surgical complications. Conclusion. Evidence regarding surgical breast reconstruction options is largely insufficient or of only low or moderate SoE. New high-quality research is needed, especially for timing of IBR and AR in relation to chemotherapy and radiation therapy, for comparisons of implant materials, and for comparisons of anatomic planes of implant placement.
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Reiter, Claudia, and Sonja Spitzer. Well-being in Europe: decompositions by country and gender for the population aged 50+. Verlag der Österreichischen Akademie der Wissenschaften, 2021. http://dx.doi.org/10.1553/populationyearbook2021.res4.1.

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The well-being of older Europeans is of increasing importance given the substantialageing of the population. This paper comprehensively analyses well-being forthe population aged 50+ in 26 European countries, using the newly proposedindicator “Years of Good Life” (YoGL), which measures the remaining yearsof life that an individual can expect to live in a “good” state. The indicatorenables the decomposition of well-being into various dimensions, thereby revealingimportant heterogeneities between regions and genders. Results show that numbersof YoGL at age 50 vary considerably between European countries. They are highestin Northern and Western European countries and lowest in Central and EasternEuropean countries, where many “good” years are lost due to low life satisfaction.Interestingly, the high life expectancy levels in Southern Europe do not translate intohigher numbers of YoGL, mainly due to the low levels of physical and cognitivehealth in this region. While women and men can expect to have similar numbersof YoGL, women are likely to spend a smaller proportion of their longer remaininglifetime in a good state. These results demonstrate the importance of using wellbeingindicators that consider population heterogeneity when measuring humanwell-being, especially for older populations.
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Plona, T. J., and M. R. Kane. Anisotropic stress analysis from downhole acoustic logs in the JAPEX/JNOC/GSC et al. Mallik 5L-38 gas hydrate production research well. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2005. http://dx.doi.org/10.4095/220862.

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