1) The document discusses integrating seismic data and uncertainties into facies models for 3D reservoir modeling. It focuses on a case study of the Snorre oil field in the North Sea.
2) A key challenge is the non-unique relationship between seismic amplitudes and geology, making facies prediction a multivariate problem. Seismic data also has uncertainties from noise, acquisition/processing, and imperfect physical models.
3) A Bayesian geostatistical inversion technique is used to estimate elastic properties like P-wave velocity from seismic data while accounting for data and prior uncertainties. This provides facies probabilities to condition the facies model.
Quantitative and qualitative seismic attributes interpretationmohamed Shihata
Seismic attribute is the only way that can enable interpreter to understand seismic data very well and generate new view for his model, but there are hundreds of seismic attributes and there are many classes that make interpreters afraid of using new thing so in this course explain both theoretical and application for each one and try to generate workflow to help interpretation for different geological environment.
In this course, we will gain an intuitive understanding of the kinds of seismic features that can be identified by 3-D seismic attributes, the sensitivity of seismic attributes to seismic acquisition and processing, and of how ‘independent’ seismic attributes can are coupled through geology. We will also discuss alternative workflows using seismic attributes for reservoir characterization as implemented by modern commercial software and practiced by interpretation service companies. Participants are invited to bring case studies from their workplace that demonstrates either the success or failure ofseismic attributes to stimulate class discussion.
This presentation includes:
Velocity modeling the principles and pitfalls
Well and seismic velocity data
Incorporating velocity data to build a reliable model in Petrel software
Time to Depth conversion (Map and reservoir property)
Residual error correction and well marker adjustment
Structural uncertainty
Quantitative and qualitative seismic attributes interpretationmohamed Shihata
Seismic attribute is the only way that can enable interpreter to understand seismic data very well and generate new view for his model, but there are hundreds of seismic attributes and there are many classes that make interpreters afraid of using new thing so in this course explain both theoretical and application for each one and try to generate workflow to help interpretation for different geological environment.
In this course, we will gain an intuitive understanding of the kinds of seismic features that can be identified by 3-D seismic attributes, the sensitivity of seismic attributes to seismic acquisition and processing, and of how ‘independent’ seismic attributes can are coupled through geology. We will also discuss alternative workflows using seismic attributes for reservoir characterization as implemented by modern commercial software and practiced by interpretation service companies. Participants are invited to bring case studies from their workplace that demonstrates either the success or failure ofseismic attributes to stimulate class discussion.
This presentation includes:
Velocity modeling the principles and pitfalls
Well and seismic velocity data
Incorporating velocity data to build a reliable model in Petrel software
Time to Depth conversion (Map and reservoir property)
Residual error correction and well marker adjustment
Structural uncertainty
This is for student of geophysics who want to know about basic of multi component seismic. For further detail or any query you can drop me mail, my mail id id bprasad461@gmail.com
Introduction Petrel Course (UAB-2014)
This course has been prepared as an introduction of Petrel software (Schlumberger, www.software.slb.com/products/platform/Pages/petrel.aspx), an application which allows the modeling and visualization of reservoirs, since the exploration stage until production, integrating geological and geophysical data, geological modeling (structural and stratigraphic frameworks), well planning, or property modeling ( petrophysical or petrological) among other possibilities.
The course will be focused mainly in the understanding and utilization of workflows aimed to build geological models based on superficial data (at the outcrop scale) but also with seismic data. The course contents have been subdivided in 5 modules each one developed through the combination of short explanations and practical exercises.
The duration of the course covers more or less 10h divided in three sessions. The starting data will be in the first week of December.
This course will be oriented mainly for the PhD and master students ascribed at the Geologic department of the UAB. For logistic reasons the maximum number of places for each torn are 9. The course is free from the Department members but the external interested will have to make a symbolic payment.
Those interested send an e-mail to the Doctor Griera (albert.griera@uab.cat).
The course will be imparted by Marc Diviu (Msc. Geology and Geophysics of reservoirs).
Modern oil and gas field management is increasingly reliant on detailed and precise 3D reservoir characterisation, and timely areal monitoring. Borehole seismic techniques bridge the gap between remote surface-seismic observations and downhole reservoir evaluation: Borehole seismic data provide intrinsically higher-resolution, higher-fidelity images than surface-seismic data in the vicinity of the wellbore, and unique access to properties of seismic wavefields to enhance surface-seismic imaging. With the advent of new, operationally-efficient very large wireline receiver arrays; fiber-optic recording using Distributed Acoustic Sensing (DAS); the crosswell seismic reflection technique, and advanced seismic imaging algorithms such as Reverse Time Migration, a new wave of borehole seismic technologies is revolutionizing 3D seismic reservoir characterization and on-demand reservoir surveillance. New borehole seismic technologies are providing deeper insights into static reservoir architecture and properties, and into dynamic reservoir performance for conventional water-flood production, EOR, and CO2 sequestration – in deepwater, unconventional, full-field, and low-footprint environments. This lecture will begin by illustrating the wide range of borehole seismic solutions for reservoir characterization and monitoring, using a diverse set of current- and recent case study examples – through which the audience will gain an understanding of the appropriate use of borehole seismic techniques for field development and management. The lecture will then focus on DAS, explaining how the technique works; its capability to deliver conventional borehole seismic solutions (with key advantages over geophones); then describing DAS’s dramatic impact on field monitoring applications and business-critical decisions. New and enhanced borehole seismic techniques – especially with DAS time-lapse monitoring – are ready to deliver critical reservoir management solutions for your fields.
Seismic attributes are being used more and more often in the reservoir characterization and interpretation processes. The new software and computer’s development allows today to generate a large number of surface and volume attributes. They proved to be very useful for the facies and reservoir properties distribution in the geological models, helping to improve their quality in the areas between the wells and areas without wells. The seismic attributes can help to better understand the stratigraphic and structural features, the sedimentation processes, lithology variations, etc. By improving the static geological models, the dynamic models are also improved, helping to better understand the reservoirs’ behavior during exploitation. As a result, the estimation of the recoverable hydrocarbon volumes becomes more reliable and the development strategies will become more successful.
This is for student of geophysics who want to know about basic of multi component seismic. For further detail or any query you can drop me mail, my mail id id bprasad461@gmail.com
Introduction Petrel Course (UAB-2014)
This course has been prepared as an introduction of Petrel software (Schlumberger, www.software.slb.com/products/platform/Pages/petrel.aspx), an application which allows the modeling and visualization of reservoirs, since the exploration stage until production, integrating geological and geophysical data, geological modeling (structural and stratigraphic frameworks), well planning, or property modeling ( petrophysical or petrological) among other possibilities.
The course will be focused mainly in the understanding and utilization of workflows aimed to build geological models based on superficial data (at the outcrop scale) but also with seismic data. The course contents have been subdivided in 5 modules each one developed through the combination of short explanations and practical exercises.
The duration of the course covers more or less 10h divided in three sessions. The starting data will be in the first week of December.
This course will be oriented mainly for the PhD and master students ascribed at the Geologic department of the UAB. For logistic reasons the maximum number of places for each torn are 9. The course is free from the Department members but the external interested will have to make a symbolic payment.
Those interested send an e-mail to the Doctor Griera (albert.griera@uab.cat).
The course will be imparted by Marc Diviu (Msc. Geology and Geophysics of reservoirs).
Modern oil and gas field management is increasingly reliant on detailed and precise 3D reservoir characterisation, and timely areal monitoring. Borehole seismic techniques bridge the gap between remote surface-seismic observations and downhole reservoir evaluation: Borehole seismic data provide intrinsically higher-resolution, higher-fidelity images than surface-seismic data in the vicinity of the wellbore, and unique access to properties of seismic wavefields to enhance surface-seismic imaging. With the advent of new, operationally-efficient very large wireline receiver arrays; fiber-optic recording using Distributed Acoustic Sensing (DAS); the crosswell seismic reflection technique, and advanced seismic imaging algorithms such as Reverse Time Migration, a new wave of borehole seismic technologies is revolutionizing 3D seismic reservoir characterization and on-demand reservoir surveillance. New borehole seismic technologies are providing deeper insights into static reservoir architecture and properties, and into dynamic reservoir performance for conventional water-flood production, EOR, and CO2 sequestration – in deepwater, unconventional, full-field, and low-footprint environments. This lecture will begin by illustrating the wide range of borehole seismic solutions for reservoir characterization and monitoring, using a diverse set of current- and recent case study examples – through which the audience will gain an understanding of the appropriate use of borehole seismic techniques for field development and management. The lecture will then focus on DAS, explaining how the technique works; its capability to deliver conventional borehole seismic solutions (with key advantages over geophones); then describing DAS’s dramatic impact on field monitoring applications and business-critical decisions. New and enhanced borehole seismic techniques – especially with DAS time-lapse monitoring – are ready to deliver critical reservoir management solutions for your fields.
Seismic attributes are being used more and more often in the reservoir characterization and interpretation processes. The new software and computer’s development allows today to generate a large number of surface and volume attributes. They proved to be very useful for the facies and reservoir properties distribution in the geological models, helping to improve their quality in the areas between the wells and areas without wells. The seismic attributes can help to better understand the stratigraphic and structural features, the sedimentation processes, lithology variations, etc. By improving the static geological models, the dynamic models are also improved, helping to better understand the reservoirs’ behavior during exploitation. As a result, the estimation of the recoverable hydrocarbon volumes becomes more reliable and the development strategies will become more successful.
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The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
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A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
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Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
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Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
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1. Integration of Seismic Data and
Uncertainties in the Facies Model
P. Nivlet*, S. Ng, M.A. Hetle, K. Børset, A.B. Rustad (Statoil ASA),
P. Dahle, R. Hauge & O. Kolbjørnsen (Norwegian Computing Center)
1- Classification: Internal 2010-06-10
2. Motivation: 3D reservoir modelling
Reservoir
simulations
Production data
3D reservoir model
2- 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
3. The Snorre field
• Location: Blocks 34/4 and 34/7 in the
Tampen area, in the northern part of the
North Sea (191 km2)
• Production start: 1992
• Production (2009): ~180,000 bbl/day
3- 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
4. Motivations: Data integration
seismic amplitudes
(angle-stacks)
Well log data 3D reservoir model
Structure, stratigraphy
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5. Challenges in integrating the data
• Multi-scale issue
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6. Challenges in integrating the data
Shale
2.0
Vp/Vs
Sand
1.7
6,000 10,000
AI (g/cm3.m/s)
• Non-unique relationship between seismic amplitudes and geology
• A multivariate problem
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7. The data uncertainty challenge
• Random noise
• Acquisition / Processing footprint
• Angle Misalignments
• Imperfect physical model
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8. Geological setting
1,000 m
•Reservoir depth: 2-2.7 km
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9. Traditional workflow
Reservoir grid
(depth)
geometry
Seismic attribute
Well facies+extracted
(depth)
seismic attribute
conditioning
integration
Facies model
9- 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
10. Proposed workflow
Reservoir grid
(depth)
geometry
Seismic attribute
(depth) Well facies+extracted
seismic attribute
conditioning
integration
Facies model
10 - 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
11. Workflow from inversion to facies prediction
Bayesian wavelet
extraction
Seismic
modelling
Vp
Seismic facies
analysis
Vs
Seismic (partial angle-stacks)
Inversion
ρ
34/4-1
34/4-
m BCU
=
OWCLunde Increasing
probability
of shale
SN ML
SN LL Decreasing
probability
of shale
Lomvi Fm
m mBG mS mHF Facies probability
11 - 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
12. Geostatistical seismic inversion
• 1D modelling of seismic amplitudes (Aki&Richards’ model): linear in
m=(log(vp), log(vs), log))
d Gm n
• Normal distribution of elastic properties m
mm|d = mBG+mG*(GmG* + e )-1(d - GmBG)
m|d = m - mG*(GmG* + e )-1G m
• Data (e) stationary uncertainties estimated from analysis of amplitudes
• Prior (m) stationary uncertainties estimated from well log analysis
12 - 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
13. Advantages/limitations of the technique
Stationary uncertainty model:
Lateral correlations
- Global matrix
- Different stratigraphy settings - Lateral correlations
- Grid built from max. 2 horizons - Vertical correlations
13 - 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
14. Inversion result: Elastic properties
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15. Impact on elastic parameter uncertainties
Seismic bandwidth (Near)
0 10 20 30
AI
0
Vp
Rho
-50
SI
Vs
Vp/Vs
0 20 40 60
Frequency (Hz)
Prior Posterior uncertainty Prior Posterior uncertainty
variation (%) variation AI (%)
15 - 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
16. Inversion results QC
Band-pass filtered
AI SI Rhob
100 ms
Well
Inversion
Multivariate correlation (RV) between band-
pass well-logs and inversion results
35% of wells RV > 0.8
33% of wells 0.8 > RV > 0.7
32% of wells RV < 0.7
16 - 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
17. Workflow from inversion to facies prediction
Bayesian wavelet
extraction
Seismic
modelling
Vp
Seismic facies
analysis
Vs
Seismic (partial angle-stacks)
Inversion
ρ
34/4-1
34/4-
m BCU
=
OWCLunde Increasing
probability
of shale
SN ML
SN LL Decreasing
probability
of shale
Lomvi Fm
m mBG mS mHF Facies probability
17 - 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
18. Supervised seismic facies analysis
Kernel
estimator
p(m | Sand)
p(Sand | m)
18 - 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
19. Supervised seismic facies analysis
Different resolution scales
Raw Well logs
Filtered well logs
Inversion results at well position
Inversion filtered well logs
μ m|d = μm+(I- Σm/dΣm-1)(m – μm) + e*
19 - 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
20. Cross plots: Inversion filtered well logs
1
2.0 2.0
Shale
Vp/Vs
Vp/Vs
Sand
1.7 1.7
0
6,000 10,000 6,000 10,000
AI (g/cm3 m/s) AI (g/cm3 m/s)
Inversion frequency filtered Predicted SAND probability
20 - 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
21. Seismic facies analysis: Sand probability results
Sand
probability
21 - 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
22. Inversion results QC: Finding optimal well
position
Confidence index (khi2):
Vertical sand proportion from well
100
ms compared with seismic sand probability
Seismic sand probability section
22 - 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
23. Facies probability QC
31% of wells: Good confidence
61% of wells: Medium
8% of wells: Bad confidence
23 - 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
24. Inversion results QC
Potential factors impacting mismatch
Stratigraphic level ++
Position with respect to OWC +
Presence of faults +
Average shale proportion +
24 - 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
25. 3D confidence index
• Measurement of prediction
• Weighting function in facies
modelling
1
Well Confidence
Inversion result [0,1]
0
25 - 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
26. Proposed workflow
Reservoir grid
(depth)
geometry
Well facies+extracted
seismic attribute
conditioning
integration
Facies model
26 - 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
27. Snorre: Average proportion of channel
Average map estimated from 8 realizations
1 1
0 0
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28. Concluding remarks
• Integrated workflow from seismic inversion to consistent seismic constrained
facies modelling
• Fast geostatistical inversion approach and facies prediction
• Consistent resolution between inversion results and facies probabilities gives
realistic predictions and facies models
28 - 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
29. Concluding remarks: Further work
• How to refine the upscaling of elastic parameters from well log to seismic
scales? How to have a more local approach?
• Constraining observed 4D signals by using predicted facies sand probability
(Ayzenberg and Theune, “Stratigraphically constrained seismic 4D inversion”
M017, Room 127/128, Wednesday, 9h30)
• Flow simulations of constrained facies models and history matching with 4D for
more predictive production prognoses
29 - 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
30. Acknowledgements
Thanks to Statoil, Norwegian Computing Center and the
Snorre partners
Petoro, ExxonMobil Norge, Idemitsu Petroleum, RWE Dea
Norge, Total E&P Norge and Amerada Hess Norge
for discussions and permission to publish this work.
30 - 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
31. Thank you
Integration of Seismic Data and Uncertainties in the
Facies Model
Philippe Nivlet
Principal Geophysicist –Petek Tyrihans
pniv@statoil.com, tel: +47 958 16 589
www.statoil.com
31 - 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010