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PEMODELAN STATIS
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INTRODUCTION TO STATICRESERVOIR MODELING
Event09.00-10.30 Introduction10.30-10.45 Break10.45-12.00 Geological Control12.00-13.00 Break13.00-14.00 Well Correlation14.00-14.15 Break14.15-16.00 Seismic Interpretation16.00-16.15 Homework
09.00-09.30 Review09.30-10.30 Geostatistic10.30-10.45 Break10.45-12.00 Geometry Modeling12.00-13.00 Break13.00-15.00 Facies & Property Modeling15.15-16.00 Volumetric & Uncertainty
Time
24-Mei-2014
25-Mei-2014
TRAINING SCHEDULE
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OIL & GAS UPSTREAM BUSSINES PROCESS
OIL & GAS UPSTREAM BUSSINES PROCESS
EXPLORATION DEVELOPMENT PRODUCTIONPREPARATION MARKETING
AcquiringContract Area
ResourcesReserves
Reserves Production
ProductOptimization
FindingMarket
SKKMIGAS, 2013
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GEOLOGICAL MODELING
HISTORICAL PERSPECTIVESuppose you are required to prospect a very large area for gold. Youhave all the necessary tools for drilling to mine a spot for gold.However, due to costs and technical difficulty you do not have theluxury to mine physically the whole area (with extensive drilling) inorder to find out the locations where gold is deposited in highamounts. Another problem that complicates your objective is that thereis no precedence of gold mining in your area (i.e., no body reallyknows the geology or any historical fact to guide you to choosingdrilling locations that may have a high probability of having golddeposits.)So what do you do?
(the founder of geostatistics Dr. Krige in South Africa was faced with thesame problem some 80 years ago)
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GEOSTATISTICSGeostatistics defined as the branch of statistical sciences that studiedspatial/temporal phenomena and capitalizes on spatial relationship tomodel possible value(s) at unobserved, unsample location. (Caers,2005)
Geostatistics concept: Quantify Spatial Relationship (i.e. by using Variogram)
The non-randomness of geological phenomena entails that valuemeasured close to each other are more alike than value measurefarther apart.
Modeling Spatial Relationship Estimation: Kriging Simulation: Conditional Simulation (SGS/SIS/TGS)
GEOLOGICAL MODELINGGeomodeling consists of the set of all the mathematical methodsallowing to model in an unified way the topology, the geometry and thephysical properties of geological objects while taking into account anytype of data related to these objects. (Mallet, 2002)
A Geomodel is the numerical equivalent of a three-dimensionalgeological map complemented by a description of physical quantitiesin the domain of interest. (Mallet, 2008)
Geologic modeling or Geomodeling is the applied science of creatingcomputerized representations of portions of the Earth's crust basedon geophysical and geological observations made on and below theEarth surface. (Wikipedia)
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WHY DO WE NEED GEOMODEL?3D models help us visualize the ground beneath our feet without the need fortraining in complex geological techniques.
Modelling the Earth's subsurface can help us understand the relationshipbetween geology and our environment.Our traditional printed, 2D geological maps show the distribution of geologicalunits at the surface, but 3D models of the same geology shows us the depth offeatures such as faults, changes in thickness, tilted units and subsurfacecontacts.3D models can: allow non IT specialists to easily access geological information answer specific questions about the subsurface produce a range of outputs display 360 views
DEVELOPMENT OF GEOMODELIn the 70's, geomodelling mainly consisted of automatic 2Dcartographic techniques such as contouring, implemented asFORTRAN routines communicating directly with plotting hardware.
The advent of workstations with 3D graphics capabilities during the80's gave birth to a new generation of geomodelling software withgraphical user interface which became mature during the 90's
Since its inception, geomodelling has been mainly motivated andsupported by oil and gas industry.
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APPLICATIONGeomodelingApplication
Mining HydrologyPetroleum Geothermal
Basin Reservoir
UnconventionalConventional
Silisiclastics Carbonate Basement Tight Sand ShaleHydrocarbonCoal BedMethane
BASIN & RESERVOIR MODELING
Basin ModelingLooks into larger aspects like existence ofa petroleum system in the areaAim is to predict Reservoir development, Source rock
maturation, Migration history, Thermal history, Pressure development etc.
Reservoir ModelingLooks into finer aspects of the reservoir Static Static model Presents the current geologic setup Presents the current state of tectonic
deformation Presents the current state of
stratigraphy Models current distribution of rock
properties
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CONVENTIONAL & UNCONVENTIONAL
Conventional Hydrodynamic emplacement and
trapping Controlled by local structure and
stratigraphy Well defined limits (e.g. seal and
fluid contact) Discrete fields
Un-stimulated Production
Unconventional Trapping not hydrodynamic Controlled by regional stratigraphy Poorly defined limits Continuous or Dispersed
Accumulations Requires stimulation / de-watering
SOURCE OF DATASource of data are reservoir modeling: Geological Data any data related to the style of geological
deposition: Core data porosity, permeability, and relative permeability per
facies Well log data any suite of logs that indicate lithology,
petrophysics, and fluid types near the wellbore Sedimentological and stratigraphic interpretation Outcrop analog data
Geophysical Data any data originating from seismic surveys: Surface and fault interpreted on 3D seismic Seismic Attribute Rock physics data
Reservoir Engineering Data any data related to the testing andproduction of the reservoir: Pressure/volume/temperature (PVT) data. Well-test data Production data
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ROLE OF GEOMODELER Data QC and data harmonization (structural, sedimentological, petrophysical,
geophysical and geomechanical analysis) Elaboration of conceptual model as an integrated process that involves experts from
various fields Structural modeling: Incorporate relevant structural elements and delineate
different fault blocks Gridding of target area Facies Modeling (Sequential Indicator Simulation (SIS), Truncated Gaussian
Simulation (TGS), object based modeling or Multi Point Statistics (MPS)) Petrophysical Modeling: Geostatistical data analysis and simulation (Sequential
Gaussian Simulation (SGS) and co-simulation)Water saturation modeling (J-function analysis) Static Model upscaling Uncertainty Analysis: Visualize dependencies between the input parameters
(seismic, structure, facies, petrophysics) and quantification and visualization of thespatial location and variability of the uncertainty
Discrete Fractured Network modeling (DFN)
GEOLOGICAL CONTROL
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SILISICLASTICS
CARBONATES
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FRACTURED BASEMENT
SHALE HYDROCARBON
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COAL BED METHANE
End of Slide Show
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End of Slide Show
WELL CORRELATION
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Scope of discussion
Sequence Stratigraphy ConceptsElectrofaciesRegional Geology of Jambi Sub-BasinCore DescriptionSequence Stratigraphy Correlation
Sequence Stratigraphy Concepts
Sediment patterns in siliciclastic non-marine and shelf deposits are controlled by twofundamental parameters :1. The rate of sediment influx (Sedimentation rate)2. Changes in the potental space available for sedimentation (Space accomodation)
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Sequence Stratigraphy Concepts
Sequence Stratigraphy Concepts
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Sequence Stratigraphy Concepts
Boyd & Diesel, 1994
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Electrofacies
Serra. O, 1985
Electrofacies
Fluvial Environment
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Electrofacies
Incised Valley and Estuarine Environment
Electrofacies
Delta Environment
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Electrofacies
Deepwater Submarine and Turbidite Environment
Electrofacies
Deepwater Submarine and Turbidite Environment
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Core Description
bottom
top
Interval: 1219.00 - 1229.43 M
Sequence Statigraphic Analysis of Well LogPrevious Study
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Transgresisive
retrogradational
Lowstandaggradation
Highstandprogradatio
nal
Lowstandaggradation
Transgresisive
retrogradational
Uppe
rPen
dopo
Lowe
rPen
dopo
Core
interval
A
B
FERG-2
Interval: 1219.00 - 1229.43 m / 3999.344 - 4033.563 ft
End of Slide Show
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Sedimentology and StratigraphyReview for Static Modeling
Scope of discussion
Important of sedimentology and stratigraphy in static modeling Definition review Aim of sedimentology and stratigraphy in static modeling Scale of observation Reservoir Geometry
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Important of sedimentology and stratigraphy in staticmodeling
(Examples)
Almost onSedimentary Rocks
Definition review
Outline of our discussion : Introduction Geology control Silisiclastic Correlation and Seismic Picking Geostatistic Geometrical modelling Property Modelling Volumetric
Geolo
gical
Facto
r
Sedim
entol
ogy a
ndStr
atigra
phy
Facto
r
Geological understanding need
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Definition review
Sedimentology of the scientific study of sediments (unconsolidated) andsedimentary rocks (consolidated) in terms of their description,classification, origin and diagenesis (Shanmugam, 2006).
Reading (1986) suggested four steps for reconstructing ancientenvironments: (1) description of the rocks; (2) interpretation ofprocesses; (3) establishment of vertical and lateral facies relationships;and (4) use of modern analogs.
Good News!!
Sedimentology field activities
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Definition Review
Stratigraphy is a branch of geology which studies rock layers (strata) andlayering (stratification)(Wikipedia.org).
Some stratigraphic subfields : Lithologic stratigraphy Biologic stratigraphy Chronostratigraphic Magnetostratigraphic Archeological stratigraphy
Definition Review
Sequence stratigraphy is a methodology that provides aframework for the elements of any depositional setting,facilitating paleogeographic reconstruction and the prediction offacies and lithologies away from control point(Catuneanu, 2011)
This framework ties changes in stratal stacking patterns to theresponses to varying accomodation and sediment suplly throughtime.
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Aim of sedimentology and stratigraphy in static modeling
Data should be talking about geological processes and feature,not only statistic and useful for hydrocarbon exploration andproduction.
What geological processes and feature means : Geometry of sand body would be filled by hydrocarbon. Depositional environment and paleogeography.
Scale of observation
Gunter et al (1997)
: Sedimentology and stratigraphy applied
: Sedimentology and stratigraphy model applied
Stage I : Geological Assesment provides a description of the sand-
body dimensions, geometry, andconnectivity.
Stage II : Petrophysical Evaluation focuses on the rock and fluid
systems at a much smaller scale,i.e. the pore scale.
Stage III : Formation Evaluation pore-scale descriptions from Stage
II are upscaled and integrated intocontinuous profiles of porosity,permeability, water saturation,and hydraulic rock types at thewellbore
Stage IV : Reservoir Modeling
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Scale of observation
Mini-scale Core description include lithology, sedimentary structure and
textural atribute.
Scale of observation
Meso-scale Upscaled interpretation of the vertical distribution of the depositional
rock type and identification of the processes influencing their verticaldistribution.
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Scale of observation
Mega-scale The associated geologic processes and the depositional rock types are interpreted in terms of
depositional environments that further provide insights into the initial reservoir dimensions,geometry, position, and connectivity.
Reservoir GeometryMini-Scale Meso-Scale
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Reservoir GeometryMega Scale
End of Slide Show
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GEOSTATISTICS IN RESERVOIRMODELING
OUTLINE
IntroductionSome basic definitionSpatial StatisticsDeterministic ModelingStochastic Modeling
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INTRODUCTION
What is Geostatistics?
Geostatistics: study of phenomena that vary in space and/or time (Deutsch, 2002)
Geostatistics can be regarded as a collection of numerical techniques that deal with thecharacterization of spatial attributes, employing primarily random models in a manner similarto the way in which time series analysis characterizes temporal data. (Olea, 1999)
Geostatistics offers a way of describing the spatial continuity of natural phenomena andprovides adaptations of classical regression techniques to take advantage of this continuity.(Isaaks and Srivastava, 1989)
Statistical technique that accounts for spatial relationships of variables in estimating values ofthe variables at unsampled locations. (Kelkar and Perez, 19??)
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Application of Geostatistics
Interpolation and Extrapolation Spatial Distribution Analysis Risk Analysis/Uncertainty Estimates Use of Intercorrelated Attributes
Limitations of Geostatistics Geostatistics Does Not Create Data or Eliminate the Value of
Obtaining Additional Good Data Geostatistics Does Not Replace Sound Qualitative Understanding and
Expert Judgment Geostatistics Does Not Necessarily Save Time, At Least in the Short
Term. Geostatistics Does Not Work Well as a Black Box
Porosity at X is 13.7%
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Reservoir Modeling Some basic definition
BASIC DEFINITION
STATIC RESERVOIR MODEL
DYNAMIC RESERVOIR MODEL
Parameters which does not change in timeie: Facies, Reservoir Rock Type (RRT), Phi, Initial Sw, etc.
Parameters that change in timeie: Fluid flow, Pressure, etc.
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HOMOGENY Vs. ISOTROPYHomogeny & Heterogenic Vs. Isotropy & Anisotropy
high Heterogeneity Low Heterogeneity
a) b)
c) d)
Anisotopy:a) 1b) 0.8c) 0.5d) 0.2
The directionof Maximum continuity
The directionof Minimum continuity
STATIONARY
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Mean ValueArithmetic
Geometric
Harmonic
Deterministic Vs StochasticDeterministic If One Knows Enough About the Process Responsible forthe DistributionStochastic If the Underlying Process Is Not Well Understood Deterministic Models Depend
on Outside Information NotContained in the Data Values(i.e. Quantitative ProcessDescription) and the Context ofthe Data
Deterministic Model Examples: Distance a Ball Will Travel
When Thrown Information Needed
Equation Velocity and Angle Ball Is
Thrown Gravitational Constant
(g)
Stochastic Models Stochastic Models Are Useful
When the Process Responsiblefor the Distribution of Values isNot Well Understood
A Stochastic Model is a RandomModel Controlled by a SpatialCorrelation Model
Stochastic Models are a UsefulReservoir Characterization ToolBecause a Reservoir is the EndProduct of Many PoorlyUnderstood Processes
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Estimation Vs Simulation
Estimation is Process of Obtaining theSingle Best Value of a ReservoirProperty at an Unsampled Location.Local Accuracy Takes Precedence OverGlobal Spatial Variability. EstimationMethods, Therefore, Tend to ProduceSmooth Property Distributions.
Many Traditional MethodsBlock AveragesInverse Distance WeightedInterpolationTriangulation
Many Geostatistical MethodsOrdinary KrigingCollocated Cokriging
Simulation is Process of ObtainingOne or More Good Values of aReservoir Property at an UnsampledLocation. The SimulatedDistributions Honor Global Featuresand Statistics Instead of LocalAccuracy. Simulation Methods Tendto Produce More Realistic PropertyDistributions.Variety of Methods Available,Including:
Gaussian Sequential Simulation(GSS)Sequential Indicator Simulation(SIS)Simulated AnnealingBoolean (Marked-Point, ObjectBased)
Simulation is Process of ObtainingOne or More Good Values of aReservoir Property at an UnsampledLocation. The SimulatedDistributions Honor Global Featuresand Statistics Instead of LocalAccuracy. Simulation Methods Tendto Produce More Realistic PropertyDistributions.Variety of Methods Available,Including:
Gaussian Sequential Simulation(GSS)Sequential Indicator Simulation(SIS)Simulated AnnealingBoolean (Marked-Point, ObjectBased)
Estimation Vs SimulationEstimation Simulation
Effective Porosity
Note Smooth ContoursOn Estimation Map
Compared to Simulation(Stochastic) Map.
Note that Areas ofGreatest Difference
Between the Two MapsAre In Areas of Littleor No Well Control.
Note Smooth ContoursOn Estimation Map
Compared to Simulation(Stochastic) Map.
Note that Areas ofGreatest Difference
Between the Two MapsAre In Areas of Littleor No Well Control.
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SPATIAL STATISTICS
Spatial Analysis Characteristics of Geoscience Data Sets : Exhibit SpatialRelationships neighboring values are related to each other The relationship gets stronger as the distance between twoneighbors becomes smaller
In most instances, beyond certain distance the neighboringvalues becomes uncorrelated
Statistical methods to quantify spatial relationship: Covariance Variogram
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Covariance
Variogram
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Covariance Vs. Variogram
Covariance measures similarities whereas variogram measures the difference Relationship under most situations
In geostatistics, we use variogram instead of covariance to describe spatialrelationship
Covariance Variogram
Variogram
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DETERMINISTIC MODELING Estimation Process - Kriging
ESTIMATION
Estimation means the process to estimate the value atinterwell locations.
Common method : Linear Interpolation. Linear Interpolation in Geostatistics is done using Kriging
Kriging is named after it founder Danny Krige, a gold minerscientist from South Africa (1948)
Kriging is a deterministic method. The main difference between kriging and conventionallinear interpolation is the use of spatial relationship (i.e.,variogram), instead of based on pre-defined formula.
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LOCAL ESTIMATION Point Estimation Methods
Geological Experience and/or Artistic License Traditional Algorithms That Use Weights Based on Euclidean (Geometric)Distance Polygon Method (Nearest Neighbor) Triangulation Local Sample Mean Inverse Distance
Geostatistical Algorithms That Use Weights Based on Structural (orStatistical) Distance Simple Kriging Ordinary Kriging Universal Kriging Kriging with Trend Collocated Cokriging
ESTIMATION PROCESS - KRIGING
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Stochastic modeling
SEQUENTIAL SIMULATION
The most popular technique in reservoir description Uses grid based method Can generate multiple realizations of various reservoirattributes
The two common most methods are: Sequential IndicatorSimulation (SIS) and Sequential Gaussian Simulation (SGS)
TGS : Combination of SGS and SIS Provide smoother distributin of discrete variable
To honor local relationships among various attributes, co-simulation method is used
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SEQUENTIAL SIMULATION
PROCEDURE: Transform Variogram Analysis Random Path Determination Kriging Uncertainty Quantification Back Transform
TransformGaussian Transform: Transform the data (may be originally as continuous or discretevariable) to become Continuous variable
In most cases, SGS is used for continuous variable but, it may alsobe used for discrete variable (e.g., TGS)
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Sequential Gaussian Simulation based on Simple Kriging
4 realizations
Sequential Gaussian Simulation based on Simple Cokriging
4 realizations
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Example Sequential Gaussian Cosimulation (1)
4 realizations
Example Sequential Gaussian Cosimulation (2)
4 realizations
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End of Slide Show
STATIC MODELING
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STATIC MODELING
PENDAHULUAN WORKFLOW DATA YANG DIBUTUHKAN MODEL GRID MODEL FACIES MODEL PETROFISIKA PERHITUNGAN VOLUMETRIK ANALISIS SENSITIVITAS DAN KETIDAKPASTIAN UPSCALE
PENDAHULUAN
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DEFINISI UMUM
STATIC RESERVOIR MODEL
DYNAMIC RESERVOIR MODEL
Parameters which do not change in timeie: Facies, Reservoir Rock Type (RRT), Phi, etc.
Parameters that change in timeie: Fluid flow, Pressure, etc.
Permeability ?Water Saturation ?
WORKFLOW
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WORKFLOWGeologicalIntepretationGeologicalIntepretation
Static Model(base case)
GeophysicalIntepretationGeophysicalIntepretation
PetrophysicalIntepretationPetrophysicalIntepretation
Dynamic DataValidation
Uncertainty Analysis
Scale Up
Bubble MapBubble MapMaterial BalanceMaterial Balance
Well TestWell TestDST/MDT/RFTDST/MDT/RFT
Overall Workflow
WORKFLOWInput Data
IntepretasiPetrofisika
IntepretasiGeofisika
InterpretasiGeologi
Analisis TeknikReservoir
Model Grid
Model Patahan
Areal Gridding
Model Horison
Zonasi
PembuatanLapisan
Grid QualityControl
Model Facies
Scale Up WellLog
AnalisisGeostatistik
Trend Modeling
DistribusiFacies
IntegrasiKonsep Geologi
ModelPetrofisika
Scale Up WellLog
AnalisisGeostatistik
DistribusiPhi,K,Sw,NtGmengacu
terhadap Facies/ Rocktype
Validasi denganData Dynamic
PerhitunganVolumetrik
OOIP/OGIP
AnalisisSensitivitas
AnalisisKetidakpastian
UPSCALING
Design
StructuralUpscale
PropertiesUpscale
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KEBUTUHAN DATA
KEBUTUHAN DATAIntepretasiGeofisika
IntepretasiGeologi
IntepretasiPetrofisika
Analisis TeknikReservoir
Korelasi SumurKorelasi Sumur
Fasies GeologiFasies Geologi
Rock TypeRock TypeKonseptual Sebaran Fasies (Peta 2D)Konseptual Sebaran Fasies (Peta 2D)
PorositasPorositasSaturasi AirSaturasi Air
Kontak FluidaKontak Fluida
Analisis Uji SumurAnalisis Uji SumurBubble MapBubble Map
Atribut SeismikAtribut SeismikInterpretasi SeismikInterpretasi Seismik
Persamaan Saturasi Diatas KontakPersamaan Saturasi Diatas Kontak
Boi & BgBoi & BgPermeabilitasPermeabilitas
* Tipikal data pada reservoir konvensional, dapat berbeda pada kasus reservoir unconventional* Tipikal data pada reservoir konvensional, dapat berbeda pada kasus reservoir unconventional
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MODEL GRID
Objektif Workflow Model Patahan Areal Gridding Model Horison dan Zone Model Lapisan Scale up Well Log Grid Quality Control Studi Kasus 1 (Lapangan Bravo) Studi Kasus 2 (Lapangan KE)
OBJEKTIF
Membangun arsitektur dari reservoir dengan membaginya menjadi gridblock dengan ukuran yang konsisten terhadap resolusi data statik
Menggabungkan patahan dan horison hasil interpretasi seismik Membagi zona berdasarkan kombinasi data seismik dan sumur Membagi perlapisan pada tiap zona berdasarkan kondisi geologi
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WORKFLOW
Model PatahanModel Patahan ArealGriddingAreal
GriddingModel HorisonModel Horison Model ZonaModel Zona
Model LapisanModel LapisanQuality ControlQuality Control
WORKFLOW
Bahar, 2012
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MODEL PATAHAN
MODEL PATAHANTUJUAN:Memasukkan hasil Patahan interpretasi seimik kedalamModel Grid
HAL YANG HARUS DIPERHATIKAN: Patahan yang dimodelkan sebaiknya HANYA patahan
yang berkontribusi terhadap geometri dan propertireservoir
Geometri Patahan: Vertikal, Miring, Listrik Hubungan antar patahan (Memotong secara
lateral/Vertikal*) Smoothing dan editing sebaiknya melihat kembali data
seismik (lakukan terlebih dahulu pada domain time)karena akan mempengaruhi volume reservoir
Kaidah geologi struktur* Patahan yang memotong secara vertikal akan mempengaruhibentuk grid, biasanya memerlukan perhatian khusus. Lebih baikdihindari
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MODEL PATAHANHAL YANG HARUS DIPERHATIKAN: Patahan yang dimodelkan sebaiknya HANYA patahan yang
berkontribusi terhadap geometri dan properti reservoir
Dimodelkan atau tidak?
Man in Charge:Geologist dan Reservoir Engineer
MODEL PATAHAN
Fault memotong secara lateral Fault memotong secara vertikal
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MODEL PATAHANCommon Practice:
- Kumpulkan semua patahan hasil interpretasi, diskusikan bersama geologist dan reservoir enggineerpatahan mana saja yang akan dimodelkan.
- Tentukan bentuk dari masing masing patahan. Untuk model skala reservoir biasanya pilar lineardengan 2 atau 3 poin sudah cukup untuk memodelkan patahan.
- Pastikan apakah terdapat patahan yang berpotongan secara vertikal, jika ada diskusikan kembalidengan geologi dan geofisika apakah kedua patahan tersebut penting, jika ia maka diperlukanperhatian khusus.
- Transfer patahan hasil interpretasi ke dalam model grid.- Lakukan editing dan smoothing dengan melihat kembali data Seismik.- Diskusikan apakah hasil model patahan sudah baik dari sisi geologi, geofisika dan reservoir.
AREAL GRIDDING
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AREAL GRIDDINGTUJUAN:Membuat grid secara lateral yang meggambarkan heterogenitassecara areal.
HAL YANG HARUS DIPERHATIKAN: Usahakan berbentuk rectangular (segi empat) Ukuran minimum: Resolusi seismik Ukuran maksimum: Sediakan minimum 2 atau 3 grid blok diatara
sumur Usahakan tidak ada 2 atau lebih sumur dalam satu grid, kecuali
twin well atau beroperasi pada waktu yang berbeda Jangan berencana untuk melakukan areal upscale
AREAL GRIDDING
Contoh 1: Patahan tidak diberi arahmengakibatkan banyak grid tidak berbentuksegi empat
Contoh 2: Patahan diberi arah, grid berbentuksegi empat
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AREAL GRIDDING
Contoh 3: Patahan kompleks tanpa diberi arah Contoh 4: Patahan kompleks setelah diberi arah
AREAL GRIDDING
Ukuran grid =100 * 100Total Grid = 3,928,050
Ukuran grid =200 * 200Total Grid = 1,964,0252 sumur pada 1 grid
Ukuran grid =50 * 50Total Grid = 15,712,200Total grid terlalu besar
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AREAL GRID
Common Practice:
- Tentukan area yang ingin dimodelkan.- Buat batasan model berupa poligon, usahakan searah dengan patahan utama.- Berikan arah pada setiap patahan yang berarah sama, manfaatkan fitur
Automatic direction assignment pada perangkat lunak pemodelan- Tentukan besaran grid yang paling sesuai pada model yang akan dibangun- Periksa hasil grid, apakah terdapat grid yang masih bisa dioptimasi
MODEL HORISON DAN ZONE
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MODEL HORIZONETUJUAN:Integrasi hasil korelasi sumur dan intepretasi seismik (faultdan horison) kedalam model pilar yang telah dibuat.
HAL YANG HARUS DIPERHATIKAN: Horison yang dimodelkan sebaiknya berasal dari hasil
intepretasi seismik Residual marker dan horison telah diminimalisir agar
hasil model tidak terdapat bull eyes Jarak pengaruh dari masing masing patahan Jarak displacement maksimum dan minimum patahan
MODEL HORISON
Input horison Hasil model Jarak pengaruh patahan
Input data yang terkena pengaruh patahan akan dihilangkan, kemudianinterpolasi dari data yang berada diluar pengaruh patahan
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MODEL ZONE
TUJUAN:Membagi lapisan didalam horison yang tidakdapat didapatkan melalui intepretasi seismik.
HAL YANG HARUS DIPERHATIKAN: Zonasi dibagi berdasarkan konsep geologi
(Chrono / Lito)
MODEL LAPISAN
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MODEL LAPISANTUJUAN:Membagi setiap lapisan reservoir menjadilapisan tipis sesuai dengan resolusi data(fine layer)
HAL YANG HARUS DIPERHATIKAN: Ukuran lapisan harus dapat
mencapture tingkat heterogenitasvertikal reservoir
Tipe Layering Jumlah total grid cell
PHI SW NTG
MODEL LAPISAN
Yerus dan Chambers, 2006
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SCALE UP WELL LOG
SCALE UP WELL LOG
TUJUAN:Memasukkan nilaisumuran kedalam gridblock
HAL YANG HARUSDIPERHATIKAN: Metode scale up
Data log sumur Hasil Upscale
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GRID QUALITY CONTROL
GRID QUALITY CONTROL
Evaluasi histogram data log sumur dan hasil scale up. Jika perbedaan cukup
signifikan, perbanyak jumlah layer pada zona yang bermasalah
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GRID QUALITY CONTROL
Periksa nilai volumedari tiap grid. Nilaiminus menunjukkanbahwa ada grid yangterlipat, periksatahapan areal grid.
FACIES MODELING
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TOPICS
What is Facies, Rock Type, and Facies Modeling ? Why do we need to do Facies Modeling ? How do we do Facies Modeling ?
Facies at Well Location 3D Facies Distribution
Case Study Example of Facies Modeling.
GEOLOGICAL FACIESDefinition :
Facies are a body of rock with specified characteristics. Ideally, a facies is a distinctive rock unit that forms under certain conditions of sedimentation,
reflecting a particular process or environment
Facies are distinguished by what type of the rock is being studied (e.g., Lithofacies (based onpetrological) , Biofacies (based on fossil),
Lithofacies classifications are a purely geological grouping of reservoir rocks, which have similartexture, grain size, sorting etc.
Each lithofacies indicates a certain depositional environment with a distribution trend and dimension. Knowledge in Facies is important as it provides information on how the rock is ditributed in the
reservoir
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RESERVOIR ROCK TYPE
Definition : RRT is grouping of geological rock based on both geological facies andpetrophysical grouping (porosity, permeability, capillary pressure and
saturation).
The objective of generating RRT is to link property with geology Facies distribution may be interpreted by geological knowledge butnot necessarily the property due to diagenesis
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FACIES MODELING TECHNIQUES
FACIES MODELINGTGS SIS
Well log
Trend Property
Gaussian Simulation
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ROCK TYPE MODELINGTGSWell log Gaussian Simulation
Constraint toFacies model
Facies Modelling
Reflection strength attribute Facies model Rocktype Model
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KEY ISSUE IN FACIES MODELING Conceptual Geological Model is needed in order to QC the resultand/or used as the trend.
Integration with other information, other than well data, in the formof 2D or 3D distribution is critical in order to obtain reliable result.
Possible trend for Facies Modeling : Seismic Data Probability Map of Facies Distribution Diagenesis Model
PETROPHYSICAL MODELING
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WHY DO WE DO PETROPHYSICAL MODELING?
To obtain 3D distribution of porosity consistent with its geological (facies) distribution. It is one of the most important component for quantifying the volumetric of the reservoir.
Primary Data : Attribute at Well Locations, obtained from :
Petrophysical Analysis / Well Log Interpretation (PHIE). Theanalysis should consider core-log correlation.
Secondary Data : 3D Facies Model 2D or 3D Seismic Attributes (e.g., AI, Amplitude)
Spatial Information Calculated from well data (at least vertical variogram), if sufficient
well data exists, or Inferred from Seismic Attributes (Correlation Length and direction)
PROPERTIES MODEL
Vsh Constraint To
Rocktype Guided bySeismic Attribute
SIS
Constraint ToRocktype
Guided bySeismic Attribute
SIS Poro
sity Constraint To
Rocktype Guided bySeismic Attribute
SIS
Constraint ToRocktype
Guided bySeismic Attribute
SIS
Perm
eabil
ity Constraint ToRocktype Linearrelationships /Simulation
Constraint ToRocktype
Linearrelationships /Simulation
Water S
aturat
ion Constrain toRocktype Saturationheight functioni.e.J-Function
Constrain toRocktype
Saturationheight functioni.e.J-Function
Key Issues:Good 3D Facies Model and/or good correlation with Seismic Attribute (e.g.,
Acoustic Impedance) is essential for the success of Porosity Modeling
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VOLUMETRIC CALCULATION
VOULUMETRIC CALCULATION
Each cells have its own values
STOIIP = Bv * NtG * Porosity * (1-Sw) *(1/Boi)
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UNCERTAINTY IN THE MODELING
More is the hard data we have , less is the uncertainty in the modelCalculating the uncertainty in the model, tells us how realistic is theModel made with the available data
Its is better to have uncertaintyrather than illusion of realityAndre G. Journel
Uncertainty in the Modeling
What adds to uncertainty in the model Errors/uncertainty in seismic interpretation Errors/Uncertainty in Velocity Modeling if time to depth
conversion was involved Errors/uncertainty in the log data processing Errors/uncertainty in data analysis Errors/Uncertainty in 3D interpolationUncertainty in the Model is a Cumulative Result of all the abovementioned factors
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SENSITIVITY AND UNCERTAINTY
SENSITIVITY AND UNCERTAINTY
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SENSITIVITY AND UNCERTAINTYContact
Variogram
Permeability
Sw
CutoffBoi
SENSITIVITY AND UNCERTAINTY
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SENSITIVITY AND UNCERTAINTY
End of Slide Show
Experience
SOP Petrophysical Multimin Dual Water Saturation Shally Sand and Dual Porosity Carbonate. UTC
Pertamina. October 2012 April 2013.
G&G Study MAC and MDK Field. Husky-Cnooc Madura Ltd. April June 2013.
Petrophysical analysis of MMC Parigi. ETTI Pertamina EP. July Augustus 2013.
G&G Basic Training. Pusat Survey Geologi. Augustus September 2013.
G&G Study of Kenali Asam Dangkal Field. EOR Pertamina. October December 2013.
Provision of Basin Study and Petroleum System of West Galagah kambuna Block, North Sumatra Basin.
Petronas Carigali (West Galagah kambuna) Ltd. December 2013 May 2014.
GGRPFE Study of South jambi B Field. Pertamina Hulu Energy. Maret Oktober 2014.
SOP Rock Typing and Static Model Carbonate and Silisiclastic. UTC Pertamina. January October 2014.
Studi Karakterisasi Reservoir Gas Metana Batubara (CBM) Cekungan Sumatra
Selatan, Barito, dan Kutai. Pertamina Hulu Energy. On Going.
G& G Betun Selo Field . PT Petroenim Betun Selo, February 2012
Petrophysical Training , PT. Tropic Energy, 2013
Resertifikasi Cadangan Struktur Donggi, matindok, Maleoraja, dan Minahaki, Sulawesi tengah, MGDP
Pertamina EP
GGR Study of Badik Structure , PHE Nunukan, on going
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Pemodelan Statis rev.2.pdf (p.1-74)FERG Profile.pdf (p.75)