The petrophysicists define two porosities, the total porosity (PHIT) that includes isolated pores and the space occupied by clay-bound water and the effective porosity (PHIE) which excludes isolated pores and pore volume occupied by water adsorbed on clay minerals. Reservoirs with a high formation water salinity and a low clay mineral content are called Archie reservoirs, where the effective and total porosities are essentially the same, because there are negligible clay bound water effects. Otherwise, they are called non-Archie reservoirs, because there can be a significant clay bound water saturation. Non-Archie reservoirs can be evaluated in terms of either effective or total porosity. Different water saturation (Sw) equations use different porosities. In clean formations the Archie equation can be used, as it is assumed PHIT is equal to PHIE. In shaly formations the water saturation equation must correct for the shales’ excess conductivity. Waxman-Smits, Juhasz and Dual Water use PHIT, whereas Simandoux and Indonesia use PHIE. Using PHIT or PHIE should give the same hydrocarbon in place (HCIP). The question is which is most useful and gives the most accurate determination of HCIP. Density Porosity (PHID) is not PHIT and represents a porosity somewhere between PHIT and PHIE. This is because the matrix (Rhoma) and fluid (Rhofl) densities used for PHID are picked for clean formations and may be different in the shales. Consequently, it is necessary to calibrate PHID to the core porosity using log to core regression. Without core, it is necessary to first calculate PHIE using an appropriate density response equation. To calculate PHIT from PHIE requires knowledge of the shale porosity (PHISH) due to clay bound water. This can be determined from Qv (cation exchange capacity per unit total pore volume) using a unique algorithm or from a specially designed bulk density vs. neutron porosity crossplot. Using these techniques, it is recommended that the petrophysicist calculate both PHIT and PHIE. Finally, it is essential that a shaly sand water saturation equation be selected to correct for the shale’s excess conductivity. This equation can be confirmed using a bespoke technique where
‘unlimited’ Sw is plotted against the volume of shale (Vsh) in the water leg.
Why we need a Water Saturation vs. Height function for reservoir modelling.
Definitions: Free-Water-Level, HWC, Net, Swirr
Several case studies showing applications to reservoir modelling.
To determine a field’s hydrocarbon in place, it is necessary to model the distribution of hydrocarbon and water
throughout the reservoir. A water saturation vs. height (SwH) function provides this for the reservoir model. A
good SwH function ensures the three independent sources of fluid distribution data are consistent. These being
the core, formation pressure and electrical log data. The SwH function must be simple to apply, especially in
reservoirs where it is difficult to map permeability or where there appears to be multiple contacts. It must
accurately upscale the log and core derived water saturations to the reservoir model cell sizes.
This presentation clarifies the, often misunderstood, definitions for the free-water-level (FWL), transition zone
and irreducible water saturation. Using capillary pressure theory and the concept of fractals, a convincing SwH
function is derived from first principles. The derivation is simpler than with classical functions as there is no
porosity banding. Several case studies are presented showing the excellent match between the function and
well data. The function makes an accurate prediction of water saturations, even in wells where the resistivity
log was not run, due to well conditions. Logs and core data from eleven fields, with vastly different porosity and
permeability characteristics, depositional environments, and geological age, are compared. These
demonstrates how this SwH function is independent of permeability and litho-facies type and accurately
describes the reservoir fluid distribution.
The function determines the free water level, the hydrocarbon to water contact (HWC), net reservoir cut-off,
the irreducible water saturation, and the shape of the transition zone for the reservoir model. The function
provides a simple way to quality control electrical log and core data and justifies using core plug sized samples
to model water saturations on the reservoir scale. The presentation describes how the function has been used
to predict fluid contacts in wells where they are unclear, or where the contact is below the total depth of the
well. As the function uses the FWL as its base, it explains the apparently varying HWC in some fields and how
low porosity reservoirs can be fully water saturated for hundreds of feet above the FWL.
This simple convincing function calculates water saturation as a function of the height above the free water level
and the bulk volume of water and is independent of the porosity and permeability of the reservoir. It was voted
the best paper at the 1993 SPWLA Symposium in Calgary.
Why we need a Water Saturation vs. Height function for reservoir modelling.
Definitions: Free-Water-Level, HWC, Net, Swirr
Several case studies showing applications to reservoir modelling.
To determine a field’s hydrocarbon in place, it is necessary to model the distribution of hydrocarbon and water
throughout the reservoir. A water saturation vs. height (SwH) function provides this for the reservoir model. A
good SwH function ensures the three independent sources of fluid distribution data are consistent. These being
the core, formation pressure and electrical log data. The SwH function must be simple to apply, especially in
reservoirs where it is difficult to map permeability or where there appears to be multiple contacts. It must
accurately upscale the log and core derived water saturations to the reservoir model cell sizes.
This presentation clarifies the, often misunderstood, definitions for the free-water-level (FWL), transition zone
and irreducible water saturation. Using capillary pressure theory and the concept of fractals, a convincing SwH
function is derived from first principles. The derivation is simpler than with classical functions as there is no
porosity banding. Several case studies are presented showing the excellent match between the function and
well data. The function makes an accurate prediction of water saturations, even in wells where the resistivity
log was not run, due to well conditions. Logs and core data from eleven fields, with vastly different porosity and
permeability characteristics, depositional environments, and geological age, are compared. These
demonstrates how this SwH function is independent of permeability and litho-facies type and accurately
describes the reservoir fluid distribution.
The function determines the free water level, the hydrocarbon to water contact (HWC), net reservoir cut-off,
the irreducible water saturation, and the shape of the transition zone for the reservoir model. The function
provides a simple way to quality control electrical log and core data and justifies using core plug sized samples
to model water saturations on the reservoir scale. The presentation describes how the function has been used
to predict fluid contacts in wells where they are unclear, or where the contact is below the total depth of the
well. As the function uses the FWL as its base, it explains the apparently varying HWC in some fields and how
low porosity reservoirs can be fully water saturated for hundreds of feet above the FWL.
This simple convincing function calculates water saturation as a function of the height above the free water level
and the bulk volume of water and is independent of the porosity and permeability of the reservoir. It was voted
the best paper at the 1993 SPWLA Symposium in Calgary.
What is the different between the net pay and resrvoir thicknessStudent
Prepared by Yasir Albeatiy
Contact me with information below:
E-Mail: yasiralbeatiy2015@gmail.com
Phone No. + Whatsapp : +9647828319225
Facebook Page: www.facebook.com/petroleumengineeringz
In order to determine a field’s hydrocarbon in place it is necessary to model the distribution of fluids throughout the reservoir. A water saturation vs. height (Swh) function provides this for the reservoir model. A good Swh function ensures the three independent sources of fluid distribution data are consistent. These being the core, formation pressure and electrical log data. The Swh function must be simple to apply, especially in reservoirs where it is difficult to map permeability or where there appears to be multiple contacts. It must accurately upscale the log and core derived water saturations to the reservoir model cell sizes.
This presentation clarifies the often misunderstood definitions for the free-water-level, transition zone and irreducible water saturation. Using capillary pressure theory and the concept of fractals, a practical Swh function is derived. Logs and core data from eleven fields, with very different porosity and permeability characteristics, depositional environments and geological age are compared. This study demonstrated how this Swh function is independent of permeability and litho-facies type and accurately describes the reservoir fluid distribution.
The shape of the Swh function shows that of the transition zone is related more to pore geometry rather than porosity or permeability alone. Consequently, this Swh function gives insights into a reservoir’s quality as determined by its pore architecture. A number of case studies are presented showing the excellent match between the function and well data. The function makes an accurate prediction of water saturations even in wells where the resistivity log was not run due to well conditions. The function defines the free water level, the hydrocarbon to water contact, net reservoir and the irreducible water saturation for the reservoir model. The fractal function provides a simple way to quality control electrical log and core data and justifies using core plug sized samples to model water saturations on the reservoir scale.
What is the different between the net pay and resrvoir thicknessStudent
Prepared by Yasir Albeatiy
Contact me with information below:
E-Mail: yasiralbeatiy2015@gmail.com
Phone No. + Whatsapp : +9647828319225
Facebook Page: www.facebook.com/petroleumengineeringz
In order to determine a field’s hydrocarbon in place it is necessary to model the distribution of fluids throughout the reservoir. A water saturation vs. height (Swh) function provides this for the reservoir model. A good Swh function ensures the three independent sources of fluid distribution data are consistent. These being the core, formation pressure and electrical log data. The Swh function must be simple to apply, especially in reservoirs where it is difficult to map permeability or where there appears to be multiple contacts. It must accurately upscale the log and core derived water saturations to the reservoir model cell sizes.
This presentation clarifies the often misunderstood definitions for the free-water-level, transition zone and irreducible water saturation. Using capillary pressure theory and the concept of fractals, a practical Swh function is derived. Logs and core data from eleven fields, with very different porosity and permeability characteristics, depositional environments and geological age are compared. This study demonstrated how this Swh function is independent of permeability and litho-facies type and accurately describes the reservoir fluid distribution.
The shape of the Swh function shows that of the transition zone is related more to pore geometry rather than porosity or permeability alone. Consequently, this Swh function gives insights into a reservoir’s quality as determined by its pore architecture. A number of case studies are presented showing the excellent match between the function and well data. The function makes an accurate prediction of water saturations even in wells where the resistivity log was not run due to well conditions. The function defines the free water level, the hydrocarbon to water contact, net reservoir and the irreducible water saturation for the reservoir model. The fractal function provides a simple way to quality control electrical log and core data and justifies using core plug sized samples to model water saturations on the reservoir scale.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
2. Outline
• The difference between shale and clays
• Total and effective porosity defined
• Calculating porosity from core analysis
• Calculating porosity from electrical logs
• The difference between net pay and net reservoir
4. The Fog of Confusion - Clays and Shale*
• Clays and Shales mean different things to geologists, reservoir engineers
and petrophysicists
• Some petrophysicists use the terms clay and shale interchangeably
- clearly this is wrong
• Shale is composed of clay and silt
• When computing porosity we need to account for the clay minerals,
because their density is often different from the matrix density
• The clays adsorb water on their surfaces
- If the mineral chemically holds water on its surface it is a clay
• When computing water saturation we need to account for the excess
conductivity due to the clay minerals in the shales
• Petrophysicists define clay in such a way to compute the most accurate
porosity and water saturation
* from Spooner
5. • PHIT - The total porosity includes isolated pores and the space occupied by
clay-bound water*
• PHIE - Effective porosity excludes isolated pores and pore volume occupied
by water adsorbed on clay minerals*
• PHIT = PHIE + Clay Bound Water
Porosity - Petrophysicist’s definition
Matrix Silt Clay
Clay
Bound
Water
Hydrocarbon
Capillary
Bound
Water
F Total
F Core
F Core (Humidity dried)
F Effective
V Shale
V Clay
*Schlumberger Oilfield Glossary
Isolated
Pores
Isolated pores can be neglected in most clastic formations
Unit volume
of reservoir
6. Total and Effective Porosities
• PHIT is the total reservoir rock containing all fluids
– Hydrocarbon, Capillary Bound Water, Clay Bound Water
• PHIE - reservoir rock containing Hydrocarbon, Capillary Bound Water
– Capillary Bound Water = Bulk Volume of Water (BVW)
• Net Reservoir is the reservoir rock capable of storing hydrocarbon
F Total
F Effective
Matrix
Shale
Hydrocarbon
Capillary
Bound
Water
Clay
Bound
Water
Net Reservoir
7. High Sw
• Net Sand removes the shaly intervals
• Net Reservoir removes the low porosity intervals
• Net Pay removes the intervals of high water saturation
Net Pay and Net Reservoir
8. The Difference between Net Reservoir and Net Pay
• Net Reservoir
– The portion of reservoir rock which is capable of storing hydrocarbon
– Required for upscaling and reservoir modelling
– Relatively easy to pick
• Net Pay
– “The portion of reservoir rock which will produce commercial quantities of
hydrocarbon”- SPWLA
– or The portion of reservoir rock which will produce or help support production of
hydrocarbon over field development timescales
– Useful to help select perforation intervals
– More difficult to pick
9. Net Pay
• Usually defined using a Sw and/or permeability cutoff
• But it doesn’t include:
– The ratio of horizontal to vertical permeability (Kh/Kv)
– Standoff distance from the FWL
– Shape of the transition zone
– Gas and water drive
– Draw down
– Water cut
– Fractures
• Most of hydrocarbon above the FWL is potentially producible
• The amount of hydrocarbon produced depends on how hard we try
• Is Net Pay therefore a function of the oil price?
• Net Pay is difficult to define
10. Net Reservoir
• Net Reservoir is much easier to define than
Net Pay
– As it is defined as the portion of reservoir rock
which is capable of storing hydrocarbon
• Knowledge of Net Reservoir is essential for:
– Upscaling for reservoir averages
– Reservoir modelling
• Net Reservoir is used to calculate Net/Gross
11. Archie vs. Non-Archie Reservoirs
• Reservoirs with a high formation water
salinity and a low clay mineral content are
Archie reservoirs, where the effective and
total porosities are essentially the same,
because there are negligible clay bound
water effects
• Otherwise, they are non-Archie
reservoirs, because there can be a
significant clay bound water saturation
• Non-Archie reservoirs can be evaluated in
terms of either effective or total porosity
*from Worthington
12. Total and Effective Water Saturation
Solids
(Matrix, Silt, Clay)
Clay
Bound
Water
Hydrocarbon
Capillary
Bound
Water
F Total
F Effective
F Effective =
+
+ + +
F Total =
+
+ + +
+
Sw Effective =
+
Sw Total =
+
+ +
Unit volume
of reservoir
SWE is the % of capillary
bound water in the
effective porosity
SWT is the % of capillary
and clay water in the
total porosity
13. Where Total Porosity and Effective Porosity are used
• Different water saturation equations use different porosities:
– Archie equation assumes clean formation PHIT = PHIE
– Waxman-Smits, Juhasz and Dual Water use PHIT
– Simandoux and Indonesia use PHIE
• Shaly sand water saturation equations correct for the shales’ excess conductivity
Capillary Bound Water
(function of salinity)
Clay Bound Water
(shale excess conductivity)
+ -
V
V
+
Total Formation Conductivity (1/Rt)
=
14. Should petrophysicists use Total or Effective Porosity?
• Using PHIT or PHIE should give you the same hydrocarbon in place (HCIP)
• The question is which is most useful and gives the most accurate
determination of HCIP
15. Porosity from core
Matrix
Reservoir Fluids
(Hydrocarbon, Capillary Bound
Water, Mud filtrate etc.)
PHIT
PHIE
Silt Clay
Clay
Bound
Water
F Core
PHID
• Density Porosity PHID is not PHIT
– PHID is somewhere between PHIT and PHIE
– Rhoma and Rhofl are for clean formation and may be different in the shales
– It is necessary to calibrate PHID to the Core Porosity
• PHID = Rhoma - Rhob
Rhoma - Rhofl
Where:
PHID Density derived porosity
Rhob Measured bulk density
Rhoma Matrix density
Rhofl Fluid density
PHIN
16. Porosity from density log vs. core regression
• Density Porosity PHID is not PHIT
Core Porosity PHIT (v/v)
Density
Porosity
PHID
(v/v)
– PHID is somewhere between PHIT and PHIE
– It is therefore necessary to calibrate PHID
to the Core Porosity
• PHIT = PHID * Constant
• PHIE = PHIT(1-0.6425+0.22)Qv
√S
where:
S = salinity in g/l NaCl
Qv = CEC/PV (meq/ml)
(from Hill, Shirley & Klein)
or
• PHIE = PHIT – VSH*PHISH
where:
PHISH = Shale porosity
(See later slide)
17. Porosity from electrical logs
• Density Porosity is not PHIT
– Somewhere between PHIT and PHIE
– Without core it is necessary to calculate PHIE before PHIT
• The density response equation:
• Rhob = ( 1 - PHIE - VSH ) * Rhoma + VSH * Rhosh + PHIE * Rhofl)
Where:
Rhob Measured bulk density
Rhoma Matrix density
Rhosh Shale density
Matrix
(Rhoma)
Reservoir Fluids
(Rhofl)
PHIT
Rhob
PHIE
VSH
1-PHIE-VSH
Silt Clay
Clay
Bound
Water
Rhofl Fluid density
PHIE Effective porosity
VSH Volume of Shale
Can be
modified
for Sxo
Unit volume
of reservoir
18. Porosity from electrical logs
Solving for PHIE:
PHIE = Rhoma – Rhob - VSH * Rhoma - Rhosh
Rhoma - Rhofl Rhoma – Rhofl
PHIT is then calculated from PHIE
PHIT = PHIE + VSH * PHISH
Where:
PHISH = Shale porosity
Matrix
(Rhoma)
Reservoir Fluids
(Rhofl)
PHIT
Rhob
PHIE
VSH
1-PHIE-VSH
Silt Clay
Clay
Bound
Water
19. Calculation of shale porosity PHISH
PHISH = Shale porosity due to the Clay Bound Water
• PHISH is often assumed to be 10% if the shale contains an equal mixture of
illite, chlorite and kaolinite clay minerals
– but only for non-smectite formations
• PHISH can be calculated from:
PHISH = Rhodsh - Rhosh
Rhodsh - Rhow
Where:
Rhodsh Dry shale density
Rhosh Shale density
Rhow Shale water density
• The dry shale point, Rhodsh does not exist on the logs, as they are always
wet insitu
• Rhodsh can be determined from the density vs. neutron porosity crossplot
20. Calculation of shale porosity PHISH
Density
Porosity
(p.u)
0 Neutron Porosity (p.u) 100
-20
0
100
Dry
Clay
Wet
Clay
Dry
Shale
Matrix
Water
Wet
Shale
PHISH = Rhodsh - Rhosh
Rhodsh – Rhow
• The dry shale point, Rhodsh does not exist
on the logs, as they are always wet insitu
• The dry shale density can be determined
from density vs. neutron porosity crossplot
• Dry clay density can be inferred from
knowledge of the clay mineralogy
• The wet shale point is at the end of the data
boomerang
• The dry shale is the extrapolation of the
shale line to the dry line
Colour
Gamma-Ray
21. Shaly Sand Water Saturation Equations
• These must correct for the shales’ excess conductivity
Total Formation Conductivity (1/Rt)
Capillary Bound Water
(function of salinity)
Clay Bound Water
(shale excess conductivity)
+ -
V
V
+
=
22. Confirmation of the Shaly Sand Water Saturation Equation
• The shaly sand water saturation
equation must correct for the
excess conductivity of the shale
• Plot unlimited Sw vs. Vsh in the
water leg
– Essential crossplot
• Sw should average 1 as shaliness
increases
– Trend should be vertical
• Rw confirmation in clean intervals
Water Saturation (v/v)
Volume
of
Shale
(v/v)
NB:
Only Water
Leg data
23. Conclusions
• Petrophysics should calculate both PHIT and PHIE
• Density Porosity PHID is not PHIT
– The density derived porosity is between PHIT and PHIE
• If core data is available calculate PHIT, then PHIE
• If no core data is available calculate PHIE, then PHIT