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Journal articles on the topic 'Well logging'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 June 2025

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1

Hurst, Andrew. "Geological well logging." Marine and Petroleum Geology 5, no. 1 (1988): 85. http://dx.doi.org/10.1016/0264-8172(88)90046-3.

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2

Kleinberg, Robert L. "Well logging overview." Concepts in Magnetic Resonance 13, no. 6 (2001): 342–43. http://dx.doi.org/10.1002/cmr.1019.

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3

Birchak, James R. "Acoustic attenuator, well logging apparatus and method of well logging." Journal of the Acoustical Society of America 100, no. 4 (1996): 1940. http://dx.doi.org/10.1121/1.417877.

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4

Worthington, Paul F. "Encyclopedia of well logging." Marine and Petroleum Geology 5, no. 1 (1988): 83. http://dx.doi.org/10.1016/0264-8172(88)90043-8.

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5

Herron, Michael M. "Mineralogy from Geochemical Well Logging." Clays and Clay Minerals 34, no. 2 (1986): 204–13. http://dx.doi.org/10.1346/ccmn.1986.0340211.

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6

Melcher, C. L. "Scintillators for well logging applications." Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 40-41 (April 1989): 1214–18. http://dx.doi.org/10.1016/0168-583x(89)90622-8.

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7

Felder, R. D. "Advances in Openhole Well Logging." Journal of Petroleum Technology 46, no. 08 (1994): 693–701. http://dx.doi.org/10.2118/27918-pa.

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8

Donaldson, E. C. "Well logging for earth scientists." Journal of Petroleum Science and Engineering 2, no. 4 (1989): 389. http://dx.doi.org/10.1016/0920-4105(89)90013-2.

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9

Kleinberg, Robert L. "NMR well logging at Schlumberger." Concepts in Magnetic Resonance 13, no. 6 (2001): 396–403. http://dx.doi.org/10.1002/cmr.1026.

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10

Dmitrenko, E. V., Yu I. Zakharchenko, and S. P. Shuray. "Prevention of the accidents based on risk analysis during well logging." Okhrana truda i tekhnika bezopasnosti na promyshlennykh predpriyatiyakh (Labor protection and safety procedure at the industrial enterprises), no. 3 (2021): 51–55. http://dx.doi.org/10.33920/pro-4-2103-07.

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Conduction of well logging is associated with the possibility of occurrence of various emergency situations. The most probable are the sticking of well-logging tools and logging cable rupture. Such events can trigger the development of emergencies. Therefore, ensuring safety during well logging is an actual and practically important task. The authors analyzed the risk of well logging activities using the method of event tree analysis. It has been found that the main causes of sticking are rapid descent of the logging probe and casing string displacement. The rapid descent of logging probes is associated with incorrect actions of the operator and damage to the well logging winch. The logging probe breakage (rupture) is associated with mechanical damage to the cable and poor connection of the probe to the cable. The authors propose a set of specific recommendations to reduce the risk of accidents and to improve the coherence of the work carried by drilling personnel and the well logging laboratory, to timely detect defects in the logging cable and to rapidly remove well-logging tools in the event of an accident.
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11

Zhiyou, Liu, Huo Jinghuan, and Zhu Xiaogang. "The application of digital logging in exploration of Jiang Cang coalfield in Qinghai province." International Journal of Geology 1, no. 1 (2016): 44. http://dx.doi.org/10.26789/ijg.2016.006.

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A lot of well logging on to Jiang Cang coalfield in the past work and achieved good results, but due to differ in age, instrumentation equipment and method were different. In order to understand the application effect of the equipment used in the area now and in order to provide the basis for the future working to choose the appropriate logging parameters and well logging interpretation, we chosed GJS - 1 b intelligent engineering logging system produced by Chongqing geological instrument factory in Jiang Cang mining area, we collected the data when cable were ascending, according to 《the requirements of the rules of coalfield geophysical logging》, JGS - 1 b instrument full hole resistivity logging velocity of 3 m/s, the sampling interval was 5 cm, acoustic logging measurement parameter for the acoustic time, adopt the device of single-emission and double-receiving, detectorsource space was 0.5 meters, 0.7 meters, the full hole velocity was 6 m/s, the sampling interval was 10 cm.In this paper, we introduce the application of multiple well logging method in coalfield geology through to the logging examples in Mu Li Jang Cang coalfield four field of qinghai province, the effect was remarkable
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12

Qianxiao, Su, Qiao Dexin, Ren Yili, Feng Zhou, Lin Shenglan, and Huang Ruiqi. "Image Restoration of Electrical Well Logging Based on Fourier Convolution." Scientific Insights and Discoveries Review 5 (October 14, 2024): 198–207. http://dx.doi.org/10.59782/sidr.v5i1.158.

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Imaging logging is an important technical means in logging evaluation of complex reservoirs. Through imaging logging, a two-dimensional image of the resistivity distribution around the well can be obtained, which can be used to evaluate the development of well wall fractures and caves and the formation sedimentary structure. However, due to the characteristics of resistivity imaging logging instruments, blank strips will appear on the resistivity logging image, which increases the difficulty of computer processing of electrical imaging data. The current image repair method and the existing neural network image repair method are not effective enough when the part to be filled is large. Therefore, there is an urgent need for an intelligent repair method based on deep learning. Based on a fast Fourier convolution-based imaging logging image blank strip filling network, this paper constructs the electrical imaging logging images of the southwest oil and gas field as a data set, and trains a new deep learning algorithm for intelligent filling of blank strips in imaging logging images based on fast Fourier convolution. The time of various algorithms is compared. The results show that the algorithm has a better repair effect in imaging logging images with large strip widths, and the repair efficiency is significantly improved. This method can realize the rapid, accurate and intelligent repair of blank strips in imaging logging, achieve the rapid generation of full-borehole images, and solve the difficulties in obtaining full-borehole images.
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13

Wu, Lei, Zhenzhen Dong, Weirong Li, Cheng Jing, and Bochao Qu. "Well-Logging Prediction Based on Hybrid Neural Network Model." Energies 14, no. 24 (2021): 8583. http://dx.doi.org/10.3390/en14248583.

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Well-logging is an important formation characterization and resource evaluation method in oil and gas exploration and development. However, there has been a shortage of well-logging data because Well-logging can only be measured by expensive and time-consuming field tests. In this study, we aimed to find effective machine learning techniques for well-logging data prediction, considering the temporal and spatial characteristics of well-logging data. To achieve this goal, the convolutional neural network (CNN) and the long short-term memory (LSTM) neural networks were combined to extract the spatial and temporal features of well-logging data, and the particle swarm optimization (PSO) algorithm was used to determine hyperparameters of the optimal CNN-LSTM architecture to predict logging curves in this study. We applied the proposed CNN-LSTM-PSO model, along with support vector regression, gradient-boosting regression, CNN-PSO, and LSTM-PSO models, to forecast photoelectric effect (PE) logs from other logs of the target well, and from logs of adjacent wells. Among the applied algorithms, the proposed CNN-LSTM-PSO model generated the best prediction of PE logs because it fully considers the spatio-temporal information of other well-logging curves. The prediction accuracy of the PE log using logs of the adjacent wells was not as good as that using the other well-logging data of the target well itself, due to geological uncertainties between the target well and adjacent wells. The results also show that the prediction accuracy of the models can be significantly improved with the PSO algorithm. The proposed CNN-LSTM-PSO model was found to enable reliable and efficient Well-logging prediction for existing and new drilled wells; further, as the reservoir complexity increases, the proxy model should be able to reduce the optimization time dramatically.
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14

Yang, Li Ping, and Wei Wang. "The Data Manipulation Platform of Well-Logging Information Based on Agent." Applied Mechanics and Materials 397-400 (September 2013): 2347–50. http://dx.doi.org/10.4028/www.scientific.net/amm.397-400.2347.

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According to the distribution and diversity of logging information, well-logging information data manipulation platform is established by Agent software technology, in a highly flexible, robust and unified information management environment, provide support for comprehensive well-logging information distributed storage and reuse. Examples of well-logging information reuse, verified the feasibility of application mode.
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15

Liu, Jun Feng, and Hai Min Guo. "Experimental Study of Well Deviation Correction Model in Deviated and Horizontal Wells Production Profile." Advanced Materials Research 490-495 (March 2012): 2205–9. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.2205.

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There are big difference of fluid flow patterns between horizontal wells and vertical wells, so the current interpretation models of production logging multiphase flow in vertical wells are not suitable for data interpretation in highly deviated and horizontal wells. In this paper, firstly, the two-phase flow (oil-water and gas-water) simulation experiments have been carried out in large-diameter (0.124 meter internal diameter) uphill, horizontal and downhill Plexiglas pipe with practical production logging tools. Secondly, based on the conclusions of fluid flow mechanism from experimental data analysis, and considering the affecting factors (i.e. Logging tool and well deviation ), we have obtained slip velocity model after well deviation correction in highly deviated and horizontal wells, which have been corrected by the mature interpretation models. Finally, this proposed method has been proved correct and feasible through the experimental data validation.
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16

Liu, Li, Guo Sheng Jiang, Fu Long Ning, Yi Bing Yu, Ling Zhang, and Yun Zhong Tu. "Well Logging in Gas Hydrate-Bearing Sediment: A Review." Advanced Materials Research 524-527 (May 2012): 1660–70. http://dx.doi.org/10.4028/www.scientific.net/amr.524-527.1660.

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In exploration for natural gas hydrates, drilling, coring and well logging are the most important access to make deep understanding of the nature of hydrate reservoirs, besides the seismic prospecting methods. Because of the harsh conditions for hydrate stability and the complex of occurrence formations, the drilling and coring generally have a great difficulty and high cost. Therefore, the well logging becomes the priority method. The resistivity and sonic logging method, which were applied as the earliest logging method in the evaluation of hydrate reserviors, have been continuously applied ever since and the evaluation results derived from them have a relative accuracy and reliability. Other logging tools, such as borehole imaging, density, electromagnetic, nuclear magnetic resonance, etc. are also used to make integrated interpretation and evaluation for the hydrate reservoirs. Until now the applied porosity and hydrate saturation evaluation models are better suitable to the homogeneous reservoirs. However, they still need to be amended or improved for the anisotropism (e.g., fracture sediment) and shale-rich reservoirs. In addition, the external factors such as drilling fluid washout and invasion will also affect the well logging results. The combination of various well logging methods is an effective way to improve the accuracy of identification and quantification of hydrate reservoirs.
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17

Li, Ting Jun, Hai Ning Yang, Qing Zhao, and Zheng Ou Zhou. "A Novel Single-Borehole Radar for Well Logging." Applied Mechanics and Materials 239-240 (December 2012): 511–15. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.511.

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An impulse-based single-borehole radar prototype for well logging is presented. An armored multi-conductor well logging cable is used to connect subsurface sonde and surface equipment in the prototype which is well compatible with the other well logging instruments. The performance experiments of the prototype have been conducted in a test field. The results show that the prototype system is capable of detecting the target which is 8 meters away from the borehole.
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18

Li, Yong Hua. "The Design of Well Logging Data Acquisition Based on FPGA." Applied Mechanics and Materials 148-149 (December 2011): 1029–32. http://dx.doi.org/10.4028/www.scientific.net/amm.148-149.1029.

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A novel kind of logging data acquisition is designed based on FPGA.In this paper, the hardware and software are described in detail. Experiment results show that the designed circuit and program are reasonable and reliable by the logging signal simulator, which can meet the logging requirement of oil field.
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19

Zeng, Ke, Fei Guo, and Mei Ling Zhang. "Study on Calculation Method of Formation Pressure Using Well Logging Data." Applied Mechanics and Materials 490-491 (January 2014): 1419–23. http://dx.doi.org/10.4028/www.scientific.net/amm.490-491.1419.

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Formation pressure Changes is the main reason for casing damage. In order to clearly know formation pressure,taking Sabei area of Daqing oilfield for example,On the basis of researching the relation between Formation pressure and logging data,By studying the relation between test data of ninety-three small stratum pressure and well-logging data with logging unit of water-out interval logging series of nine RFT test well, with statistical regression we come into being a theory model making use of water-flooded zone log reading to calculate formation pressure quantificationally .To improve the calculation accuracy of the formation pressure using logging data.
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20

Liu, Olive Y. "Acoustic well logging method and apparatus." Journal of the Acoustical Society of America 87, no. 6 (1990): 2798. http://dx.doi.org/10.1121/1.399034.

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21

Sondergeld, Carl H. "Acoustic well logging method and system." Journal of the Acoustical Society of America 85, no. 4 (1989): 1807. http://dx.doi.org/10.1121/1.397946.

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22

Liu, Olive Y. "Acoustic well logging method and apparatus." Journal of the Acoustical Society of America 86, no. 5 (1989): 2046. http://dx.doi.org/10.1121/1.398560.

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23

Li, Ta-Tsien, and Yue-Jun Peng. "Parameter identification in SP well-logging." Inverse Problems 16, no. 2 (2000): 357–72. http://dx.doi.org/10.1088/0266-5611/16/2/307.

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24

Herron, M. M., and S. L. Herron. "Geological applications of geochemical well logging." Geological Society, London, Special Publications 48, no. 1 (1990): 165–75. http://dx.doi.org/10.1144/gsl.sp.1990.048.01.14.

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25

Snyder, Donald D., and David B. Fleming. "Well logging—A 25‐year perspective." GEOPHYSICS 50, no. 12 (1985): 2504–29. http://dx.doi.org/10.1190/1.1441881.

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Developments in the field of well logging over the last 25 years are reviewed. Surface and borehole instrumentation have evolved significantly, taking advantage of modern digital and analog integrated circuits. Most open‐hole petroleum well logs are now recorded digitally. Digital logs are also frequently acquired in cased‐hole petroleum, mineral, and geotechnical applications. Nuclear well‐log measurements have become accepted and reliable. New measurements include borehole compensated density and neutron‐porosity, sidewall epithermal neutron‐porosity, and most recently litho‐density. The neutron decay log, developed early in the 25‐year period, has undergone a number of major improvements since its introduction. Probes which make spectral measurements of natural gamma‐ray emission, and gamma‐ray emission from neutron interactions with matter have also been developed. Resistivity measurements are now made with probes which combine three or more sensors each with different depths of investigation so that information about the borehole invasion profile can be acquired. Acoustic logging methods have expressed major developments and improvements. The compensated sonic measurement was introduced early in the period along with the cement bond logging method. Interest in measurement of shear‐wave velocity has produced new direct shear‐wave measurements as well as improved acoustic probes for full‐waveform acoustic logging. Other interesting or promising methods which have been developed or improved during the period include the borehole televiewer, the borehole gravimeter, and the nuclear magnetic resonance log. The digital computer provides powerful capabilities for well‐log analysis both at the well site and in the office. Analysis of complex sand‐shale and carbonate formations using two or more logs in a simultaneous solution of a litho‐porosity model is now routine. Powerful signal processing techniques are being applied to “deconvolve” well logs, to enhance or synthesize images of the wellbore, and to estimate or extract information from full‐waveform acoustic logs. While new or improved measurements have been introduced and log analysts now have access to powerful computers and graphic work stations, understanding of the petrophysical significance of the measurements lags behind the basic hardware measurement and interpretation technology.
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26

MORITA, Tetsuya. "Photomultiplier Tube for Oil Well Logging." Journal of the Society of Mechanical Engineers 114, no. 1114 (2011): 680–81. http://dx.doi.org/10.1299/jsmemag.114.1114_680.

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27

Redding, Charles E. "Acoustic isolator for well logging system." Journal of the Acoustical Society of America 128, no. 1 (2010): 510. http://dx.doi.org/10.1121/1.3472303.

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28

Jackson, Jasper A. "Los Alamos NMR well logging project." Concepts in Magnetic Resonance 13, no. 6 (2001): 368–78. http://dx.doi.org/10.1002/cmr.1022.

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29

Wei, Xi. "The Research of Production Split Using Well Logging and Well Testing Method." Advanced Materials Research 915-916 (April 2014): 1438–42. http://dx.doi.org/10.4028/www.scientific.net/amr.915-916.1438.

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The most popular method to split the production is mainly based on permeability and effective thickness of reservoir. In this general method, the permeability comes from the well logging interpretation. But this permeability can not reflect the flow characteristics of real fluid in the reservoir. In this paper, a new production split method is proposed considering the well logging method, the well test method and relative permeability curve. It is proved to be reliable and effective after cases study. It is worth to promote in oilfields.
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30

Li, Lin. "Discussion on the function of logging technology in geological steering of horizontal well drilling." E3S Web of Conferences 375 (2023): 01033. http://dx.doi.org/10.1051/e3sconf/202337501033.

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Logging technology is the key technology used in modern petroleum resources exploitation. Reasonable application of logging technology is conducive to efficient exploitation of petroleum resources. Therefore, the application of logging technology in oil exploitation has always been the key research target of oil well exploitation technology. This paper discusses the function of logging technology in geological steering of horizontal well. This paper discusses the logging technology and the geological steering work of horizontal well, discusses the specific application and due function of the logging technology. In the process of discussion, in order to ensure the practical research of this paper, a good application process of the logging technology in the drilling geological steering of a horizontal well is put forward.
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31

Судаков, Владислав Анатольевич, Алексей Александрович Леонтьев, Марат Фанисович Валидов, et al. "Gisneiro 2.0 program package’s well logging interpretation capabilities allow involving huge number of wells." Нефтяная провинция, no. 4(32) (December 30, 2022): 252–66. http://dx.doi.org/10.25689/np.2022.4.252-266.

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Инновационный отечественный продукт ПК «Гиснейро 2.0» является комплексом алгоритмов по автоматизации интерпретации данных геофизических исследований скважин (ГИС), позволяющий усовершенствовать выполнение процессов при обработке геофизических данных по скважинам в многоскважинном режиме. Автоматическая обработка и интерпретация каротажных диаграмм значительно ускоряет анализ данных по скважинам, обеспечивает полное использование геологической информации и существенно повышают эффективность работы с ГИС. ПК «Гиснейро 2.0» на основе машинного обучения и математических алгоритмов проводит автоматическое стратиграфическое и литологическое расчленение разреза, расчет фильтрационно-емкостных свойств (ФЕС), определение эффективных толщин и их типы насыщения. По результатам опытной эксплуатации ПК «Гиснейро 2.0» в рамках научно-исследовательских работ (создание БД скважинной информации, проведение интерпретации ГИС, анализ и статистическая обработка данных керна и ГИС) и совместной работы с ПАО «Татнефть» позволяет в многоскважинном полуавтоматическом режиме получить результаты интерпретации ГИС, в том числе стратиграфическую разбивку разреза, границы эффективных толщин и их параметры фильтрационно-емкостных свойств и анализа керна. A novel domestic software product Gisneiro 2.0 is a complex of machine well logging interpretation algorithms to improve interpreting of a bulk of well logging data that have been gathered during life cycles of a huge number of wells. Automated interpretation of well logs accelerates the process of well data analysis by orders of magnitude utilizing the geological information available to the fullest extent at that, thus, significantly improving the efficiency of well logging interpretation. The machine learning and statistical methods employed by the Giseiro 2.0 software allow automated stratigraphic breakdown and lithology differentiation, calculation of reservoir properties, determination of net thicknesses and types of saturation. The software designer and the TATNEFT Company have tested the Gisneiro 2.0 program package withing the framework of a pilot project involving creation of a database, interpretation of well logs, analysis and statistical interpretation of well logging and core data. The results of well logging interpretation in the semi-automatic mode were obtained, in that number, stratigraphic breakdown, boundaries of net thicknesses, reservoir characteristics, and core analysis.
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32

Seidov, Vagif M., and Maleyka A. Aghayeva. "Algorithm for constructing unmeasured logging curves (pseudo-logging) in wells of developed fields." Journal of Geology, Geography and Geoecology 33, no. 3 (2024): 562–70. http://dx.doi.org/10.15421/112452.

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In many cases, the diagrams of some logs in an old well of producing fields are not recorded for some reason or another. Currently, the importance of reassessing the efficiency of such fields is obvious, while the absence of some log curves causes great difficulties. In particular very few studies with acoustic logging (AL) have been carried out in old wells. On the other hand, in 1960-80, among the studies carried out in wells, preference was given to various types of electrical logging (EL), neutron-gamma logging (NGL), and gamma ray logging (GRL). To overcome such difficulties, it is necessary to create diagrams of unmeasured well logs or pseudo-logs. Nowadays, there are methods for constructing pseudolog curves. For example, the method of statistical analysis is widely used. However, in most cases, their implementation is very complex and requires a lot of work. We have developed a new method to construct pseudo-log curves. At first, the propagation velocity of elastic waves in rocks is studied based on seismic survey (SS) and AL data, which are performed presently on the investigation field. Based on this information, objects of interest to the oil industry are selected, their geometric dimensions are determined, the contour of oil and gas saturation is assessed, areas with abnormally high reservoir pressure are studied, the process of accumulation deposition is re-examined, and the preparation of an optimal well network diagram for the effective exploitation of the field is determined. Further, based on the long-ago measured curves of electrical loggings and neutron-gamma logging, we obtain the pseudo-log AL curve in old boreholes and the data SS. The proposed method was tested in 48 wells in the Bahar field (Azerbaijan). The method has shown high efficiency, its reliability is confirmed by independent data. The results of the method help in solving several geological problems: monitoring changes in petrophysical quantities of rocks between wells during development; study of well formations located below the design depth; recalculation of hydrocarbon reserves; determination of the location for drilling new wells, etc
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33

Zhang, Wenzhe, Jiajia Feng, Shan Gao, Zhongzheng Wang, and Jiarui Cheng. "Force Analysis of Logging Cable in Deep Well." Journal of Physics: Conference Series 2076, no. 1 (2021): 012092. http://dx.doi.org/10.1088/1742-6596/2076/1/012092.

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Abstract With the increase of deep wells, high temperature and high pressure wells and complex wells, the demand for logging is also increasing. Wireline logging is an important technical means to obtain downhole data in the process of petroleum testing. This paper establishes a cable mechanics model by analyzing the main influencing factors of the cable force in the inclined well section or the vertical well section. Calculate the lifting power of the tool. Through logging calculation, the force change law of the downhole cable and tool string is obtained when the wellhead pressure changes.
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34

Ren, Zhi Ping, and Rui Rong Dang. "A New Resistivity Logging Method in Production Well." Advanced Materials Research 616-618 (December 2012): 975–78. http://dx.doi.org/10.4028/www.scientific.net/amr.616-618.975.

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This paper presents a new method for resistivity logging in production well. Based on electromagnetic theory, an electromagnetic response model is established for multi-cylindrical-layer, and the general expressions for through casing electromagnetic logging are deduced. We further calculate and simulate the effects of casing permeability on electromagnetic response and demonstrate the necessity of casing magnetization. Finally, the effectiveness of casing magnetization is validated by the magnetization experiment. The result shows signal penetration capability is significantly enhanced after casing magnetization. It demonstrates that casing magnetization is an effective method for resistivity logging in production well.
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35

Al-Somali, Omar, and Mustafa Amer. "Radiation protection for well logging operations in Saudi Arabia." Radiation Protection Dosimetry 199, no. 8-9 (2023): 911–14. http://dx.doi.org/10.1093/rpd/ncad091.

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Abstract In well logging, sophisticated tools are inserted into a borehole to measure physical and geological properties of rocks surrounding the well. Some of these, known as nuclear logging tools, contain radioactive sources and are used to obtain useful information. When logging tools containing radioactive sources are inserted into the well, there is a risk that they may get stuck. If this occurs, a recovery operation, known as fishing, is used to attempt retrieval. If fishing fails to recover the radioactive sources, they are abandoned following an established protocol, which is in accordance with international, national and corporate standards, in addition to industry best practices. This paper provides an overview of radiation protection requirements for well logging operations in Saudi Arabia, to ensure the safety and security of radioactive sources, and the protection of workers and the public without impacting operational productivity.
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36

M, Halafawi. "Offset Wells Data Analysis and Thermal Simulations Improve the Performance of Drilling HPHT Well." Petroleum & Petrochemical Engineering Journal 6, no. 1 (2022): 1–15. http://dx.doi.org/10.23880/ppej-16000298.

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To drill new HPHT development wells safely, an exact estimate of their stability is essential. Analyzing previously drilled offset wells can assist in this determination, eliminating any stratigraphic column issues and saving nonproductive time. The challenges found with offset wellbores, their consequences on well design, possible remedies, and preventative measures are discussed in this paper. It examines drilling data from offset wells in order to discover, diagnose, and treat serious issues. Furthermore, thermal simulation was done in order to study the temperature distribution of the wellbore, annuli and fluids during drilling, tripping, circulation, logging, casing and cementing in HPHT zone.
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37

Zhang, Yuan Zhong, and Xiu Ying Miao. "Numerical Modeling of Mud Invasion Profile Based on the Well Logging with the Shallow Depth of Investigation." Advanced Materials Research 524-527 (May 2012): 148–51. http://dx.doi.org/10.4028/www.scientific.net/amr.524-527.148.

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The drilling mud invasion is a dynamic process varying with time and reveal the permeability or the relatively permeability of the formation saturated with fluid. The mud invasion profile denotes the water saturation change with the radial distance from the borehole wall. The logging with the shallow depth of investigation (DOI), such as density logging, neutron logging and shallow electrical logging, often detects the information of the flushed zone. We chose DOI logging to model the slope invasion profile with the hyperbolic secant function, and the calculated logging reading comparing from the real logging reading to optimize the invasion profile. The results show that the mud invasion profile and the invasion depth are strongly affected by porosity, where the invasion depth reduces with porosity adding.
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38

Korost, D. V., G. A. Kalmykov, E. V. Reshetov, and V. S. Belokhin. "Petrophysical software for interpreting integrated well-logging data based on spectrometric gamma-logging." Moscow University Geology Bulletin 64, no. 2 (2009): 130–37. http://dx.doi.org/10.3103/s0145875209020082.

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39

Varignier, Geoffrey, Valentin Fondement, Cédric Carasco, et al. "Comparison between GEANT4 and MCNP for well logging applications." EPJ Web of Conferences 288 (2023): 01002. http://dx.doi.org/10.1051/epjconf/202328801002.

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MCNP and GEANT4 are two reference Monte Carlo nuclear simulators, MCNP being the standard in the Oil & Gas nuclear logging industry. While performing a simulation benchmark of these two software for the purpose of “Cased Hole” wellbore evaluation, discrepancies between MCNP and GEANT4 were observed: computational experiments were performed first in a theoretical and simplified environment using spherical models, then in a more realistic “Open Hole” wellbore context with simplified logging tools. Results of this comparison show an excellent overall agreement for gamma-gamma physics and an acceptable agreement for neutron-neutron physics. However, the agreement for neutron-gamma physics is satisfactory only for certain lithologies and energy windows, but not acceptable for other operating conditions. These results need to be put in perspective with the current use of nuclear simulation in the logging industry. Indeed, wellbore evaluations rely on charts simulated with Monte Carlo codes in various contexts. In the case of radially heterogeneous environments such as “Cased Hole” wellbores, nuclear simulations are mandatory to precisely determine the radial sensitivity of logging tools via the so-called sensitivity functions. The feasibility of wellbore inversion relies on the physical validity of such sensitivity functions obtained from nuclear simulations. This MCNP vs. GEANT4 benchmark was conducted with the perspective to secure the physical fundamentals used for building the sensitivity functions of logging tools.
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40

Liu, Haining, Yuping Wu, Yingchang Cao, et al. "Well Logging Based Lithology Identification Model Establishment Under Data Drift: A Transfer Learning Method." Sensors 20, no. 13 (2020): 3643. http://dx.doi.org/10.3390/s20133643.

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Recent years have witnessed the development of the applications of machine learning technologies to well logging-based lithology identification. Most of the existing work assumes that the well loggings gathered from different wells share the same probability distribution; however, the variations in sedimentary environment and well-logging technique might cause the data drift problem; i.e., data of different wells have different probability distributions. Therefore, the model trained on old wells does not perform well in predicting the lithologies in newly-coming wells, which motivates us to propose a transfer learning method named the data drift joint adaptation extreme learning machine (DDJA-ELM) to increase the accuracy of the old model applying to new wells. In such a method, three key points, i.e., the project mean maximum mean discrepancy, joint distribution domain adaptation, and manifold regularization, are incorporated into extreme learning machine. As found experimentally in multiple wells in Jiyang Depression, Bohai Bay Basin, DDJA-ELM could significantly increase the accuracy of an old model when identifying the lithologies in new wells.
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41

Hu, Jie, Guangzheng Jiang, Yibo Wang, and Shengbiao Hu. "Thermal Conductivity Estimation Based on Well Logging." Mathematics 9, no. 11 (2021): 1176. http://dx.doi.org/10.3390/math9111176.

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The thermal conductivity of a stratum is a key factor to study the deep temperature distribution and the thermal structure of the basin. A huge expense of core sampling from boreholes, especially in offshore areas, makes it expensive to directly test stratum samples. Therefore, the use of well logging (the gamma-ray, the neutron porosity, and the temperature) to estimate the thermal conductivity of the samples obtained from boreholes could be a good alternative. In this study, we measured the thermal conductivity of 72 samples obtained from an offshore area as references. When the stratum is considered to be a shale–sand–fluid model, the thermal conductivity can be calculated based on the mixing models (the geometric mean and the square root mean). The contents of the shale and the sand were derived from the natural gamma-ray logs, and the content of the fluid (porosity) was derived from the neutron porosity logs. The temperature corrections of the thermal conductivity were performed for the solid component and the fluid component separately. By comparing with the measured data, the thermal conductivity predicted based on the square root model showed good consistency. This technique is low-cost and has great potential to be used as an application method to obtain the thermal conductivity for geothermal research.
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Lavanya, B. "Interpretation Well Logging Data in KG Basin." International Journal for Research in Applied Science and Engineering Technology 6, no. 1 (2018): 856–64. http://dx.doi.org/10.22214/ijraset.2018.1130.

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43

Knight, Bruce L., and Mohammad J. Davarzani. "Injection Well Logging Using Viscous EOR Fluids." SPE Formation Evaluation 1, no. 03 (1986): 300–308. http://dx.doi.org/10.2118/13143-pa.

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44

Wignall, Albert H., and David C. Hoyle. "Sonic well logging tool longitudinal wave attenuator." Journal of the Acoustical Society of America 87, no. 5 (1990): 2270. http://dx.doi.org/10.1121/1.399168.

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45

Kai Hsu and T. L. Marzetta. "Velocity filtering of acoustic well logging waveforms." IEEE Transactions on Acoustics, Speech, and Signal Processing 37, no. 2 (1989): 265–74. http://dx.doi.org/10.1109/29.21689.

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46

Traeger, R. K., and P. C. Lysne. "High temperature electronics application in well logging." IEEE Transactions on Nuclear Science 35, no. 1 (1988): 852–54. http://dx.doi.org/10.1109/23.12845.

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47

Melcher, C. L., J. S. Schweitzer, R. S. Manente, and C. A. Peterson. "Applicability of GSO scintillators for well logging." IEEE Transactions on Nuclear Science 38, no. 2 (1991): 506–9. http://dx.doi.org/10.1109/23.289349.

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48

Forster, R. A., R. C. Little, J. F. Briesmeister, and J. S. Hendricks. "MCNP capabilities for nuclear well logging calculations." IEEE Transactions on Nuclear Science 37, no. 3 (1990): 1378–85. http://dx.doi.org/10.1109/23.57390.

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49

Frankle, C. M., and G. E. Dale. "Unconventional neutron sources for oil well logging." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 723 (September 2013): 24–29. http://dx.doi.org/10.1016/j.nima.2013.05.012.

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50

Epov, M. I., V. L. Mironov, K. V. Muzalevskiy, I. N. Eltsov, and U. P. Salomatov. "A geosteering tool for horizontal well logging." Russian Geology and Geophysics 54, no. 9 (2013): 1103–7. http://dx.doi.org/10.1016/j.rgg.2013.07.022.

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