scholarly journals Influence of the Gamma Ray towards Porosity Evaluation of the Rocks Using Well Log Measurement

2015 ◽  
Vol 15 (2) ◽  
pp. 103
Author(s):  
Puguh Hiskiawan
Keyword(s):  
Physical Parameter ◽  
Gamma Ray ◽  
The Self ◽  
Physical Parameters ◽  
Well Log ◽  
Earth Surface ◽  
Rock Density ◽  
Potential Value ◽  
Rock Structure ◽  
Self Potential

Well Log method is one of the geophysics methods used to examine rock structure under earth surface. This research is studied by using utilizing influence of the gamma ray to physical parameter on well log measurement. This research is explained to compare the measuring data with some physical parameters. The results shown that the rock density was about 2.00 – 2.50 g/cc, the rock porosity was round 2.00 – 2.50 g/cc, whereas the resistivity has 2 Ωm to 60 Ωm. All those vaiables were measured at same depth 3420 – 3470 ft. Furthermore, the self potential value was the highest negatively. The result from physical parameter provided meaning about anomaly positively of hydrocarbon existing.Keywords: Well Log, gamma ray, hydrocarbon  

scholarly journals Resolving the excess of long GRB’s at low redshift in the Swift era

2020 ◽  
Vol 493 (1) ◽  
pp. 1479-1491 ◽  
Author(s):  
Truong Le ◽  
Cecilia Ratke ◽  
Vedant Mehta
Keyword(s):  
Physical Parameter ◽  
Power Law ◽  
Gamma Ray ◽  
Hubble Constant ◽  
High Energy ◽  
Star Formation History ◽  
Physical Parameters ◽  
Data Set ◽  
Energy Indices ◽  
Parameter Values

ABSTRACT Utilizing more than 100 long gamma-ray bursts (LGRBs) in the Swift-Ryan-2012 sample that includes the observed redshifts and jet angles, Le & Mehta performed a timely study of the rate density of LGRBs with an assumed broken power-law GRB spectrum and obtained a GRB-burst-rate functional form that gives acceptable fits to the pre-Swift and Swift redshift, and jet angle distributions. The results indicated an excess of LGRBs at redshift below z ∼ 2 in the Swift sample. In this work, we are investigating if the excess is caused by the cosmological Hubble constant H0, the gamma-ray energy released ${\cal E}_{*\gamma }$, the low- and high-energy indices (α, β) of the Band function, the minimum and maximum jet angles θj, min and θj, max, or that the excess is due to a bias in the Swift-Ryan-2012 sample. Our analyses indicate that none of the above physical parameters resolved the excess problem, but suggesting that the Swift-Ryan-2012 sample is biased with possible afterglow selection effect. The following model physical parameter values provide the best fit to the Swift-Ryan-2012 and pre-Swift samples: the Hubble constant $H_0 = 72 \, {\rm km s^{-1} Mpc^{-1}}$, the energy released ${\cal E}_{*\gamma }\sim 4.47 \times 10^{51}$ erg, the energy indices α ∼ 0.9 and β ∼ −2.13, the jet angles of θj, max ∼ 0.8 rad, and θj, min ∼ 0.065 and ∼0.04 rad for pre-Swift and Swift, respectively, s ∼ −1.55 the jet angle power-law index, and a GRB formation rate that is similar to the Hopkins & Beacom observed star formation history and as extended by Li. Using the Swift Gamma-Ray Burst Host Galaxy Legacy Survey (SHOALS) Swift-Perley LGRB sample and applying the same physical parameter values as above, however, our model provides consistent results with this data set and indicating no excess of LGRBs at any redshift.

scholarly journals IDENTIFICATION OF GROUNDWATER DISTRIBUTION USING SELF POTENTIAL METHOD

2019 ◽  
Vol 1 (01) ◽  
pp. 16-23
Author(s):  
Muhammad Fathur Rouf Hasan ◽  
Tri Widya Swastika ◽  
Nunung Martina ◽  
Linda Sari Wulandari
Keyword(s):  
Electric Potential ◽  
Potential Method ◽  
The Self ◽  
Karst Area ◽  
Potential Value ◽  
Underground River ◽  
Self Potential ◽  
Groundwater Distribution

Drought is one of the biggest problems for the community, this problem will be slightly resolved by the compilation of regions with groundwater sources. This research has been carried out in the karst area, Druju Village, Sumbermanjing District, Wetan Regency, Malang. The purpose of this study is to determine the distribution of underground air and the pattern between underground rivers in the Wonorejo draw well and spring sources in the Kaligoro River, Wonorejo Hamlet.The method that used is the Self Potential (SP) method to set the moving electrode (leap frop) with 104 measurement points. The results of this study indicate the value of electric potential shown between 0-13.5 mV, and assume that a low potential value (0-1 mV) is an indication of groundwater distribution. Areas with low potential values are found in the empathy area. When taking a distribution of water on the surface of the land that appears through the Kaligoro River, it does not need to go out in one stream with an underground river flowing in the wells of the residents. The area with the lowest potential value is a reference in making wells

scholarly journals Hydrocarbon prospectivity of the miocene-pliocene clastic reservoirs, Northern Taranaki basin, New Zealand: integration of petrographic and geophysical studies

2022 ◽  
Author(s):  
Ahmed A. Radwan ◽  
Bassem S. Nabawy
Keyword(s):  
New Zealand ◽  
Gamma Ray ◽  
Well Log ◽  
Thin Sections ◽  
Early Pliocene ◽  
Fine Grained ◽  
The North ◽  
High K ◽  
Clastic Reservoirs ◽  
Self Potential

AbstractIn this study, it is aimed to characterize the Early pliocene sandstone (EP-SD) and the Late Miocene-Early Pliocene Mangaa sandstone reservoirs and the efficiency of their sealing cap rocks using the petrographical and petrophysical data of these sandstone zones in northern Taranaki basin, New Zealand. The prospective potential reservoirs were studied using impregnated thin sections, XRD data analysis, and well log data (self-potential, gamma-ray, sonic, density, neutron, shallow\deep resistivity and PEF) to characterize the reservoir zones, in addition to Mercury intrusion capillary pressure data (MICP) to check the efficiency of some potential seals. The EP-SD and the Mangaa sandstone units are typically poorly consolidated very fine sandstone to siltstone, with porosities averaging 25%. The sands are composed of quartz (38.3–57.4%), with common feldspars (9.9–15.2% plagioclase, and 2.7–6.3% K-feldspars) and up to 31.8% mica. In Albacore-1 well to the north of the Taranaki Basin, the Mangaa formation includes three separate for each of the EP-SD zones (EP-SD1, EP-SD2, and EP-SD3), and the Mangaa sequence (Mangaa-0, Mangaa-1, and Mangaa-2). The thin section studies indicate that, the studied samples are grouped into greywackes, arenites and siltstone microfacies with much lithic fragments and feldspars, sometimes with glauconite pellets. From the XRD data, it is achieved that the mineral composition is dominated by quartz, mica/illite, feldspars, and chlorite. The petrophysical investigation revealed absence of pay zones in the EP-SD zones, and presence of thin pay zone with net thickness 5.79 m and hydrocarbon saturation of about 25.6%. The effective porosities vary between 23.6 and 27.7%, while the shale volume lies between 12.3 and 16.9%. Although the shale content is relatively low, the relatively high API (50–112 API of average 75 API) is contributed by the relatively high K-feldspar content and intercalations with thin siltstone and muddy siltstone beds. Sealing units include the intra-formational seals within the Mangaa sequence, mudstones and fine grained units overlying the Mangaa and further intra-formational mudstones, within the shallower EP-SD units. The efficiency of these seals indicates the capability to trap 16.4–40.6 m gas or 17.4–43.0 m oil which is relatively low in correlation with their efficiency in the central parts of the Taranaki Basin Overlying the primary seals, mudstones of the Giant Foresets Formation provide additional regional seal.

scholarly journals Mapping the Pollution Plume Using the Self-Potential Geophysical Method: Case of Oum Azza Landfill, Rabat, Morocco

Water ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 961
Author(s):  
Meryem Touzani ◽  
Ismail Mohsine ◽  
Jamila Ouardi ◽  
Ilias Kacimi ◽  
Moad Morarech ◽  
...  
Keyword(s):  
Electrical Conductivity ◽  
Water Table ◽  
Soil Surface ◽  
The Self ◽  
Drainage Network ◽  
Geophysical Method ◽  
Direct Measurements ◽  
Sandy Material ◽  
The City ◽  
Self Potential

The main landfill in the city of Rabat (Morocco) is based on sandy material containing the shallow Mio-Pliocene aquifer. The presence of a pollution plume is likely, but its extent is not known. Measurements of spontaneous potential (SP) from the soil surface were cross-referenced with direct measurements of the water table and leachates (pH, redox potential, electrical conductivity) according to the available accesses, as well as with an analysis of the landscape and the water table flows. With a few precautions during data acquisition on this resistive terrain, the results made it possible to separate the electrokinetic (~30%) and electrochemical (~70%) components responsible for the range of potentials observed (70 mV). The plume is detected in the hydrogeological downstream of the discharge, but is captured by the natural drainage network and does not extend further under the hills.

scholarly journals Wave Propagation Characteristics in Gas Hydrate-Bearing Sediments and Estimation of Hydrate Saturation

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 804
Author(s):  
Lin Liu ◽  
Xiumei Zhang ◽  
Xiuming Wang
Keyword(s):  
Gas Hydrate ◽  
Clay Content ◽  
Clean Energy ◽  
Compressional Wave ◽  
Physical Parameters ◽  
Well Log ◽  
Log Data ◽  
Phase Velocities ◽  
Hydrate Saturation ◽  
Hydrate Bearing Sediments

Natural gas hydrate is a new clean energy source in the 21st century, which has become a research point of the exploration and development technology. Acoustic well logs are one of the most important assets in gas hydrate studies. In this paper, an improved Carcione–Leclaire model is proposed by introducing the expressions of frame bulk modulus, shear modulus and friction coefficient between solid phases. On this basis, the sensitivities of the velocities and attenuations of the first kind of compressional (P1) and shear (S1) waves to relevant physical parameters are explored. In particular, we perform numerical modeling to investigate the effects of frequency, gas hydrate saturation and clay on the phase velocities and attenuations of the above five waves. The analyses demonstrate that, the velocities and attenuations of P1 and S1 are more sensitive to gas hydrate saturation than other parameters. The larger the gas hydrate saturation, the more reliable P1 velocity. Besides, the attenuations of P1 and S1 are more sensitive than velocity to gas hydrate saturation. Further, P1 and S1 are almost nondispersive while their phase velocities increase with the increase of gas hydrate saturation. The second compressional (P2) and shear (S2) waves and the third kind of compressional wave (P3) are dispersive in the seismic band, and the attenuations of them are significant. Moreover, in the case of clay in the solid grain frame, gas hydrate-bearing sediments exhibit lower P1 and S1 velocities. Clay decreases the attenuation of P1, and the attenuations of S1, P2, S2 and P3 exhibit little effect on clay content. We compared the velocity of P1 predicted by the model with the well log data from the Ocean Drilling Program (ODP) Leg 164 Site 995B to verify the applicability of the model. The results of the model agree well with the well log data. Finally, we estimate the hydrate layer at ODP Leg 204 Site 1247B is about 100–130 m below the seafloor, the saturation is between 0–27%, and the average saturation is 7.2%.

scholarly journals On a Possible Origin of the Gamma-ray Excess around the Galactic Center

Symmetry ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1432
Author(s):  
Dmitry O. Chernyshov ◽  
Andrei E. Egorov ◽  
Vladimir A. Dogiel ◽  
Alexei V. Ivlev
Keyword(s):  
Dark Matter ◽  
Gamma Rays ◽  
Molecular Clouds ◽  
Alternative Explanation ◽  
Gamma Ray ◽  
Galactic Center ◽  
The Self ◽  
Large Area ◽  
Mhd Turbulence ◽  
The Standard Model

Recent observations of gamma rays with the Fermi Large Area Telescope (LAT) in the direction of the inner galaxy revealed a mysterious excess of GeV. Its intensity is significantly above predictions of the standard model of cosmic rays (CRs) generation and propagation with a peak in the spectrum around a few GeV. Popular interpretations of this excess are that it is due to either spherically distributed annihilating dark matter (DM) or an abnormal population of millisecond pulsars. We suggest an alternative explanation of the excess through the CR interactions with molecular clouds in the Galactic Center (GC) region. We assumed that the excess could be imitated by the emission of molecular clouds with depleted density of CRs with energies below ∼10 GeV inside. A novelty of our work is in detailed elaboration of the depletion mechanism of CRs with the mentioned energies through the “barrier” near the cloud edge formed by the self-excited MHD turbulence. This depletion of CRs inside the clouds may be a reason for the deficit of gamma rays from the Central Molecular Zone (CMZ) at energies below a few GeV. This in turn changes the ratio between various emission components at those energies and may potentially absorb the GeV excess by a simple renormalization of key components.

On a self-similar solution for the decay of turbulent bursts

1992 ◽  
Vol 3 (4) ◽  
pp. 319-341 ◽  
Author(s):  
S. P. Hastings ◽  
L. A. Peletier
Keyword(s):  
Asymptotic Behaviour ◽  
Physical Parameter ◽  
Dimensional Analysis ◽  
Existence And Uniqueness ◽  
The Self ◽  
Similar Solution ◽  
Self Similar Solution ◽  
Eigenvalue Parameter ◽  
Self Similar ◽  
Similar Solutions

We discuss the self-similar solutions of the second kind associated with the propagation of turbulent bursts in a fluid at rest. Such solutions involve an eigenvalue parameter μ, which cannot be determined from dimensional analysis. Existence and uniqueness are established and the dependence of μ on a physical parameter λ in the problem is studied: estimates are obtained and the asymptotic behaviour as λ → ∞ is established.

A Method for Identifying Parameters of Mechanical Joints

1990 ◽  
Vol 57 (2) ◽  
pp. 337-342 ◽  
Author(s):  
J. Wang ◽  
P. Sas
Keyword(s):  
Physical Parameter ◽  
Mechanical Systems ◽  
Degree Of Freedom ◽  
Modal Testing ◽  
Physical Parameters ◽  
Mechanical Joints ◽  
Joint Parameters

A method for identifying the physical parameters of joints in mechanical systems is presented. In the method, a multi-d.o.f. (degree-of-freedom) system is transformed into several single d.o.f. systems using selected eigenvectors. With the result from modal testing, each single d.o.f. system is used to solve for a pair of unknown physical parameters. For complicated cases where the exact eigenvector cannot be obtained, it will be proven that a particular physical parameter has a stationary value in the neighborhood of an eigenvector. Therefore, a good approximation for a joint physical parameter can be obtained by using an approximate eigenvector and the exact value for the joint parameters can be reached by carrying out this process in an iterative way.

Automating Well Log Correlation Workflow Using Soft Attention Convolutional Neural Networks

2021 ◽  
Author(s):  
Aria Abubakar ◽  
Mandar Kulkarni ◽  
Anisha Kaul
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Gamma Ray ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Well Log ◽  
Machine Learning Model ◽  
Well Correlation ◽  
Geological Formations

Abstract In the process of deriving the reservoir petrophysical properties of a basin, identifying the pay capability of wells by interpreting various geological formations is key. Currently, this process is facilitated and preceded by well log correlation, which involves petrophysicists and geologists examining multiple raw log measurements for the well in question, indicating geological markers of formation changes and correlating them with those of neighboring wells. As it may seem, this activity of picking markers of a well is performed manually and the process of ‘examining’ may be highly subjective, thus, prone to inconsistencies. In our work, we propose to automate the well correlation workflow by using a Soft- Attention Convolutional Neural Network to predict well markers. The machine learning algorithm is supervised by examples of manual marker picks and their corresponding occurrence in logs such as gamma-ray, resistivity and density. Our experiments have shown that, specifically, the attention mechanism allows the Convolutional Neural Network to look at relevant features or patterns in the log measurements that suggest a change in formation, making the machine learning model highly precise.

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