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Journal articles on the topic 'Well Log Analysis'

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Published: 4 June 2021

Last updated: 5 February 2022

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Zhao, Hank, Natalie B. Givens, and Brad Curtis. "Thermal maturity of the Barnett Shale determined from well-log analysis." AAPG Bulletin 91, no. 4 (2007): 535–49. http://dx.doi.org/10.1306/10270606060.

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12

Hernandez-Martinez, Eliseo, Teresa Perez-Muñoz, Jorge X. Velasco-Hernandez, Armando Altamira-Areyan, and Luis Velasquillo-Martinez. "Facies Recognition Using Multifractal Hurst Analysis: Applications to Well-Log Data." Mathematical Geosciences 45, no. 4 (2013): 471–86. http://dx.doi.org/10.1007/s11004-013-9445-6.

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13

Mann, Ulrich, and Peter J. Müller. "Source rock evaluation by well log analysis (Lower Toarcian, Hils syncline)." Organic Geochemistry 13, no. 1-3 (1988): 109–19. http://dx.doi.org/10.1016/0146-6380(88)90031-9.

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14

S S, Paul, Okwueze ., E. E, and Udo K I. "Petrophysical Analysis of Well Logs for the Estimation of Oil Reserves in Southern Niger Delta." International Journal of Advanced Geosciences 6, no. 1 (2018): 145. http://dx.doi.org/10.14419/ijag.v6i1.11815.

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Gamma Ray log, Resistivity log, Density log, Micro-spherical focus log (MSFL), Deep Induction log (ILD) , Medium Induction log(ILM) and Spontaneous Potential (SP) log were collected for 2 wells in onshore Niger Delta. These insitu well logs were analyzed and interpreted. Porosity, permeability, water saturation, reservoir thickness and Shale volume were estimated for each hydrocarbon bearing zone delineated for each well. The parameters obtained were further analyzed and interpreted quantitatively to estimate the hydrocarbon potentials of each well. Twelve reservoir zones of interest (sand bodies) were delineated, correlated across the field and were ranked using average results of petrophysical parameters. In well one, Reservoirs E and F were identified as the thickest with 41ft each while reservoir A is the smallest in thickness (30ft). Petrophysical properties of hydrocarbon bearing zones delineated in well one ranged from 17.81% to 23.20% for porosity, 1292.09mD to 2018.17mD for permeability and 56.40% to 68.40% for hydrocarbon saturation compared to well 2 with 14.67% to 19.52% for porosity, 1211.61mD to1843.11mD for permeability and 51.80% to 66.40% for hydrocarbon saturation. The estimated averages of petrophysical properties for well one are 20.14% porosity, 1643.65mD permeability, 63.20% hydrocarbon saturation compared to well 2 with 15.55% porosity, 1582.58mD permeability and 61.93% hydrocarbon saturation. Results show 148.45MMBB and 145.91MMBB as oil reserve (Recoverable) for the field. From the results obtained, well one is likely to be more productive than well [2] and the field has exploitable oil in place.

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15

Zhang, Jia, Shiqing Cheng, Shiying Di, Zhanwu Gao, Rui Yang, and Pengliang Cao. "A Two-Phase Numerical Model of Well Test Analysis to Characterize Formation Damage in Near-Well Regions of Injection Wells." Geofluids 2021 (February 18, 2021): 1–15. http://dx.doi.org/10.1155/2021/6693965.

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Formation damage usually occurs in near-well regions for injection wells completed in offshore oilfields under the development of line drive patterns. However, current works on characterizing the damage by well test analysis were basically focused on using single-phase analogy to solve two-phase flow issues, resulting in errors on the diagnosis and interpretation of transient pressure data. In this paper, we developed a two-phase model to simulate the pressure transient behavior of a water injection well in a multiwell system. To solve the model more efficiently, we used the finite volume method to discretize partially differential flow equations in a hybrid grid system, including both Cartesian and radial meshes. The fully implicit Newton-Raphson method was also employed to solve the equations in our model. With this methodology, we compared the resulting solutions with a commercial simulator. Our results keep a good agreement with the solutions from the simulator. We then graphed the solutions on a log-log plot and concluded that the effects of transitional zone and interwell interference can be individually identified by analyzing specific flow regimes on the plot. Further, seven scenarios were raised to understand the parameters which dominate the pressure transient behavior of these flow regimes. Finally, we showed a workflow and verified the applicability of our model by demonstrating a case study in a Chinese offshore oilfield. Our model provides a useful tool to reduce errors in the interpretation of pressure transient data derived from injection wells located in a line drive pattern.

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16

Amosu, Adewale, and Yuefeng Sun. "MinInversion: A Program for Petrophysical Composition Analysis of Geophysical Well Log Data." Geosciences 8, no. 2 (2018): 65. http://dx.doi.org/10.3390/geosciences8020065.

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17

Subhakar, D., and E. Chandrasekhar. "Reservoir characterization using multifractal detrended fluctuation analysis of geophysical well-log data." Physica A: Statistical Mechanics and its Applications 445 (March 2016): 57–65. http://dx.doi.org/10.1016/j.physa.2015.10.103.

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18

Chandrasekhar, E., and V. Eswara Rao. "Wavelet Analysis of Geophysical Well-log Data of Bombay Offshore Basin, India." Mathematical Geosciences 44, no. 8 (2012): 901–28. http://dx.doi.org/10.1007/s11004-012-9423-4.

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19

Inichinbia, Sonny, Peter O. Sule, Aminu L. Ahmed, Halidu Hamza, and Kolawole M. Lawal. "Petro physical analysis of among hydrocarbon field fluid and lithofaciesusing Well Log Data." IOSR Journal of Applied Geology and Geophysics 2, no. 2 (2014): 86–96. http://dx.doi.org/10.9790/0990-02218696.

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20

O.A, Omoregbe, Oloyede D.A, and Momoh H.N. "Volumetric Analysis and Characterization of D_7 Reservoir using Well log and Seismic Data." International Journal of Scientific and Research Publications (IJSRP) 10, no. 7 (2020): 68–79. http://dx.doi.org/10.29322/ijsrp.10.07.2020.p10308.

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21

Lomakina, N. Y., V. B. Belozerov, and N. V. Bukhanov. "Lithology evaluation of a Bazhenov formation reservoir using seismic and well log analysis." IOP Conference Series: Earth and Environmental Science 24 (January 1, 2015): 012047. http://dx.doi.org/10.1088/1755-1315/24/1/012047.

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22

Song, Man-Hyok, Kyong-Ho Li, and Song-Nam Kim. "Evaluation of periodicities and fractal characteristics by wavelet analysis of well log data." Computers & Geosciences 119 (October 2018): 29–38. http://dx.doi.org/10.1016/j.cageo.2018.05.002.

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23

Nasr, Maher, Jasmin Raymond, Michel Malo, and Erwan Gloaguen. "Geothermal potential of the St. Lawrence Lowlands sedimentary basin from well log analysis." Geothermics 75 (September 2018): 68–80. http://dx.doi.org/10.1016/j.geothermics.2018.04.004.

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24

Feng, Runhai. "Uncertainty analysis in well log classification by Bayesian long short-term memory networks." Journal of Petroleum Science and Engineering 205 (October 2021): 108816. http://dx.doi.org/10.1016/j.petrol.2021.108816.

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25

Feng, Runhai, Dario Grana, and Niels Balling. "Imputation of missing well log data by random forest and its uncertainty analysis." Computers & Geosciences 152 (July 2021): 104763. http://dx.doi.org/10.1016/j.cageo.2021.104763.

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26

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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27

Baker, P. L. "Log analysis systems incorporating artificial intelligence techniques ? some examples." Exploration Geophysics 20, no. 2 (1989): 331. http://dx.doi.org/10.1071/eg989331.

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Artificial Intelligence (Al) systems have been used with some success in the areas of dipmeter interpretation, quantitative log interpretation and well-to-well correlation. A prototype expert system has been developed using a rule-based approach to lithology identification. Extensions of the system are now being considered to do mineral identification for the problem of mineral model construction for multi-mineral log interpretation algorithms.

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28

Escobar, Freddy-Humberto, Laura-Jimena Vega, and Luis-Fernando Bonilla. "Determination of well-drainage area for power-law fluids by transient pressure analysis." CT&F - Ciencia, Tecnología y Futuro 5, no. 1 (2012): 45–56. http://dx.doi.org/10.29047/01225383.214.

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Since conventional oil is almost depleted, oil companies are focusing their efforts on exploiting heavy oil reserves. A modern and practical technique using the pressure and pressure derivative, log-log plot for estimating the well-drainage area in closed and constant-pressure reservoirs, drained by a vertical well is presented by considering a non-Newtonian flow model for describing the fluid behavior. Several synthetic examples were presented for demonstration and verification purposes.Such fluids as heavy oil, fracturing fluids, some fluids used for Enhanced Oil Recovery (EOR) and drilling muds can behave as either Power-law or Bingham, usually referred to as the non-Newtonian fluids. Currently, there is no way to estimate the well-drainage area from conventional well test analysis when a non-Newtonian fluid is dealt with; therefore, none of the commercial well test interpretation package can estimate this parameter (drainage area).

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29

Nie, Xiang Rong, and Shi Qing Cheng. "Pressure Transient Analysis of Polymer Injection Wells." Advanced Materials Research 361-363 (October 2011): 370–76. http://dx.doi.org/10.4028/www.scientific.net/amr.361-363.370.

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Polymer solution is known as non-Newtonian Fluid. Hence, when a well is injected by polymer solution, the well test data analysis using Newtonian fluid flow model will be erroneous. However, the analysis results usually were inaccurate when generalized non-Newtonian fluid model which considering polymer solution as power law fluid and taking no account of physical and chemical behaviors. These results clearly suggest the need for a study to come up with a new model considering both physical and chemical behaviors when polymer solution flowing in the reservoirs. At first, this study modified two parameter models: viscosity model and permeability decreasing coefficient model, all of them considering diffusion, conduction and IPV (inaccessible pore volume). Then, those models were applied to set up the new well testing model of a well located in an infinite reservoir. The log-log plots of the pressure and pressure derivatives have been prepared through numerical solutions. A further study has been done about the characteristics of the new type curves considering different parameters.

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30

Einstein, Elizabeth E., and Ken W. Edwards. "Comparison of an Expert System to Human Experts in Well-Log Analysis and Interpretation." SPE Formation Evaluation 5, no. 01 (1990): 39–45. http://dx.doi.org/10.2118/18129-pa.

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31

Feng, Runhai. "Improving uncertainty analysis in well log classification by machine learning with a scaling algorithm." Journal of Petroleum Science and Engineering 196 (January 2021): 107995. http://dx.doi.org/10.1016/j.petrol.2020.107995.

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32

Oluwatoyin, Ologe. "Reservoir evaluation of “T-X” field (Onshore, Niger delta) from well log petrophysical analysis." Bayero Journal of Pure and Applied Sciences 9, no. 2 (2017): 132. http://dx.doi.org/10.4314/bajopas.v9i2.25.

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33

Zong, Jingjing, Robert R. Stewart, Nikolay Dyaur, and Michael T. Myers. "Elastic properties of rock salt: Laboratory measurements and Gulf of Mexico well-log analysis." GEOPHYSICS 82, no. 5 (2017): D303—D317. http://dx.doi.org/10.1190/geo2016-0527.1.

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Rock salt (essentially halite) is a special type of sedimentary rock that has played a large role throughout tectonic and economic history. The unique physical properties of halite (ductility, low density, flowability, and impermeability) can be critical factors in hydrocarbon traps and underground storage. However, seismic imaging and interpretation can be challenging when salt structures are present due to their complex geometry and large impedance contrasts relative to surrounding rocks. To investigate the properties of rock salt in terms of elastic parameters, we use ultrasonic laboratory measurements and well logs. In the laboratory, we have analyzed the effects of composition, crystalline structure, pressure, and temperature on the elastic behavior of a variety of rock salt samples. The samples include pure halite (>95 wt%) from the Gulf of Mexico (GOM) area, argillaceous rock salt from the Zipaquirá Mine, Colombia, and crystalline salt from the Goderich Mine, Canada. Current measurements suggest that the GOM salt cores behave isotropically in general. The Zipaquirá salt samples show velocity and density variations on account of their heterogeneous composition. The Goderich halite crystals display distinct cubic anisotropy. Measurements on the GOM samples at varying confining pressures and temperatures indicate that increasing pressure elevates velocity whereas increasing temperature decreases velocity. From the analysis of 145 log suites from boreholes drilled through rock salt in the northern GOM, we found that, within the salt formations, P-wave velocities increased slightly with depth (approximately [Formula: see text] per km). The S-wave velocities from three wells range from 2280 to [Formula: see text]. Bulk densities from all the wells cluster at [Formula: see text]. These laboratory and log measurements provide new values for the elastic properties of rock salt, which can assist in velocity model building, synthetic seismogram generation, and the understanding of the rock physics of halite.

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34

Esmailzadeh, Akbar, Sina Ahmadi, Reza Rooki, and Reza Mikaeil. "Oil Reservoir Permeability Estimation from Well Logging Data Using Statistical Methods (A Case Study: South Pars Oil Reservoir)." Civil Engineering Journal 3, no. 10 (2017): 831. http://dx.doi.org/10.28991/cej-030918.

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Permeability is a key parameter that affects fluids flow in reservoir and its accurate determination is a significant task. Permeability usually is measured using practical approaches such as either core analysis or well test which both are time and cost consuming. For these reasons applying well logging data in order to obtaining petrophysical properties of oil reservoir such as permeability and porosity is common. Most of petrophysical parameters generally have relationship with one of well logged data. But reservoir permeability does not show clear and meaningful correlation with any of logged data. Sonic log, density log, neutron log, resistivity log, photo electric factor log and gamma log, are the logs which effect on permeability. It is clear that all of above logs do not effect on permeability with same degree. Hence determination of which log or logs have more effect on permeability is essential task. In order to obtaining mathematical relationship between permeability and affected log data, fitting statistical nonlinear models on measured geophysical data logs as input data and measured vertical and horizontal permeability data as output, was studied. Results indicate that sonic log, density log, neutron log and resistivity log have most effect on permeability, so nonlinear relationships between these logs and permeability was done.

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35

Grassinger, Julia Maria, Andreas Floren, Tobias Müller, et al. "Digital Lesions in Dogs: A Statistical Breed Analysis of 2912 Cases." Veterinary Sciences 8, no. 7 (2021): 136. http://dx.doi.org/10.3390/vetsci8070136.

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Breed predispositions to canine digital neoplasms are well known. However, there is currently no statistical analysis identifying the least affected breeds. To this end, 2912 canine amputated digits submitted from 2014–2019 to the Laboklin GmbH & Co. KG for routine diagnostics were statistically analyzed. The study population consisted of 155 different breeds (most common: 634 Mongrels, 411 Schnauzers, 197 Labrador Retrievers, 93 Golden Retrievers). Non-neoplastic processes were present in 1246 (43%), tumor-like lesions in 138 (5%), and neoplasms in 1528 cases (52%). Benign tumors (n = 335) were characterized by 217 subungual keratoacanthomas, 36 histiocytomas, 35 plasmacytomas, 16 papillomas, 12 melanocytomas, 9 sebaceous gland tumors, 6 lipomas, and 4 bone tumors. Malignant neoplasms (n = 1193) included 758 squamous cell carcinomas (SCC), 196 malignant melanomas (MM), 76 soft tissue sarcomas, 52 mast cell tumors, 37 non-specified sarcomas, 29 anaplastic neoplasms, 24 carcinomas, 20 bone tumors, and 1 histiocytic sarcoma. Predisposed breeds for SCC included the Schnauzer (log OR = 2.61), Briard (log OR = 1.78), Rottweiler (log OR = 1.54), Poodle (log OR = 1.40), and Dachshund (log OR = 1.30). Jack Russell Terriers (log OR = −2.95) were significantly less affected by SCC than Mongrels. Acral MM were significantly more frequent in Rottweilers (log OR = 1.88) and Labrador Retrievers (log OR = 1.09). In contrast, Dachshunds (log OR = −2.17), Jack Russell Terriers (log OR = −1.88), and Rhodesian Ridgebacks (log OR = −1.88) were rarely affected. This contrasted with the well-known predisposition of Dachshunds and Rhodesian Ridgebacks to oral and cutaneous melanocytic neoplasms. Further studies are needed to explain the underlying reasons for breed predisposition or “resistance” to the development of specific acral tumors and/or other sites.

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36

Imran, Qazi Sohail, Numair Ahmad Siddiqui, Abdul Halim Abdul Latif, Yasir Bashir, Almasgari Abdalsalam Abduh Saeed Ali, and Muhammad Jamil. "Integrated Well Data And 3D Seismic Inversion Study For Reservoir Delineation And Description." Bulletin Of The Geological Society Of Malaysia 70, no. 1 (2020): 209–20. http://dx.doi.org/10.7186/bgsm70202016.

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Offshore petroleum systems are often very complex and subtle because of a variety of depositional environments. Characterizing a reservoir based on conventional seismic and well-log stratigraphic analysis in intricate settings often leads to uncertainties. Drilling risks, as well as associated subsurface uncertainties can be minimized by accurate reservoir delineation. Moreover, a forecast can also be made about production and performance of a reservoir. This study is aimed to design a workflow in reservoir characterization by integrating seismic inversion, petrophysics and rock physics tools. Firstly, to define litho facies, rock physics modeling was carried out through well log analysis separately for each facies. Next, the available subsurface information is incorporated in a Bayesian engine which outputs several simulations of elastic reservoir properties, as well as their probabilities that were used for post-inversion analysis. Vast areal coverage of seismic and sparse vertical well log data was integrated by geostatistical inversion to produce acoustic impedance realizations of high-resolution. Porosity models were built later using the 3D impedance model. Lastly, reservoir bodies were identified and cross plot analysis discriminated the lithology and fluid within the bodies successfully.

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37

Leem, Kook-Mook, Jong-Oh Park, Young-Chul Yu, and Moo-Young Song. "Geophysical Well-log Analysis of Andesite and Sedimentary Rocks in Myodo-dong, Yeosu, Southern Korea." Journal of the Korean earth science society 30, no. 4 (2009): 401–8. http://dx.doi.org/10.5467/jkess.2009.30.4.401.

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38

Siregar, Irdian, Yufu Niu, Peyman Mostaghimi, and Ryan T. Armstrong. "Coal ash content estimation using fuzzy curves and ensemble neural networks for well log analysis." International Journal of Coal Geology 181 (August 2017): 11–22. http://dx.doi.org/10.1016/j.coal.2017.08.003.

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39

Al-Areeq, Nabil M., and Abdulwahab S. Alaug. "Well log analysis and hydrocarbon potential of the Sa'ar–Nayfa reservoir, Hiswah Oilfield, eastern Yemen." Arabian Journal of Geosciences 7, no. 7 (2013): 2941–56. http://dx.doi.org/10.1007/s12517-013-1003-5.

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40

Grana, Dario, Marco Pirrone, and Tapan Mukerji. "Quantitative log interpretation and uncertainty propagation of petrophysical properties and facies classification from rock-physics modeling and formation evaluation analysis." GEOPHYSICS 77, no. 3 (2012): WA45—WA63. http://dx.doi.org/10.1190/geo2011-0272.1.

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Formation evaluation analysis, rock-physics models, and log-facies classification are powerful tools to link the physical properties measured at wells with petrophysical, elastic, and seismic properties. However, this link can be affected by several sources of uncertainty. We proposed a complete statistical workflow for obtaining petrophysical properties at the well location and the corresponding log-facies classification. This methodology is based on traditional formation evaluation models and cluster analysis techniques, but it introduces a full Monte Carlo approach to account for uncertainty evaluation. The workflow includes rock-physics models in log-facies classification to preserve the link between petrophysical properties, elastic properties, and facies. The use of rock-physics model predictions guarantees obtaining a consistent set of well-log data that can be used both to calibrate the usual physical models used in seismic reservoir characterization and to condition reservoir models. The final output is the set of petrophysical curves with the associated uncertainty, the profile of the facies probabilities, and the entropy, or degree of confusion, related to the most probable facies profile. The full statistical approach allows us to propagate the uncertainty from data measured at the well location to the estimated petrophysical curves and facies profiles. We applied the proposed methodology to two different well-log studies to determine its applicability, the advantages of the new integrated approach, and the value of uncertainty analysis.

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SAUCIER, ANTOINE, and JIRI MULLER. "USE OF MULTIFRACTAL ANALYSIS IN THE CHARACTERIZATION OF GEOLOGICAL FORMATIONS." Fractals 01, no. 03 (1993): 617–28. http://dx.doi.org/10.1142/s0218348x93000654.

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We use multifractal analysis as a tool for the characterization of geological well log signals. The signals investigated come from dipmeter microresistivity log devices. It is suggested that the multifractal spectra computed from these signals could be used to distinguish geological formations and lithofacies containing different types of oil reservoir heterogeneities.

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Essien, Ubong. "Shale volume and porosity delineation of coast swamp depobelt in Niger delta region, Nigeria, using well log." International Journal of Advanced Geosciences 7, no. 2 (2019): 142. http://dx.doi.org/10.14419/ijag.v7i2.29772.

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Well log data from two wells were evaluated for shale volume, total and effective porosity. Well log data were obtained from gamma ray, neutron-density log, resistivity, sonic and caliper log respectively. This study aimed at evaluating the effect of shale volume, total and effective porosity form two well log data. The results of the analysis depict the presence of sand, sand-shale and shale formations. Hydrocarbon accumulation were found to be high in sand, fair in sand-shale and low in shale, since existence of shale reduces total and effective porosity and water saturation of the reservoir. The thickness of the reservoir ranged from 66 – 248.5ft. The average values of volume of shale, total and effective porosity values ranged from 0.004 – 0.299dec, 0.178 – 0.207dec and 0.154 – 0.194dec. Similarly, the water saturation and permeability ranged from 0.277 – 0.447dec and 36.637 - 7808.519md respectively. These values of total and effective porosity are high in sand, fair in sand-shale and low in shale formations. The results for this study demonstrate: accuracy, applicability of these approaches and enhance the proper evaluation of petrophysical parameters from well log data.

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Tang, Hong, Niall Toomey, and W. Scott Meddaugh. "Using an Artificial-Neural-Network Method To Predict Carbonate Well Log Facies Successfully." SPE Reservoir Evaluation & Engineering 14, no. 01 (2011): 35–44. http://dx.doi.org/10.2118/123988-pa.

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Summary The Maastrichtian (Upper Cretaceous) reservoir is one of five prolific oil reservoirs in the giant Wafra oil field. The Maastrichtian oil production is largely from subtidal dolomites at an average depth of 2,500 ft. Carbonate deposition occurred on a very gently dipping, shallow, arid, and restricted ramp setting that transitioned between normal marine conditions to restricted lagoonal environments. The average porosity of the reservoir interval is approximately 15%, although productive zones have porosity values up to 30–40%. The average permeability of the reservoir interval is approximately 30 md. Individual core plugs have measured permeability up to 1,200 md. Efforts to predict sedimentary facies from well logs in carbonate reservoirs is difficult because of the complex carbonate sedimentary facies structures, strong diagenetic overprint, and challenging log analysis in part owing to the presence of vugs and fractures. In the study, a workflow including (1) core description preprocessing, (2) log- and core-data cleanup, and (3) probabilistic-neural-network (PNN) facies analysis was used to predict facies from log data accurately. After evaluation of a variety of statistical approaches, a PNN-based approach was used to predict facies from well-log data. The PNN was selected as a tool because it has the capability to delineate complex nonlinear relationships between facies and log data. The PNN method was shown to outperform multivariate statistical algorithms and, in this study, gave good prediction accuracy (above 70%). The prediction uncertainty was quantified by two probabilistic logs—discriminant ability and overall confidence. These probabilistic logs can be used to evaluate the prediction uncertainty during interpretation. Lithofacies were predicted for 15 key wells in the Wafra Maastrichtian reservoir and were effectively used to extend the understanding of the Maastrichtian stratigraphy, depositional setting, and facies distribution.

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Bintarto, Bambang, Rizky Rahmat Auliya, Riza Andhika Mahendra Putra, Afif Surya Pradipta, and Rafli Arie Kurnia. "Production Data Analysis and Sonolog for Determining Artificial Lift Design and Well Characteristic." Journal of Petroleum and Geothermal Technology 1, no. 1 (2020): 28. http://dx.doi.org/10.31315/jpgt.v1i1.3321.

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Tarakan Field, North Kalimantan is a part of PT. Pertamina EP Asset 5. The Tarakan Field has 5 structures in the form of Pamusian, Juata, Sesanip, Mangatal, and Sembakung. The Tarakan Field has 57 production wells and 6 injection wells. The wells at Tarakan field are produced with artificial lifts in the form of Sucker Rod Pump (SRP) totaling 25, Hydraulic Pumping Unit (HPU) totaling 11, Electric Submersible Pump (ESP) totaling 19 and Progressive Cavity Pump (PCP) totaling 2. The determination of artificial lifts is carried out by the design of well characteristics and production history. The design at Tarakan Field was carried out with an artificial lift in the form of ESP (Electric Submersible Pump). ESP is used according to reservoir and formation characteristics in Tarakan Field. Water Control Diagnostic Plot is a method used to analyze the effect of control on produced water. Water Control Diagnostic plot is plot between WOR and WOR derivative vs time. The plot was carried out on a log-log scale. The plot on the Water Control Diagnostic Plot is then analyzed against the graph created by the KS Chan. So from the analyzed plot, it is found whether or not there is a problem in the well at Tarakan Field. The results of the graph analysis on the well at Tarakan Field on the chart show that the field does not indicate a problem.  Keywords: chan plot; design; esp; production

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Grigorash, Alexander, Siobhan O'Neill, Raymond Bond, Colette Ramsey, Cherie Armour, and Maurice D. Mulvenna. "Predicting Caller Type From a Mental Health and Well-Being Helpline: Analysis of Call Log Data." JMIR Mental Health 5, no. 2 (2018): e47. http://dx.doi.org/10.2196/mental.9946.

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Background This paper presents an analysis of call data records pertaining to a telephone helpline in Ireland among individuals seeking mental health and well-being support and among those who are in a suicidal crisis. Objective The objective of our study was to examine whether rule sets generated from decision tree classification, trained using features derived from callers’ several initial calls, could be used to predict what caller type they would become. Methods Machine learning techniques were applied to the call log data, and five distinct patterns of caller behaviors were revealed, each impacting the helpline capacity in different ways. Results The primary findings of this study indicate that a significant model (P<.001) for predicting caller type from call log data obtained from the first 8 calls is possible. This indicates an association between callers’ behavior exhibited during initial calls and their behavior over the lifetime of using the service. Conclusions These data-driven findings contribute to advanced workload forecasting for operational management of the telephone-based helpline and inform the literature on helpline caller behavior in general.

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S Mulyanto, Bagus, Ordas Dewanto, and Sri Rizky. "Determining Layer Oil Shale as New Alternative Energy Sources Using Core Analysis and Well Log Method." International Journal of Engineering & Technology 7, no. 4.36 (2018): 941. http://dx.doi.org/10.14419/ijet.v7i4.36.24925.

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Oil shale is a type of shale containing organic material is not yet mature. When heated to a certain temperature, the organic content to mature so that it can produce petroleum. Exploration with a reliable method to energy sources of the future needs to be done, because it has a particularly important role in the context of the national energy supply security in the future. Oil deposits in shale oil is quite high, estimated as relatively large reserves spread across several regions of Indonesia. To determine the content of oil shale in the basin necessary to evaluate the condition of the reservoir, by determining and analyzing the reservoir parameters. Determination and analysis of reservoir parameters is done by two methods, namely core analysis in the laboratory and log interpretation in the field. Determination of oil-bearing layer in the wells is performed quantitatively which is based on the value HTTI=14.20-75.30 ´103 cal cm-3)and Ro=0.70-2.17 (%). Further is qualitatively to determine layer containing shale (oil shale). First look at the layers of shale from the high GR value and then their separation sonic and resistivity in the shale layer. A layer of oil shale wells BG-6 is present at a depth of 1637-1687 meters. BGS-1 wells at a depth of 2025-2062, 2100-2125, 2175-2200, 2275-2310 meters. BGS-2 wells at a depth of 2025-2045, 2138-2160, 2275-2295, 2380-2405 meters. BGS-3 wells at a depth of 2150-2165, 2212-2225 meters. While the BGS-4 wells at a depth of 2175-2187.5, 2212.5-2245, 2300-2312.5, 2350-2388 meters.

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Kumar, Atul, Mohd Haris Mohd Khir, and Wan Ismail Wan Yusoff. "A model-based approach for integration analysis of well log and seismic data for reservoir characterization." Geosciences Journal 20, no. 3 (2016): 331–50. http://dx.doi.org/10.1007/s12303-015-0045-y.

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Bueno, Juliana Finoto, Bruno César Zanardo Honório, Michelle Chaves Kuroda, Alexandre Campane Vidal, and Emilson Pereira Leite. "Structural and stratigraphic feature delineation and facies distribution using seismic attributes and well log analysis applied to a Brazilian carbonate field." Interpretation 2, no. 1 (2014): SA83—SA92. http://dx.doi.org/10.1190/int-2013-0087.1.

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Identification of structural and stratigraphic components within a hydrocarbon reservoir is essential for the construction of geologic models that represent the facies distribution as accurately as possible. Such construction may represent a challenge for ancient carbonate reservoirs, which exhibit greater heterogeneity than siliciclastic reservoirs. We developed a workflow that incorporated seismic attribute analysis and well log data to highlight structural and architectural elements with the benefit of the understanding the distribution of facies in this features in an Albian offshore carbonate reservoir in Brazil. The proposed workflow combined incoherence, sweetness, and a new approach of spectral decomposition to delineate the main structural and architectural elements in the field. Well log facies and modern analog analyses were used to support the architectural elements’ interpretation. Joulters Cays is an oolitic shoal in the Bahamas that provided a modern analog for the field in study. Three main fault systems, responsible for the rollover structure of the field, were detected with the incoherence attribute. The workflow allowed the interpretation of four main architectural elements in the field: (1) tidal channels, (2) carbonate shoals, (3) reef, and (4) lagoons. Tidal channels and lagoons are mainly composed by wackestone. Carbonate shoals are primarily composed by grainstone with secondary packstone, while the reef crest is composed by packstone. Sequential indicator simulation (SIS) of well log facies supported these observations. The highlighted architectural elements could be helpful for the construction of a conceptual model of the field or useful as a training image for multiple-point statistics because SIS of well log facies does not define geologic bodies.

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J. Sunday, Abe, and Lurogho S. Ayoleyi. "Petrophysical analysis of “explorer” wells using well log and core data(a case study of “explorer” field, offshore Niger Delta, Nigeria)." International Journal of Advanced Geosciences 8, no. 2 (2020): 219. http://dx.doi.org/10.14419/ijag.v8i2.31114.

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Reservoir characterization involves computing various petrophysical parameters and defining them in terms of their quantity and quality so as to ascertain the yield of the reservoir. Petrophysical well log and core data were integrated to analyze the reservoir characteristics of Explorer field, Offshore, Niger Delta using three wells. The study entails determination of the lithology, shale volume (Vsh), porosity (Φ), permeability (K), fluid saturation and cross plotting of petrophysical and core values at specific intervals to know their level of correlation. The analysis identified twelve hydrocarbon-bearing reservoir from three different wells. Average permeability value of the reservoir is 20, 0140md while porosity value range between 18% to 39%. Fluid type defined in the reservoirs on the basis of neutron/density log signature were basically water, oil and gas, low water saturation values ranging from 2.9% to 46% in Explorer wells indicate high hydrocarbon saturation. The Pearson Correlation Coefficient and Regression Equation gave a significant relationship between petrophysical derived data and core data. Scatter plot of petrophysical gamma ray values versus core gamma ray values gave an approximate linear relationship with correlation coefficient values of 0.6642, 0.9831 and 0.3261. Crossplot of petrophysical density values and core density values revealed that there is a strong linear relationship between the two data set with correlation coefficient values of 0.7581, 0.9872 and 0.3557, and the regression equation confirmed the relationship between the two data set. Also the scatter plot of petrophysical porosity density values versus core porosity density values revealed a strong linear relationship between the two data set with correlation coefficient values of 0.7608 and 0.9849, the regression equation confirmed this also. Crossplot of petrophysical porosity density values versus core porosity density values in Well 3 gave a very weak correlation coefficient values of 0.3261 and 0.3557 with a negative slope. The petrophysical properties of the reservoirs in Explorer Well showed that they contain hydrocarbon in commercial quantity and the cross plot of the petrophysical and core values showed direct relationship in most of the wells.

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Alimohammadi, Davood, Ebrahim Analouei Adegani, Teodor Bulboacă, and Nak Eun Cho. "Logarithmic Coefficient Bounds and Coefficient Conjectures for Classes Associated with Convex Functions." Journal of Function Spaces 2021 (June 13, 2021): 1–7. http://dx.doi.org/10.1155/2021/6690027.

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It is well-known that the logarithmic coefficients play an important role in the development of the theory of univalent functions. If S denotes the class of functions f z = z + ∑ n = 2 ∞ a n z n analytic and univalent in the open unit disk U , then the logarithmic coefficients γ n f of the function f ∈ S are defined by log f z / z = 2 ∑ n = 1 ∞ γ n f z n . In the current paper, the bounds for the logarithmic coefficients γ n for some well-known classes like C 1 + α z for α ∈ 0 , 1 and C V hpl 1 / 2 were estimated. Further, conjectures for the logarithmic coefficients γ n for functions f belonging to these classes are stated. For example, it is forecasted that if the function f ∈ C 1 + α z , then the logarithmic coefficients of f satisfy the inequalities γ n ≤ α / 2 n n + 1 , n ∈ ℕ . Equality is attained for the function L α , n , that is, log L α , n z / z = 2 ∑ n = 1 ∞ γ n L α , n z n = α / n n + 1 z n + ⋯ , z ∈ U .

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