Well log is one of the most fundamental methods for reservoir characterization, in oil and gas industry, it is an essential method for geoscientist to acquire more knowledge about the condition below the surface by using physical properties of rocks.
WELL LOG : Types of Logs, The Bore Hole Image, Interpreting Geophysical Well Logs, applications, Production logs, Well Log Classification and Cataloging
well logging project report_ongc project studentknigh7
It briefs well logging basics for students of geophysics on well logging or partly on reservoir characterization. It can be good note book for summer ,winter training in well logging data analysis and open hole log interpretation
Introduction
Petrophysic of the rocks
It is the study of the physical and chemical properties of the rocks related to the pores and fluid distribution
Porosity, is ratio between volume of void to the total voids of the rock.
Permeability, is ability of a porous material to allow fluids to pass through it.
Electric, most of the sedimentary rocks don’t have conductivity.
Radiation, clay rocks have 40K, radiate alpha ray.
Hardness, it depends on the cementing material and thickness of the sediments.
WELL LOGGING
The systematic recording of rock properties and it’s fluid contents in wells being drilled or produced to obtain various petrophysical parameters and characteristics of down hole sequences (G.E Archie 1950).
The measurement versus depth or time, or both, of one or more physical properties in a well.
These methods are particularly good when surface outcrops are not available, but a direct sample of the rock is needed to be sure of the lithology.
A wide range of physical parameters can be measured.
In some cases, the measurements are not direct, it require interpretation by analogy or by correlating values between two or more logs run in the same hole.
Provide information on lithology, boundaries of formations and stratigraphic correlation.
Determine Porosity, Permeability, water, oil and gas saturation.
Reservoir modeling and Structural studies… etc.
Types of Well Logging
Logs can be classified into several types under different category
Permeability and lithology Logs
Gamma Ray log
Self Potential [SP] log
Caliber log
Porosity Logs
Density log
Sonic log
Neutron log
Electrical Logs
Resistivity Log
For contact : omerupto3@gmail.com
WELL LOG : Types of Logs, The Bore Hole Image, Interpreting Geophysical Well Logs, applications, Production logs, Well Log Classification and Cataloging
well logging project report_ongc project studentknigh7
It briefs well logging basics for students of geophysics on well logging or partly on reservoir characterization. It can be good note book for summer ,winter training in well logging data analysis and open hole log interpretation
Introduction
Petrophysic of the rocks
It is the study of the physical and chemical properties of the rocks related to the pores and fluid distribution
Porosity, is ratio between volume of void to the total voids of the rock.
Permeability, is ability of a porous material to allow fluids to pass through it.
Electric, most of the sedimentary rocks don’t have conductivity.
Radiation, clay rocks have 40K, radiate alpha ray.
Hardness, it depends on the cementing material and thickness of the sediments.
WELL LOGGING
The systematic recording of rock properties and it’s fluid contents in wells being drilled or produced to obtain various petrophysical parameters and characteristics of down hole sequences (G.E Archie 1950).
The measurement versus depth or time, or both, of one or more physical properties in a well.
These methods are particularly good when surface outcrops are not available, but a direct sample of the rock is needed to be sure of the lithology.
A wide range of physical parameters can be measured.
In some cases, the measurements are not direct, it require interpretation by analogy or by correlating values between two or more logs run in the same hole.
Provide information on lithology, boundaries of formations and stratigraphic correlation.
Determine Porosity, Permeability, water, oil and gas saturation.
Reservoir modeling and Structural studies… etc.
Types of Well Logging
Logs can be classified into several types under different category
Permeability and lithology Logs
Gamma Ray log
Self Potential [SP] log
Caliber log
Porosity Logs
Density log
Sonic log
Neutron log
Electrical Logs
Resistivity Log
For contact : omerupto3@gmail.com
Effect of Petrophysical Parameters on Water Saturation in Carbonate FormationIJERA Editor
Assessment of petrophysical parameters is very essential for reservoir engineers. Three techniques can be used to
predict reservoir properties: well logging, well testing, and core analysis. Cementation factors and saturation
exponents are crucial for calculation, and their values pose a pronounced effect on water saturation estimation. In
this study, a sensitivity analysis was performed to investigate the influence of cementation factor and saturation
exponent variation, as it applies to logs and core analysis, for use in water saturation estimates. Measurements of
water saturation resulting from these variations showed a maximum spread difference of around fifteen percent.
Advanced logging evaluation gas reservoir of Levantine basinFabio Brambilla
Experience gained in recent activity in the Levantine basin has allowed for the development of a formation evaluation strategy for accurate gas reservoirs description in this region. The proposed evaluation approach considers operational issues of deep water wells, challenging borehole conditions (high salinity mud, deep invasion) and other geological features of these clastic reservoirs and their fluids. Our case study highlights benefits of the integrated evaluation of new laterolog resistivity data together with 2D NMR inversion results optimized for a gas bearing reservoir. Furthermore borehole imaging logs are included in our evaluation approach. The recently developed multi laterolog tool has an advantage of four multiple depths of investigation. It provides a detailed high 1ft vertical resolution radial resistivity profile overcoming the deep invasion often present in these reservoirs. The NMR acquired in gas oriented acquisition mode exploits the multi-frequency capability of the logging device. Combined together multiple G•TE and multiple TW experiments contribute to robust determination of the T1 and T2 reservoir fluid properties. This acquisition sequence allows for continuous hydrocarbon typing applying the T1/T2 vs T2 2D maps method, which is practical for these reservoirs given the T1 contrast between gas and other fluids. Consequently we are able to perform accurate HI corrections and therefore improve the estimates of NMR permeability and saturations. Further in the workflow we compare NMR and Stoneley wave permeability’s and assess in details their differences. The geological study performed with the combination of simultaneously acquired ultrasonic and resistivity borehole images provides additional insight into the reservoir architectures, taking advantage during the analysis of the different logging responses of the petrophysical factors to acoustic and resistivity investigation for a detailed delineation of the productive beds. The advantages of this integrated approach are illustrated with field data examples.
Oil and gas reserves sensitivity to log evaluationPeter Cockcroft
A paper by Peter Cockcroft and John Owens about sensitivity of log analysis to oil and gas reserves estimations. Presented to to the Society of Core Analysts in 1990 by John Owens
Effects of shale volume distribution on the elastic properties of reserviors ...DR. RICHMOND IDEOZU
Shale volume (Vsh) estimation has been carried out on three selected reservoirs (Nan.1, Nan.2, and Nan.4) distributed across four wells (01, 03, 06, and 12) in Nantin Field, using petrophysical analysis and reservoir modeling techniques with a view to understanding the reservoir elastic properties. Materials utilized for this research work include: Well Log data (Gamma Ray Log, Resistivity Log, Sonic Log, Density Log, Neutron porosity log), and a 3-D Seismic volume were used for the study. Sand and shale were the prevalent lithologies in Nantin Field. Nan. 1 reservoir was thickest in Nantin well 12 (29.7ft), Nantin 2 reservoir was thickest in Nantin Well 12 (30.9ft) while Nantin 4 reservoir was thickest in Well 3 (72ft). Correlation well panel across the Field showed that Nantin 4 reservoir, was thicker than Nan 1 and Nan 2 Reservoir respectively. Normal and synthetic Faults were also mapped, the trapping system in the field includes anticlines in association with fault closures. The thicknesses and lateral extents of these reservoirs were delineated into three zones (1, 2, and 3) which were modeled appropriately. Petrophysical and some elasticity parameters such as Poisson ratio (PR), Acoustic Impedance (AI), and Reflectivity Coefficient (RC) were evaluated for the wells. The results from elasticity evaluation showed a high Poisson Ratio of 0.40 in Nantin 2 reservoir of Well 12 based on high shale volume distribution of 0.70 indicating high stress level and possible boundary to hydraulic fracture. The lowest Poisson Ratio was evaluated in Nantin reservoir of Well 1 with lowest shale volume of 0.18 which indicates weak zones and may not constrain a fracturing job. Results from Acoustic impedance showed a high AI value of 7994.3 in Nan 2 Reservoir compared to Nan.1 which has the least AI value of 7447.3 because of low shale volume. A higher Reflectivity Coefficient of 0.01 was recorded in Nan.2 reservoir indicating bright spot while a lower RC of -0.00023 was recorded in Nan.4 Reservoir indicating dim spot. Hydrocarbon volume estimate of the three reservoirs showed 163mmstb in Nan.1 reservoir, 169mmstb, in Nantin 2 reservoir and 115mmstb in Nan. 4 Reservoir. The reservoirs encountered were faulted and laterally extensive. Nantin 2 reservoir was more prolific with a STOIIP of 169 mmstb compared to Nan. 1 with a STOIP of 163 mmstb and Nantin.4 with a STOIP of 115 mmstb, because of its good petrophysical values, facies quality and low shale volume distributions.
Delineation of Hydrocarbon Bearing Reservoirs from Surface Seismic and Well L...IOSR Journals
Hydrocarbon reservoir has been delineated and their boundaries mapped using direct indicators from 3-D seismic and well log data from an oil field in Nembe creek, Niger Delta region. Well log signatures were employed to identify hydrocarbon bearing sands. Well to seismic correlation revealed that these reservoirs tied with direct hydrocarbon indicators on the seismic section. The results of the interpreted well logs revealed that the hydrocarbon interval in the area occurs between 6450ft to 6533ft for well A, 6449ft to 6537ft for well B and 6629ft to 6704ft for well C; which were delineated using the resistivity, water saturation and gamma ray logs. Cross plot analysis was carried out to validate the sensitivity of the rock attributes to reservoir saturation condition. Analysis of the extracted seismic attribute slices revealed HD5000 as hydrocarbon bearing reservoir.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
Phenomics assisted breeding in crop improvementIshaGoswami9
As the population is increasing and will reach about 9 billion upto 2050. Also due to climate change, it is difficult to meet the food requirement of such a large population. Facing the challenges presented by resource shortages, climate
change, and increasing global population, crop yield and quality need to be improved in a sustainable way over the coming decades. Genetic improvement by breeding is the best way to increase crop productivity. With the rapid progression of functional
genomics, an increasing number of crop genomes have been sequenced and dozens of genes influencing key agronomic traits have been identified. However, current genome sequence information has not been adequately exploited for understanding
the complex characteristics of multiple gene, owing to a lack of crop phenotypic data. Efficient, automatic, and accurate technologies and platforms that can capture phenotypic data that can
be linked to genomics information for crop improvement at all growth stages have become as important as genotyping. Thus,
high-throughput phenotyping has become the major bottleneck restricting crop breeding. Plant phenomics has been defined as the high-throughput, accurate acquisition and analysis of multi-dimensional phenotypes
during crop growing stages at the organism level, including the cell, tissue, organ, individual plant, plot, and field levels. With the rapid development of novel sensors, imaging technology,
and analysis methods, numerous infrastructure platforms have been developed for phenotyping.
Well log analysis for reservoir characterization aapg wiki
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Wiki Write-Off Entry
Student Chapter Universitas Gadjah Mada
Competition December 2014
Well log analysis for reservoir characterization
From AAPG Wiki
Well log is one of the most fundamental methods for reservoir characterization, in oil and gas
industry, it is an essential method for geoscientist to acquire more knowledge about the condition
below the surface by using physical properties of rocks. This method is very useful to detect
hydrocarbon bearing zone, calculate the hydrocarbon volume, and many others. Some
approaches are needed to characterize reservoir, by using well log data, the user may be able to
calculate:
1. shale volume (Vsh)
2. water saturation (Sw)
3. porosity (φ)
4. permeability (k)
5. elasticity (σ, AI, SI, etc.)
6. reflectivity coefficient (R)
7. other data that the user need
The interpretation of well log data must be done in several steps and it is not recommended for
the user to analyze them randomly because, the result might be a total error. Figure 1 shows the steps for reservoir characterization by using well
log data. Basically, there are two types of properties that will be used in reservoir characterization, they are petrophysics (shale volume, water
saturation, permeability, etc.) which are more geology-like and rock physics (elasticity, wave velocity, etc.) which are more geophysics-like.
Every properties are related each other, the relation between each properties is shown in figure 2, the author called it as the “fish diagram”.
There are many techniques to find a hydrocarbon bearing zone, the user may use RHOB-NPHI cross over (with some corrections), reflectivity
coefficient (just like in seismic interpretation), AI anomaly, etc. Every method has their own weaknesses, so it is a wise decision to use every
method to acquire the right result. There are so many kinds of modern logs, see table 1 for the information about the logs and also their uses.
Table 1 The functions of every log in petrophysical and rock physics properties calculation and analysis.
Name Uses
Gamma Ray (GR)
Lithology interpretation, shale volume calculation, calculate clay volume, permeability calculation, porosity
calculation, wave velocity calculation, etc.
Spontaneous Potential
(SP)
Lithology interpretation, Rw and Rwe calculation, detect permeable zone, etc.
Caliper (CALI) Detect permeable zone, locate a bad hole
Shallow Resistivity (LLS
and ILD)
Lithology interpretation, finding hydrocarbon bearing zone, calculate water saturation, etc.
Deep Resistivity (LLD and
ILD)
Lithology interpretation, finding hydrocarbon bearing zone, calculate water saturation, etc.
Density (RHOB)
Lithology interpretation, finding hydrocarbon bearing zone, porosity calculation, rock physics properties (AI, SI, σ,
etc.) calculation, etc.
Neutron Porosity (NPHI) Finding hydrocarbon bearing zone, porosity calculation, etc.
Sonic (DT) Porosity calculation, wave velocity calculation, rock physics properties (AI, SI, σ, etc.) calculation, etc.
Photoelectric (PEF) Mineral determination (for lithology interpretation) *not used in this article
Figure 1-Flowchart to analyze well logs that must be done to characterize an oil
or gas reservoir, the user should follow these steps in order to acquire the correct
result.
Figure 2-Fish diagram that shows the relation between petrophysical and elasticity
properties.
Contents
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Figure 3-The use of gamma ray log to determine the
lithology.[1]
1 Interpret the Lithology
2 Calculate the Shale Volume
3 Calculate the Porosity
4 Calculate the Water Saturation
5 Calculate the Permeability
6 Calculate the Elasticity
7 Reflectivity Coefficient
8 Case Study
8.1 Data
8.2 Lithology Interpretation
8.3 Petrophysical & Rock Physics Properties Analysis
9 Sources
10 References
Interpret the Lithology
The user will be able to interpret the lithology by using several logs, there are gamma ray, spontaneous potential,
resistivity, and density log. Basically, a formation with high gamma ray reading indicates that it is a shaly or shale,
when the low gamma ray reading indicates a clean formation (sand, carbonate, evaporite, etc.), lithology
interpretation is very important in reservoir characterization because, if the lithology interpretation is already wrong,
the other steps such as porosity and water saturation calculation will be a total mess.
Calculate the Shale Volume
This second step could be done by using gamma ray log, Larionov (1969) proposed two formulas to calculate the
shale volume, those formulas are:
Larionov (1969) for tertiary rocks:
Larionov (1969) for older rocks:
where IGR is the gamma ray index, Vsh is the shale volume, GRlog is the gamma ray reading, GRmax is the maximum gamma ray reading, and GRmin is the minimum
gamma ray reading. Calculating shale volume is an important thing to do because, it can be useful to calculate the water saturation, if the reservoir has shale within its body
(shaly) such as in delta, that reservoir may has higher water saturation because, shale has the ability to bound together with water which will increase the water saturation.
Shale volume could also be used as an indicator of zone of interest or not, many users usually will not classify a formation with high shale volume as a reservoir because of its
low permeability.
Calculate the Porosity
Porosity is the void or space inside the rock, they are very useful to store fluids such as oil, gas, and water, they are also able to transmit those fluids to a place with lower
pressure (probably surface) if they are permeable (see permeability in section 5). Porosity calculation is the third step of well log analysis and it could only be done correctly
if the first step (lithology interpretation) is correct. There are many methods that can be used to calculate the porosity, the user may use density log, sonic log, neutron log, or
combination between them, but the most common one is neutron-density log combination. The user may use the formulas below to calculate the neutron-density porosity:
for non-gas reservoir, or
for gas reservoir
φd value:
where ρmatrix is the matrix density (the value depends on the lithology, see table 2 for the value reference), ρfluid is the fluid density (see table 2 for the value reference),
ρlog is the density log reading, φd is the density-derived porosity, φn is the neutron porosity (from neutron log reading), and φnd is the neutron-density porosity. If the
lithology interpretation has been wrong from the start, the density-derived porosity will also show the wrong result which means that the neutron-density porosity will also be
wrong, so the ability to interpret the lithology correctly is an important asset for the user.
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Table 2-Matrix density and fluid density reference
table (Halliburton, 1991) with some additions.
Lithology Value (gr/cm3) Fluid Value (gr/cm3)
Sandstone 2.644 Fresh Water 1.0
Limestone 2.710 Salt Water 1.15
Dolomite 2.877 Methane 0.423
Anhydrite 2.960 Oil 0.8
Salt 2.040
Calculate the Water Saturation
There are so many methods to calculate water saturation, the user may use Archie’s,[2] Simandoux’s (1963), etc. which will use different formula for every one of them, but
in this article, the author will use Simandoux’s (1963) method, to calculate the water saturation by using this method, the user will need to use the following formula:
where Rt is the true resistivity of the formation (deep resistivity), Rw is the formation water resistivity, Vsh is the shale volume, Rsh is the resistivity of shale, Rwe is the
formation water resistivity (without thermal effect), is the bottom hole temperature, Rmf is the mud filtrate resistivity, SP is the spontaneous potential log reading, F is
the formation volume factor, a is the tortuosity factor, m is the cementation exponent, φ is the porosity, and Sw is the water saturation. To acquire the value of a and m, the
user will need to create a pickett plot, but according to Asquith,[3] the reference value is shown in table 3.
Table 3-Tortuosity factor (a) and cementation exponent (m) reference
table.[3]
Lithology a (tortuosity factor) m (cementation exponent)
Carbonate 1.0 2.0
Consolidated Sandstone 0.81 2.0
Unconsolidated Sandstone 0.62 2.15
Average Sand 1.45 1.54
Shaly Sand 1.65 1.33
Calcareous Sand 1.45 1.70
Carbonate (Carothers, 1986) 0.85 2.14
Pliocene Sand 2.45 1.08
Miocene Sand 1.97 1.29
Clean, granular formation 1.0 φ(2.05-φ)
Calculate the Permeability
Defined as the rock’s ability to transmit fluid, higher permeability shows that the rock is able to transmit fluid easiliy and it means that the more hydrocarbon that can be
produced daily, it is affected by many factors, such as shale volume, effective porosity, and many other else. There are so many methods that can be used to calculate the
permeability, but in this article, the author will use Coates’s (1981) method, the formula is listed below:
where k is the permeability, φ is the porosity, and Swirr is the irreducible water saturation (the author use 0.3 as the assumption for this variable). From the formula above,
we can conclude that if the irreducible water saturation is at 1, then the permeability will be zero.
Calculate the Elasticity
There are so many kinds of elastic properties of a rock, there are Acoustic Impedance (AI), Shear Impedance (SI), Poisson Ratio (σ), etc. and most of them depend on the
wave velocity and density.
where Vp is the P-Wave velocity and Vs is the S-Wave velocity. According to Castagna et al,[4] Vp and Vs can be calculated by using this formula:
BHT
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where φs is the sonic-derived porosity, Vclay is the clay volume, Δtlog is the sonic log reading (DT), Δtmatrix is the matrix transit time (see table 4 for reference value), and
Δtfluid is the fluid transit time (see table 4 for reference value). Theoretically, a formation with high density will has lower transit time (Δtlog) which will cause the seismic
wave to travel faster in that formation. An anomaly in density and sonic log (Δt) in a formation may indicates the presence of fluids in that formation (see section 9).
Table 4-Matrix and fluid transit time reference table.[5]
Lithology Value (μs/ft) Fluid Value (μs/ft)
Consolidated Sandstone 55.5 Fresh Water 218
Unconsolidated Sandstone 51.5 Salt Water 189
Limestone 47.5 Oil 238
Dolomite 43.5 Methane 626
Anhydrite 50.0
Gypsum 52.0
Salt 67.0
Reflectivity Coefficient
The reflectivity coefficient could be derived from density and sonic log then the user may complete this method simply by using the AI difference between every formation
which shows the reflectivity coefficient (R) which shows the rock’s ability to reflect the seismic wave to the surface, the formula is listed below:
where ρ1 is the density of the rock in the first formation, ρ2 is the density of the rock in the second formation, Vp1 is the P-Wave velocity in the first formation, and Vp2 is
the P-Wave velocity in the second formation. The reflectivity coefficient is very related with seismic, it represents how good is the rock’s ability to reflect seismic wave, if the
reflectivity is high, then more seismic wave will be reflected back to the surface which will be shown by the presence of bright spot, but if the reflectivity is very low, it is
called dim spot, both of them could be used as hydrocarbon indicator.
Case Study
Data
The author used the well data from South Barrow 18’s well (downloaded from http://energy.cr.usgs.gov/OF00-200/WELLS/SBAR18/LAS/SB18.LAS), the data are
shown in figure 4A.
Lithology Interpretation
By using gamma ray (GR), spontaneous potential (SP), resistivity (LLD and LLS), and density log (RHOB), the user will able to interpret the lithology (figure 5A), there are
4 lithology in this well, they are sandstone, shaly sandstone, sandy shale, and shale. There is also a bad hole here (figure 4B), shown by the caliper log’s value that is very big
which indicates a heavily weathered layer, the user should not try to interpret or analyze logs in a bad hole, because the well data may contain error which is caused by the
inability of the instruments to reach the formation, so instead of measuring the formation’s properties, they are measuring the empty zone so the data cannot be trusted
anymore.
By using gamma ray log (see figure 3), the user will be able to differentiate the shale (or shaly) or non-shale formation. With the help of spontaneous potential log, the user
could give some corrections to the gamma ray log, shale usually has positive SP log reading, when clean (sand, etc.) formation has very negative SP log reading, shaly
formation lies between them (not too negative). Resistivity log will also help the user to differentiate the lithology, sandstone or carbonates have high resistivity, the average
resistivity value in this well is around 8 Ωm, because of that, formation with higher resistivity than that can be classified as sandstone (if the gamma ray value is low to
medium) or carbonates (if the gamma ray value is very low). The last one is the density log (RHOB), with this log, the user could differentiate if the formation is tight or not,
also with this log, the user could differentiate between shale-shaly-non shale formation, shale usually has low density when non-shale formation usually has density higher
than shale, shaly formation lies between them, if the formation has a very high density log reading, the user may classify that formation as a “tight” formation, when its gamma
ray log reading is around 30-50, we may call it as a “tight sandstone” formation, or if the gamma ray log reading is very log (usually below 15 ), the resistivity and
density log reading is very high, it could be an anhydrite which is a good cap rock in petroleum system. Table 5 shows the characteristics of some rocks that can be used to
differentiate the lithology, but please remember that the reference value is relatively different for every well, so the user should not confused with this issue.
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Table 5-Petrophysical properties reference of some sedimentary rocks.
Lithology Gamma Ray ( ) Spontaneous Potential (mV) Resistivity (Ωm) [If shale resistivity is 8] Density (gr/cm3)
Sandstone 30 – 50 Varies, very negative 10+ 2.4 – 2.8
Shaly-sandstone 50 – 75 Varies, negative 8 < Resistivity < 10 Around 2.4
Sandy-shale 75 – 90 Varies, negative Around 8 Around 2.3
Shale Higher than 90 Higher than 0 8 Around 2.3
Anhydrite Below 15 - Very high, up to 100+ Up to 2.9
Coal Varies - Varies Varies, could be 1.7 – 2.2
Crystalline Below 30 - Very high, up to 150+ Up to 2.9
Limestone 20 – 30 - Very high, up to 100+ 2.3 – 2.7
Petrophysical & Rock Physics Properties Analysis
Based on the formulas in section 2-6, the author has done some calculations about the well log data (see figure 6 and 7), from figure 6, we can see the petrophysical
properties (Vshale, Sw, φ, and k) and from figure 7 we can see the rock physics properties (AI, SI, Vp/Vs, and σ). Based on the data, we can see that the reservoirs in this
well (see figure 9A or B) have low shale volume content (compare figure 9A or 9B with figure 6), which indicates that those reservoirs should have higher permeability than
the other formations, those reservoirs are also have low water saturation (see figure 6) which indicates a high amount of hydrocarbon proven by the velocity ratio vs AI
crossplot (figure 11) and if we correlate it with the porosity, we can conclude that those reservoirs have good porosity and low water saturation which make them good
reservoirs with high hydrocarbon content.
To look for reservoir by using rock physics method, the user can do it by making a crossplot between the Depth vs AI (figure 10A and 10B), theoretically, the AI of every
rock should increase as it deposited in a deeper place, and by quick looking into the anomaly, the user can say that it is a zone of interest but some corrections with the
other data must be done to get the more accurate result. From figure 8 we can observe the reflectivity coefficient which mainly talks about density and wave velocity of
every formation, the user may use them as hydrocarbon detector, the formation with very negative and very positive R value shows that there is a very big density and wave
velocity difference between the upper and lower formation which can be used to detect hydrocarbon (direct hydrocarbon indicator), after that, we should do some
correction by using gamma ray, resistivity, and caliper log (figure 9A), the user should also has the knowledge about the bit size, the blue line in figure 9A shows that not
every very negative or very positive R value represents dim spot or bright spot, caliper log and bit size data shows that there is a bad hole there so that the R value in 1930-
1960ft is not a dim spot or bright spot, but it is just an error which is caused by the bad hole, but the other direct hydrocarbon indicator (2050-2080ft) is an oil reservoir
(reservoir A) and the other reservoir (reservoir B) which lies from 2120ft is a gas reservoir, both of them are sandstone reservoirs (see figure 5B).
Based from petrophysics point of view, a reservoir usually has lower density than the same lithology that surrounds the reservoir, low gamma ray, and high resistivity
response (figure 9B). First, the density, a formation with low density usually has high porosity which is needed to store the hydrocarbon fluid. Second, the gamma ray
response, the usual reservoir are sandstone, carbonates, or shaly-sandstone, a formation with very high gamma ray response usually contains more shale than the one with
low gamma ray response, shale will block the interconnected pores which will reduce the effective porosity and permeability and that will prevent the hydrocarbon fluid to
be stored inside the pores. The last one is resistivity, oil and gas has higher resistivity than water, so by looking onto the well log data, a zone of interest (where cross over
between RHOB-NPHI is present) is not always a reservoir if the resistivity is low.
Figure 4A-Well logs that will be used for the
interpretation of South Barrow 18 well.
Figure 4B-Determining a bad hole based on bit size and
caliper log response.
Figure 5A-Lithology interpretation of South Barrow 18
well, the author use the combination of GR-SP-
Resistivity-RHOB logs to interpret the lithology
(NPHI log is present here to aid the author in locating
a hydrocarbon bearing zone.
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Figure 5B-Reservoir A (upper) lithology interpretation.
Figure 6-The calculation result of Vshale, Sw, φ, and k in South Barrow
18 well.
Figure 7-The calculation result of AI, SI, Vp/Vs, and σ in South
Barrow 18 well.
Figure 8-The result of
reflectivity coefficient
calculation, a very high or
very low R value is usually
caused by the presence of
hydrocarbon or big
difference of density and
wave velocity between
two formations.
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Figure 9A-The relation between log data and reflectivity coefficient, from this figure, we can see that the detection zone of interest (red and black
circle) can also be done by looking onto the R, a formation that contains hydrocarbon usually has very low or very high R (purple lines).
Figure 9B-The technique to detect hydrocarbon bearing zone by using RHOB-NPHI, resistivity, and gamma ray log.
Figure 10A-Crossplot between depth and acoustic impedance (AI). Figure 10B-Crossplot between depth and acoustic impedance (AI), the black circles
show the acoustic impedance anomaly.
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Figure 11-Crossplot between velocity ratio (Vp/Vs) and acoustic impedance (AI),
by using this crossplot, we can determine the formation orientation whether it
contains hydrocarbon or not, how about the pressure, etc.
Sources
Ijasan, O., Torres-Verdín, C., & Preeg, W. E. (2013). Interpretation of porosity and fluid constituents from well logs using an interactive neutron-density matrix
scale. Interpretation, 1(2), T143-T155.
Tiab, D., & Donaldson, E. C. (2011). Petrophysics: theory and practice of measuring reservoir rock and fluid transport properties. Gulf professional publishing.
Jorgensen, D. G. (1989). Using geophysical logs to estimate porosity, water resistivity, and intrinsic permeability.
Doveton, J. H. (1986). Log analysis of subsurface geology: Concepts and computer methods.
Ellis, D. V., & Singer, J. M. (2007). Well logging for earth scientists (Vol. 692). Dordrecht: Springer.
Muammar, R. (2014). Application of Fluid Mechanics to Determine Oil and Gas Reservoir’s Petrophysical Properties By Using Well Log Data.
Balan, B., Mohaghegh, S., & Ameri, S. (1995). State-of-the-art in permeability determination from well log data: part 1-A comparative study, model development.
paper SPE, 30978, 17-21.
References
1. ↑ Railsback (2011). Characteristics of wireline well logs in the petroleum industry.
2. ↑ Archie, G. E. (1950). Introduction to petrophysics of reservoir rocks. AAPG Bulletin, 34(5), 943-961.
3. ↑ 3.0 3.1 Asquith, G. B., Krygowski, D., & Gibson, C. R. (2004). Basic well log analysis(Vol. 16). Tulsa: American Association of Petroleum Geologists.
4. ↑ Castagna, J. P., Batzle, M. L., & Eastwood, R. L. (1985). Relationships between compressional-wave and shear-wave velocities in clastic silicate
rocks.Geophysics, 50(4), 571-581.
5. ↑ Schlumberger Limited. (1984). Schlumberger log interpretation charts. Schlumberger.
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