DENSITY LOGGING

Geophysics ◽  
1960 ◽  
Vol 25 (4) ◽  
pp. 891-904 ◽  
Author(s):  
J. J. Pickell ◽  
J. G. Heacock
Keyword(s):  
Bulk Density ◽  
Gamma Ray ◽  
Petroleum Industry ◽  
Core Data ◽  
Response Characteristics ◽  
Calibration Curves ◽  
Ray Density ◽  
Log Interpretation

This review of density logging is primarily a compilation of information presented in the petroleum industry literature. It includes a brief discussion of some of the theory involved in gamma‐ray density logging, various calibration curves, comparisons of density‐log and core data, and comments on density‐log interpretation. Conclusions are that the density log, under good borehole conditions, provides an accurate means for measuring bulk density of the formation adjacent to the borehole. If grain density is known, valid estimates of porosity can also be made. Because of the response characteristics of the system, accuracy in determining porosity is best when formation densities are low and porosities are high.

DEVELOPMENT OF COMPACTION PATTERNS DUE TO MACHINERY OPERATION IN AN ORCHARD SOIL

1976 ◽  
Vol 56 (3) ◽  
pp. 505-509 ◽  
Author(s):  
G. S. V. RAGHAVAN ◽  
E. MCKYES ◽  
M. CHASSÉ ◽  
F. MÉRINEAU
Keyword(s):  
Bulk Density ◽  
Gamma Ray ◽  
Soil Bulk Density ◽  
Soil Conditions ◽  
Soil Density ◽  
Compaction Test ◽  
Orchard Soil ◽  
Ray Density

A series of tests was performed in a field, freshly prepared and ready for the planting of new trees, to study the pattern of soil density changes under different loads, soil conditions, tire sizes and numbers of passes using a gamma-ray density meter. The change in soil bulk density varied from 0.08 g/cc to 0.48 g/cc for increasing numbers of traverses of tractor and sprayer. The soil bulk density achieved after 15 passes with a tractor and sprayer approximated both the maximum bulk density obtained with a standard Proctor compaction test and the maximum bulk density that has been observed in adjacent orchards that are 30–40 yr old.

Gamma Ray Density Gauging

1971 ◽  
pp. 387-394
Author(s):  
John E. Hoel ◽  
Thomas W. Novitsky
Keyword(s):  
Gamma Ray ◽  
Ray Density

scholarly journals Nonintrusive Depth Estimation of Buried Radioactive Wastes Using Ground Penetrating Radar and a Gamma Ray Detector

2019 ◽  
Vol 11 (2) ◽  
pp. 141 ◽  
Author(s):  
Ikechukwu Ukaegbu ◽  
Kelum Gamage ◽  
Michael Aspinall
Keyword(s):  
Bulk Density ◽  
Ground Penetrating Radar ◽  
Gamma Ray ◽  
Depth Estimation ◽  
Average Error ◽  
Density Estimates ◽  
Material Density ◽  
Linear Correction ◽  
Ground Penetrating ◽  
Gamma Ray Detector

This study reports on the combination of data from a ground penetrating radar (GPR) and a gamma ray detector for nonintrusive depth estimation of buried radioactive sources. The use of the GPR was to enable the estimation of the material density required for the calculation of the depth of the source from the radiation data. Four different models for bulk density estimation were analysed using three materials, namely: sand, gravel and soil. The results showed that the GPR was able to estimate the bulk density of the three materials with an average error of 4.5%. The density estimates were then used together with gamma ray measurements to successfully estimate the depth of a 658 kBq ceasium-137 radioactive source buried in each of the three materials investigated. However, a linear correction factor needs to be applied to the depth estimates due to the deviation of the estimated depth from the measured depth as the depth increases. This new application of GPR will further extend the possible fields of application of this ubiquitous geophysical tool.

Coal identification using neural networks with real-time coalbed methane drilling data

2019 ◽  
Vol 59 (1) ◽  
pp. 319 ◽  
Author(s):  
Ruizhi Zhong ◽  
Raymond Johnson Jr ◽  
Zhongwei Chen ◽  
Nathaniel Chand
Keyword(s):  
Real Time ◽  
Coalbed Methane ◽  
Gamma Ray ◽  
Ann Model ◽  
Drilling Data ◽  
Weight On Bit ◽  
Net To Gross ◽  
Rotary Speed ◽  
Artificial Neural Network Ann ◽  
Log Interpretation

Currently, coal is identified using coring data or log interpretation. Coring is the most dependable methodology, but it is costly and its characterisation is expensive and time consuming. Logging methods are convenient, reliable, and reproducible, but can be subject to statistical and shouldering effects and often have operational difficulties in deviated or horizontal wells. Drilling data, which are routinely available, can potentially be used to identify coal sections in a machine learning environment when conventional wireline logs are not available. To achieve this, a four-layer artificial neural network (ANN) was used to identify coals in a well at Walloon Sub-Group, Surat Basin. The ANN model used drilling data and some logging-while-drilling (LWD) data. The inputs for the lithological model from high-frequency drilling data include weight on bit, rotary speed, torque, and rate of penetration. Inputs from LWD data include gamma ray and hole diameter. The criterion for coal identification is based on bulk density cutoff. The simulation results show that the ANN can deliver an overall accuracy of 96%. Due to the low net-to-gross ratio of coals within the Walloon sequence, a lower but reasonable F1 score of 0.78 is achievable for the coal sections. The proposed model can potentially be implemented in real-time to identify coal intervals without additional logs and aid validation of minimal log data.

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