Introduction to Static Reservoir Modeling

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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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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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Mini-scale Core description include lithology, sedimentary structure and

textural atribute.


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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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


Evaluasi histogram data log sumur dan hasil scale up. Jika perbedaan cukup

signifikan, perbanyak jumlah layer pada zona yang bermasalah

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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


Guided bySeismic Attribute

SIS

Perm

eabil

ity Constraint ToRocktype Linearrelationships /Simulation


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


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SENSITIVITY AND UNCERTAINTYContact

Variogram

Permeability

Sw

CutoffBoi


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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

What we do

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Pemodelan Statis rev.2.pdf (p.1-74)FERG Profile.pdf (p.75)

Documents

Introduction to Static Reservoir Modeling