Fast Prediction Method of Radon Concentration in Environment Air

2014 ◽  
Vol 539 ◽  
pp. 819-822
Author(s):  
He Xi Wu ◽  
Qiang Lin Wei ◽  
Bo Yang ◽  
Qing Cheng Liu
Keyword(s):  
Relative Error ◽  
Radon Concentration ◽  
Gamma Ray ◽  
Transport Model ◽  
Prediction Method ◽  
Field Tests ◽  
Mean Value ◽  
Absolute Relative Error ◽  
Average Absolute Relative Error ◽  
Fast Prediction

Base on the theory that 222Rn can transport in any medium, fast prediction model of radon concentration in environment air can be acquired. And it has been proved accurate by an experiment in laboratory. Many field tests also showed that the average absolute relative error is 8.78% between mean value of measurement and that of fast prediction. It can be predict fleetly the radon concentration by 226Ra which is acquired from the airborne gamma-ray spectra. The relative error between measurement and model is-11.7%. Therefore, the transport model can be effectively applied to predict radon concentration in environment air.

scholarly journals An Improved CO2-Crude Oil Minimum Miscibility Pressure Correlation

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Hao Zhang ◽  
Dali Hou ◽  
Kai Li
Keyword(s):  
Experimental Data ◽  
Crude Oil ◽  
Relative Error ◽  
Oil And Gas ◽  
Empirical Correlation ◽  
Volatile Components ◽  
Absolute Relative Error ◽  
Minimum Miscibility Pressure ◽  
New Correlation ◽  
Average Absolute Relative Error

Minimum miscibility pressure (MMP), which plays an important role in miscible flooding, is a key parameter in determining whether crude oil and gas are completely miscible. On the basis of 210 groups of CO2-crude oil system minimum miscibility pressure data, an improved CO2-crude oil system minimum miscibility pressure correlation was built by modified conjugate gradient method and global optimizing method. The new correlation is a uniform empirical correlation to calculate the MMP for both thin oil and heavy oil and is expressed as a function of reservoir temperature, C7+molecular weight of crude oil, and mole fractions of volatile components (CH4and N2) and intermediate components (CO2, H2S, and C2~C6) of crude oil. Compared to the eleven most popular and relatively high-accuracy CO2-oil system MMP correlations in the previous literature by other nine groups of CO2-oil MMP experimental data, which have not been used to develop the new correlation, it is found that the new empirical correlation provides the best reproduction of the nine groups of CO2-oil MMP experimental data with a percentage average absolute relative error (%AARE) of 8% and a percentage maximum absolute relative error (%MARE) of 21%, respectively.

scholarly journals Constitutive Equations for Describing the Warm and Hot Deformation Behavior of 20Cr2Ni4A Alloy Steel

Metals ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 1169
Author(s):  
Haoran Wang ◽  
Wei Wang ◽  
Ruixue Zhai ◽  
Rui Ma ◽  
Jun Zhao ◽  
...  
Keyword(s):  
Flow Stress ◽  
Alloy Steel ◽  
Relative Error ◽  
Constitutive Equations ◽  
Type Equation ◽  
Coefficient Of Determination ◽  
Original Strain ◽  
Compression Tests ◽  
Absolute Relative Error ◽  
Average Absolute Relative Error

Isothermal hot compression tests of 20Cr2Ni4A alloy steel were performed under temperatures of 973–1273 K and strain rates of 0.001–1 s−1. The behavior of the flow stress of 20Cr2Ni4A alloy steel at warm and hot temperatures is complicated because of the influence of the work hardening, the dynamic recovery, and the dynamic recrystallization. Four constitutive equations were used to predict the flow stress of 20Cr2Ni4A alloy steel, including the original strain-compensated Arrhenius-type (osA-type) equation, the new modified strain-compensated Arrhenius-type (msA-type) equation, the original Hensel–Spittel (oHS) equation and the modified Hensel–Spittel (mHS) equation. The msA-type and mHS are developed by revising the deformation temperatures, which can improve prediction accuracy. In addition, we propose a new method of solving the parameters by combining a linear search with multiple linear regression. The new solving method is used to establish the two modified constitutive equations instead of the traditional regression analysis. A comparison of the predicted values based on the four constitutive equations was performed via relative error, average absolute relative error (AARE) and the coefficient of determination (R2). These results show the msA-type and mHS equations are more accurate and efficient in terms of predicting the flow stress of the 20Cr2Ni4A steel at elevated temperature.

scholarly journals Comparative Study of Accurate Descriptions of Hot Flow Behaviors of BT22 Alloy by Intelligence Algorithm and Physical Modeling

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Shaoling Ding ◽  
Chao Fang ◽  
Shulin Zhang
Keyword(s):  
Physical Model ◽  
Relative Error ◽  
Nonlinear Flow ◽  
Support Vector ◽  
Experimental Stress ◽  
Stress Strain ◽  
Absolute Relative Error ◽  
Model Average ◽  
Average Absolute Relative Error ◽  
Strain Data

The nonlinear flow behaviors of BT22 alloy were investigated by thermal simulation experiments at different temperature and strain rates. Taking the experimental stress-strain data as samples, the support vector regression (SVR) model and back propagation artificial neural network (BPANN) model were established by cross-validation (CV) method to describe the nonlinear flow behaviors of BT22 alloy. Genetic algorithm (GA) was used to optimize the parameters of the SVR model and establish the GA-SVR model. At the same time, the physical model optimized by GA algorithm is compared with the machine learning model. Average absolute relative error (AARE), absolute relative error (ARE), and correlation coefficient (R) were used to evaluate the predictive ability of the four models. The results show that the order of model accuracy and generalization ability is GA-SVR > BPANN > SVR > physical model. The AARE value of the GA-SVR model is 1.5752%, and the R value is as high as 0.9984, which can accurately predict the flow behaviors of BT22 alloy. According to the GA-SVR model, the flow behaviors under other conditions could be predicted to expand the experimental stress-strain data and avoid a large number of artificial tests.

Application of Optimized Least Square Support Vector Machine and Genetic Programming for Accurate Estimation of Drilling Rate of Penetration

2018 ◽  
Vol 7 (4) ◽  
pp. 92-108
Author(s):  
Meysam Naderi ◽  
Ehsan Khamehchi
Keyword(s):  
Relative Error ◽  
Flow Rate ◽  
Rotation Speed ◽  
Gel Strength ◽  
Least Square ◽  
Accurate Estimation ◽  
Support Vector ◽  
Absolute Relative Error ◽  
Average Absolute Relative Error ◽  
Weight On Bit

This article describes how the accurate estimation of the rate of penetration (ROP) is essential to minimize drilling costs. There are various factors influencing ROP such as formation rock, drilling fluid properties, wellbore geometry, type of bit, hydraulics, weight on bit, flow rate and bit rotation speed. This paper presents two novel methods based on least square support vector machine (LSSVM) and genetic programming (GP). Models are a function of depth, weight on bit, rotation speed, stand pipe pressure, flow rate, mud weight, bit rotational hours, plastic viscosity, yield point, 10 second gel strength, 10 minute gel strength, and fluid loss. Results show that LSSVM estimates 92% of field data with average absolute relative error of less than 6%. In addition, sensitivity analysis showed that factors of depth, weight on bit, stand pipe pressure, flow rate and bit rotation speed account for 93% of total variation of ROP. Finally, results indicate that LSSVM is superior over GP in terms of average relative error, average absolute relative error, root mean square error, and the coefficient of determination.

scholarly journals Predicting the flow zone indicator of carbonate reservoirs using NMR echo transforms, and routine open-hole log measurements: Insights from a field case study spanning extreme micro-structure properties

2021 ◽  
Author(s):  
Mabkhout Al-Dousari ◽  
◽  
Salah Almudhhi ◽  
Ali A. Garrouch ◽  
◽  
...  
Keyword(s):  
Relative Error ◽  
Carbonate Reservoir ◽  
Micro Structure ◽  
Flow Zone ◽  
Hydraulic Flow ◽  
Absolute Relative Error ◽  
Flow Units ◽  
Average Absolute Relative Error ◽  
Open Hole ◽  
Hydraulic Flow Units

Predicting the flow zone indicator is essential for identifying the hydraulic flow units of hydrocarbon reservoirs. Delineation of hydraulic flow units is crucial for mapping petrophysical and rock mechanical properties. Precise prediction of the flow zone indicator (FZI) of carbonate rocks using well log measurements in un-cored intervals is still a daunting challenge for petrophysicists. This study presents a data mining methodology for predicting the rock FZI using NMR echo transforms, and conventional open-hole log measurements. The methodology is applied on a carbonate reservoir with extreme microstructure properties, from an oil “M” field characterized by a relatively high-permeability with a median of approximately 167 mD, and a maximum of 3480 mD. The reservoir from the M field features detritic, or vuggy structure, covering a wide range of rock fabrics varying from microcrystalline mudstones to coarse-grained grainstones. Porosity has a median of approximately 22%. Dimensional analysis and regression analysis are applied for the derivation of four transforms that appear to capture approximately 80% of the FZI variance. These four transforms are formulated using the geometric mean of the transverse NMR relaxation time (T2lm), the ratio of the free fluid index (FFI) to the bulk volume irreducible (BVI), the bulk density, the sonic compressional travel time, the true resistivity, the photo-electric absorption, and the effective porosity. Non-linear regression models have been developed for predicting the FZI using the derived transforms, for the carbonate reservoir from the M field. The average relative error for the estimated FZI values is approximately 52%. The same transforms are used as input for training a developed general regression neural network (GRNN), built for the purpose of predicting rock FZI. The constructed GRNN predicts FZI with a notable precision. The average absolute relative error on FZI for the training set is approximately 3.1%. The average absolute relative error on FZI for the blind testing set is approximately 22.0 %. The data mining approach presented in this study appears to suggest that (i) the relationship between the flow zone indicator and open-hole log attributes is highly non-linear, (ii) the FZI is highly affected by parameters that reflect rock texture, rock micro-structure geometry, and diagenetic alterations, and (iii) the derived transforms provide a means for further enhancement of the flow zone indicator prediction in carbonate reservoirs.

The Measurements of Natural Radioactivity, (Radon and Gamma concentrations), around the old fertilizer factory in Basrah/Iraq

2015 ◽  
Vol 7 (1) ◽  
pp. 1324-1335
Author(s):  
Jabbar H. Jebur
Keyword(s):  
Effective Dose ◽  
Radon Concentration ◽  
Gamma Ray ◽  
Annual Effective Dose ◽  
Track Detector ◽  
Exhalation Rate ◽  
Hazard Indices ◽  
Radium Equivalent ◽  
Passive Technique ◽  
Radium Activity

Radon concentration, exhalation rate, annual effective dose, radium activity, thorium, uranium potassium and radium equivalent have been measured in the present investigation for soil in the area around the old fertilizer factory in southern of Basrah Governorate. The measurements based on CR39 track detector for passive method, RAD7 for active method and NaI(Tl) for gamma concentration measurements. Average values for radon concentration in soil were 112.04±10.76 Bq/m3 using passive technique and 104.56±6.05 Bq/m3 using RAD7. From the result of the passive technique, area and mass exhalation rates and the annual effective dose were calculated. Gamma ray spectroscopy for the soil samples were performed and found that the average concentrations of 226Ra, 232Th and 40K were 50.89 Bq/kg, 21.74 Bq/kg and 640.4 Bq/kg respectively. Gamma ray hazard indices were calculated and found they are within the world average.

Shut-in Stabilization Time Optimization for Better Reservoir Pressure Monitoring Harnessing Neural Network Modeling

2021 ◽  
Author(s):  
Mohammad Al Kadem ◽  
Ali Al Ssafwany ◽  
Ahmed Abdulghani ◽  
Hussain Al Nasir
Keyword(s):  
Neural Network ◽  
Neural Network Modeling ◽  
Stabilization Time ◽  
Pressure Derivative ◽  
Absolute Relative Error ◽  
Pressure Sensitive ◽  
Time Optimization ◽  
Average Absolute Relative Error ◽  
Study Results ◽  
Artificial Neural Network Ann

Abstract Stabilization time is an essential key for pressure measurement accuracy. Obtaining representative pressure points in build-up tests for pressure-sensitive reservoirs is driven by optimizing stabilization time. An artificial intelligence technique was used in the study for testing pressure-sensitive reservoirs using measuring gauges. The stabilization time function of reservoir characteristics is generally calculated using the diffusivity equation where rock and fluid properties are honored. The artificial neural network (ANN) technique will be used to predict the stabilization time and optimize it using readily available and known inputs or parameters. The values obtained from the formula known as the diffusion formula and the ANN technique are then compared against the actual values measured from pressure gauges in the reservoirs. The optimization of the number of datasets required to be fed to the network to allow for coverage over the whole range is essential as opposed to the clustering of the datasets. A total of about 3000 pressure derivative samples from the wells were used in the testing, training, and validation of the ANN. The datasets are optimized by dividing them into three fractional parts, and the number optimized through monitoring the ANN performance. The optimization of the stabilization time is essential and leads to the improvement of the ANN learning process. The sensitivity analysis proves that the use of the formula and ANN technique, compared to actual datasets, is better since, in the formula and ANN technique, the time was optimized with an average absolute relative error of 3.67%. The results are near the same, especially when the ANN technique undergoes testing using known and easily available parameters. Time optimization is essential since discreet points or datasets in the ANN technique and formula would not work, allowing ANN to work in situations of optimization. The study was expected to provide additional data and information, considering that stabilization time is essential in obtaining the pressure map representation. ANN is a superior technique and, through its superiority, allows for proper optimization of time as a parameter. Thus it can predict reservoir log data almost accurately. The method used in the study shows the importance of optimizing pressure stabilization time through reduction. The study results can, therefore, be applied in reservoir testing to achieve optimal results.

scholarly journals Assessment of the Accuracy of a Multi-Beam LED Scanner Sensor for Measuring Olive Canopies

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4406 ◽  
Author(s):  
Rafael Sola-Guirado ◽  
Sergio Bayano-Tejero ◽  
Antonio Rodríguez-Lizana ◽  
Jesús Gil-Ribes ◽  
Antonio Miranda-Fuentes
Keyword(s):  
Laboratory Test ◽  
Real Time ◽  
Relative Error ◽  
Laser Scanner ◽  
Field Tests ◽  
High Accuracy ◽  
Field Conditions ◽  
Canopy Trees ◽  
Canopy Volume

Canopy characterization has become important when trying to optimize any kind of agricultural operation in high-growing crops, such as olive. Many sensors and techniques have reported satisfactory results in these approaches and in this work a 2D laser scanner was explored for measuring canopy trees in real-time conditions. The sensor was tested in both laboratory and field conditions to check its accuracy, its cone width, and its ability to characterize olive canopies in situ. The sensor was mounted on a mast and tested in laboratory conditions to check: (i) its accuracy at different measurement distances; (ii) its measurement cone width with different reflectivity targets; and (iii) the influence of the target’s density on its accuracy. The field tests involved both isolated and hedgerow orchards, in which the measurements were taken manually and with the sensor. The canopy volume was estimated with a methodology consisting of revolving or extruding the canopy contour. The sensor showed high accuracy in the laboratory test, except for the measurements performed at 1.0 m distance, with 60 mm error (6%). Otherwise, error remained below 20 mm (1% relative error). The cone width depended on the target reflectivity. The accuracy decreased with the target density.

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