Simulation

RESERVOIR SIMULATION

Dr. Keka OjhaDepartment of Petroleum Engineering

Indian School of Mines University-Dhanbad

What is Simulation Literal Meaning: “mere appearance of , without

reality”… It may be defined as the art of designing an artificial

system close to a real system- Experimentation of modeling is simulation.

Reservoir simulation is a tool that allows the petroleum engineer to gain greater insight into the mechanism of petroleum recovery that is otherwise possible. It can, if properly used, be one of the most valuable tool.

It is the only means to give better understanding of the operating characteristics and engineering aspects of the oil and gas productions from a reservoir.

Reservoir Simulation- Why?

Need/ Benefit of Simulation• An engineer can have only a single opportunity to produce the

reservoir; any mistake made in the process will be around forever. But, the simulation study can be made several times and alternatively examined. As an e.g. experimentation on aeroplane can be done without passengers but not with any passenger.

• Many complex systems were previously impossible to handle in reality, modern simulation process can solve without undue difficulty.

• The mechanics of simulation have compiled all the data of reservoir into one compact data base.

• Presence of common ground between companies and regulatory bodies and other agencies which deal with petroleum resources.

Applications Reservoir simulation is used to satisfy the corporate needs.

It can be used to forecast economy of the project (as the production rate changes with time, it must be evaluated annually) to get revenue and cash flow.

Evaluation of effectiveness of the development options: there are a number of ways by which we can develop the reservoir.

A reservoir may be containing a single well, a group of wells, or several wells interacting as a complex system. In case of multi-zone reservoirs, the productivity and oil in place of a given horizon or zone may be needed.

Modeling of the reservoir as a series of zone can produce this information and allow the engineers to schedule production and completion operations for these zones more effectively.

Applications• Reservoir management: interpretation of reservoir

behavior. Simulators can also be used to study the mechanics of fluid flow in porous media.

• The original oil-in-place is an important and necessary objective in any study; this is usually required as a reservoir in total.

• Optimization of field development plans: Out of different possibilities, the most profitable options to be chosen.

• Estimation of primary recovery.• Mode of pressure maintenance, EOR methods.• Effective rate of recovery.

Objectives of Simulation

Original oil in place Gas storage Single well study Economical parameter Optimization of petroleum system Fluid movement in reservoir

In developing each flow, the basic data required include income generated, expenses, and capital investments over the planning horizons.

Income generating parameters are the oil and gas production that are essential output of the simulator. These are available in a pre-well basis by lease or by reservoir total.

Objectives include determination of

Steps of Simulation

• Problem definition• Data review• Data acquisition• Selection of approach• Reservoir description and model

design• Programming support• History Matching• Prediction• Editing and analysis• Reporting

Interaction of Simulation Engineer with simulator and Reservoir

Description

Mechanics of Reservoir Simulation

Input to simulation

Petrophysical properties Fluid properties Details of surface facility (discharge of

wells, separator pressure etc Economic parameters Geological model Seismic interpretation Well patterns (vertical, horizontal,

multilateral, five spot, seven spot etc.)

• Output: Production data (rate, pressure)

Petrophysical properties

Porosity-packing system

Relative permeability Vs. Saturation

3-Φ Relative permeability

2-Φ relative permeability

Capillary pressure

Petrophysical Propertiesn

wo

t CSR

RI mF

22

3

)1(

M

Ck o

Carmem –Kozeny Equation relating permeability with porosity

2

32

)1(

dCk

Alternate Form: M, specific area, where area is divided by volume (length)-1

Parameter Co or C depends on lithology, packing, tortuosityAllows independent assessment of k knowing f and MBoth f and M are affected by lithology – cementation, consolidation etc. k is also influenced by these factors. As cementation increases, M decreases (As decreases, Vs increases). However f also decreases, compensating effectsRocks with identical k may have different f and vice-versa Fine grain rocks ø may be high (good sorting), however K may be very low (since M is very high)

Archie’s formula

Difference between Homogeneity and Isotropy

Proper representation of Dimension

Fluid properties

Equation of states (EOS) for fluid

Oil properties

Interfacial tension

References

• Modern Reservoir Engineering - A Simulation Approach : H. B. Crichlow

• Petroleum Reservoir Simulation: Khalid Aziz and A Settari

• Fundamentals of numerical reservoir simulation: D W Peaceman

• SPE Monograph Series on Reservoir Simulation

Dimensionality and

grid geometry

Rectangular system

1-D flow

2-D

3-D

Radial flow

3-D radial-cylindrical flow

1-D radial flow

Elliptical-cylindrical flow geometry

Example of applications: (i) Near a well, if a strong contrast exists between two principal directions in lateral plane (ii) When a vertical well is intercepted by a vertical, high conductive fracture

Spherical flow geometry

Applications: (i) Partial penetration into a thick formation by a production well(ii) Flow near the perforation

Curvilinear Flow Geometry

5-spot injection – production system

Size and Number of grid block

Size and number of grid block depends on the factors:

• Availability of data• Level of information desired• Quality of information desired• Flow characteristics• Complexity of the reservoir• Number of wells

Body-centered grid

Modeling Concept and Simulation

Schematic Diagram of Simulation Approach

Types of Model

• Physical model :Small replica of real system

• Analytical Model: Logically similar to the actual system. eg.: Electical analog.

• Mathematical Model: It is developed using the basic principles of science and engineering. In reservoir simulation, model will be developed using the principles of fluid flow, equations of states for fluid, conservation of mass, momentum and energy etc.

Contents

Boundary Conditions »

Discretization of Flow Equation »

Questions

Solutions of Equations »

Flow Equations

Initial Condition

• Discretization

• Taylor series approximations

• Approximation of the second order space derivative

• Approximation of the time derivative

• Explicit difference equation

• Introduction

• Pressure BC

• Flow rate BC

• Introduction

• Explicit formulation

• Implicit formulation

• Discussion of the formulations

Flow Equations

Linear flow• We will consider a simple horizontal slab of porous material, where

initially the pressure everywhere is P0 , and then at time zero, the left side pressure (at x=0 ) is raised to PL while the right side pressure (at x=L ) is kept at PR=P0

fluid

x

• Introduction• We will first review the simplest one-dimensional flow equations for

horizontal flow of one fluid, and look at analytical and numerical solutions of pressure as function of position and time and then go for 3-D multiphase flow equation.

Continue

Flow Equations

•Partial differential equation (PDE)The linear, one dimensional, horizontal, one phase, partial differential flow equation for a liquid, assuming constant permeability, viscosity and compressibility for transient or time dependent flow:

t

P

k

φμc

x

P2

2

• If the flow reaches a state where it is no longer time dependent, we denote the flow as steady state. The equation then simplifies to:

0

2

2

x

P

More

The Above equation is called Diffusivity Equation

Flow Equations• Transient and steady state pressure distributions are illustrated

graphically in the figure below for a system where initial and right hand pressures are equal:

pressure vs. x

x

P Transient

solution

Steady state

solution

Initial and right

side pressure

Left side pressure

Flow EquationsRadial flow (well test equation)

An alternative form of the simple one dimensional, horizontal flow equation for a liquid, is the radial equation that frequently is used for well test interpretation. In this case the flow area is proportional to r2, as shown in the following figure:

r

Flow Equations• The one-dimensional (radial) flow equation in this coordinate

system becomes:

t

P

k

φμc

r

Pr

rr

1

• For steady state flow equation simplifies to:

01

r

Pr

rr

• By integration twice for following boundary conditions: P(r=rw)=Pw andP(r=re)=Pe , the steady state solution becomes:

w

w

e r

r

rr

wew

PPPP ln

ln

More

More

Multiphase flow equations

• The equation derived for single phase flow can be converted to multiphase flow (multi-component) with incorporation of saturation and concentration terms of individual components and phases.

• The phases present in petroleum reservoir are nothing but oil(o), water (w) and gas (g). To develop the equation, let us consider the flow of a single component (‘i’th component) present in all three phase within the reservoir.

• Let, vo, vw and vg are the velocity of oil, water and gas respectively; Cio, Ciw, and Cig are the concentration of ‘i’th component in respective phases.

• Thus

• Saturation of the phases:

0.1 igiwio CCC

0.1 gwo SSS

Multiphase flow-Compositional Model

Multiphase flow-Compositional Model

3

1

3

1 pppippip

ppp

p

rpipip SCt

qCDgPkkC

3

1

3

1 pppippip

ppipip SC

tqCvC

ggigwwiwooio SCSCSC

Total mass of component ‘i’ accumulated in unit volume

Now, the continuity equation of the component ‘i’ in three phases can be given by

Where qp is the production or injection rate (in mass) of the individual phase.

Incorporating the Darcy’s equation in the continuity equation, we will get the compositional model as follows:

Number of Equations & variables

• Saturation equation=1• Composition correlation=3.• Now, we have (N+4) number of correlation .• But, the number of variables in the equations are

3N+15.

• Variable Number• Composition (Cip) 3N• Density (ρp) 3• Pressure (Pp) 3• Viscosity (μp) 3• Relative permeability (krp) 3• Saturation (Sp) 3• Total number of variables to be determined =

3N+15

Remaining Correlations Equilibrium constants for Gas-Oil and Oil –Water (2×N)

Kigo = f(Po, Pg, T, Cig, Cio) No. of Eqn=N

igoio

ig KC

C

igwiw

ig KC

C Kigw = f(Pw, Pg, T, Cig, Ciw) No. of Eqn=N

Equations of states (3) : ρp= f(Pp, T, Cip) No. of Eqn=3

Viscosity (3): μp = f(Pp, T, Cip) No. of Eqn=3

Capillary pressure (2): Pcow = f(Sw) = Po-PwPcog = f(Sg) = Pg-Po No. of Eqn=2

Relative permeability krg = f(Sg); krw= f (Sw)kro= f (Sw, Sg) No. of Eqn=3

Now, the total number of equations are =3N+15So, compositional model has a unique solution

Black Oil Model

• Black oil model assumes the presence of three pseudo-components only, oil, gas and water.

• It is further simplified that, there is only one way phase transfer of gas into or out of oil phase.

• Mass transfer between water-oil, water-gas and oil to gas is assumed to be nil.

• Incorporating the assumptions mentioned above and equations of states into the compositional model, the Black Oil model is obtained as follows:

• Water:

• Oil

• Gas

w

wwsww

ww

rw

B

S

tqDgP

B

kk

o

oosoo

oo

ro

B

S

tqDgP

B

kk

o

so

g

ggsgg

gg

rgoo

ow

sro

B

RS

B

S

tqDgP

B

kkDgP

B

Rkk

Black Oil Model

The above equations can be incorporated into a single equation using the capillary pressure and saturation relationships.

Boundary Conditions

The driving force for flow arises from the BC's. Basically, we have two types of BC's :

Boundary Conditions

Pressure condition

(Dirichlet condition)

Flow rate condition

(Neumann condition)

Block centered grid is used mainly for Neumann type boundary condition andCorner point is used for Dirichlet type boundary conditions.

Boundary Conditions• Pressure BCWhen pressure boundaries are to be specified, we

normally, specify the pressure at the end faces of the system in question. Applied to the simple linear system described above, we may have the following two BC's:

Using the index systemUsing the index system

Rt

N

Lt

i

PP

PP

0

0

21

21

• The reason we here use indices i=1/2 and N+1/2 is that the BC's are applied to the ends of the first and the last blocks, respectively.

R

L

PtLxP

PtxP

0,

0,0

• Flow rate BCAlternatively, we would specify the flow rate, Q, into or out of an end face of the system in question, for instance into the left end of the system above. Making use of the fact that the flow rate may be expressed by Darcy's law, as follows:

0

x

L x

PkAQ

Boundary Conditions

• In a real reservoir case, flow rate conditions would normally represent production or injection rates for wells. A special case is the no-flow boundary, where Q=0. This condition is specified at all outer limits of the reservoir, between non-communicating layers, and across sealing faults in the reservoir.

Multiphase FlowBoundary conditions of multiphase systems• The pressure and rate BC's discussed above

apply to multiphase systems. However, for a production well in a reservoir, we normally specify either an oil production rate at the surface, or a total liquid rate at the surface. Thus, the rate(s) must be computed from Darcy's equation. The production is subjected to maximum allowed GOR or WC, or both.

Initial conditions of multiphase systems• In addition to specification of initial pressures,

we also need to specify initial saturations in a multiphase system. This requires knowledge of water-oil contact (WOC) and gas-oil contact (GOC). Assuming that the reservoir is in equilibrium, we may compute initial phase pressures based on contact levels and densities. Then, equilibrium saturations may be interpolated from the capillary pressure curves. Alternatively, the initial saturations are based on measured logging data.

Initial Condition• Initial condition (IC)

• The initial condition (initial pressures) for our horizontal system may be specified as:

00 PP t

i Ni ,...,1

• For non-horizontal systems, hydrostatic pressures are normally computed based on a reference pressure and fluid densities

Discretization of Flow EquationNumerical solution

Analytical solutions to reservoir flow equations are only obtainable after making simplifying assumptions in regard to geometry, properties and boundary conditions that severely restrict the applicability of the solution. For most real reservoir fluid flow problems, such simplifications are not valid. Hence, there is need to solve the equations numerically.

Discretization

We will solve, as a simple example, the linear flow equation numerically by using standard finite difference approximations for the two derivative terms:

t

P

k

φμc

x

P2

2

Derivative terms

Continue

123

45

67

89

10

1 2 3 4 5 6 7 8 9 10

XY

BACK

Z123

Grid Pattern• Block Centered (BC) Grid: These are specified by the

dimension and depth of the top. All faces are vertical to each other. There is no point at the boundary. The total length (L) is divided ‘N’ number of grid , then grid size (Δx) will be L/N.

• Corner Point (CP) Grid: Corner points are specified and faces are not perpendicular to each other. The total length (L) is divided ‘N’ number of grid , then grid size (Δx) will be L/(N-1). Grid size is larger than CP geometry.

BC & CP both are used in ECLIPSE simulator. If structure is horizontal, then either may be used. But for slopping structure and change in slope or if there be any fault, BC can’t be used.

• Cell description in BC is easier and radial model easy to construct. But, irregular grid is difficult to construct using BC. It gives incorrect cell construction across the fault; pinch out and erosion surfaces are difficult to model fruitfully. All these cases corner point geometry is used preferably. However, as the cell description is complex in CP geometry compared to BC, the later is preferable wherever possible.

Block Centered or Point distributed grid

Corner Point or Lattice Grid

Time Step size selection• Time step size depends on the reporting

interval. Truncation error is in the order of time step size. Smaller the Δt, less the truncation error, but number of calculation is more.

• Discontinuity in saturation creates the problem in convergence. Generally, calculation should be started with small Δt and consecutively increased in Δt and accuracy to be checked. If accuracy remain almost at the same range, larger step should be chosen.

• Δt should be chosen such that there is less than 10-20% change in fluid saturation in a block containing the well. For irregular cell model of varying dimension,

Δtmax=min [0.1ø Δxi

ΔyihSo/(qtx5.615)]i=1, N

• First, the x-coordinate must be subdivided into a number of discrete grid blocks, and the time coordinate must be divided into discrete time steps. Then, the pressure in each block can be solved numerically for each time step. For our simple one dimensional, horizontal porous slab, we thus define the following grid block system with N grid blocks, each of length Δx:

• This is called a block-centered grid, and the grid blocks are assigned indices, i, referring to the mid-point of each block, representing the average property of the block.

1 Ni-1 i i+1

Dx

Continue

Discretization (cont)

Notations to be used throughout the discussion

Finite Difference Approximation

Forward Difference Approximation Backward Difference Approximation

Finite Difference Approximation

Central Difference Approximation

Discretization of Flow Equation

Approximation of the time derivative

At constant position, x, the pressure function may be expanded in forward direction in regard to time:

...,'''!3

,''!2

,'!1

,,32

txPt

txPt

txPt

txPttxP

...,'''!3

,''!2

,'!1

,,32

ttxPt

ttxPt

ttxPt

ttxPtxP

...,''2

,,,'

txPt

t

txPttxPtxP

• By solving for the first derivative, we get the following approximation:

tOt

PP

t

P ti

tti

t

i

...,''2

,,,'

txPt

t

txPttxPtxP

tOt

PP

t

P ti

tti

tt

i

• The pressure function may be also expanded in backward direction in regard to time:

Click for solution

Continue

This expression is identical to the expression above.

Solution Techniques• Simplification of complex equations of one

dependent parameter (P) calculating other dependent parameters (S, Pc explicitly) by IMPES equation.

• Conversion of PDE into simple linear algebraic equation using finite difference method.

• Check of stability criteria for correct step size selection using von Neumann analysis or matrix method.

• Conversion of 2-D, 3-D equations using ADIP, LSOR techniques-check the stability criteria for iterative process.

• For large variation of saturation SIP technique is used without simplification.

• Solution of simplified matrix using different solution methods like Gauss elimination, Gauss Seidel, Jecobi (direct or iterative process) etc.

Data Preparation

Important step of simulation study.Necessity: • GIGO-garbage input-garbage output…Quality of

the out put of the simulator depend on the quality of input.

• Data available for Simulation from various sources which may not be directly accessible to simulator engineer.

• Data not in the form to be directly used in simulation

• May not be available in directly, required matching with similar field by regression analysis

Data Preparation

Types of data required in simulation

• Fluid data• Rock data• Production data• Flow rate data• Mechanical and operational data• Economic data• Miscellaneous data

Fluid data• Formation volume factor

gas, oil and waterSources: laboratory studyInput form to simulator-generally polynomial • Fluid viscosity- gas, oil and waterSources: laboratory study, literatureInput form to simulator-polynomial, look up table

(in array form)

• Solution gas oil ratio: Sources: laboratory study, literature

Input form to simulator-polynomial

Rock Data

Required data• Permeability• Porosity• Formation thickness• Formation elevation• Compressibility• Relative permeability• Formation fluid saturation• Capillary pressure

Various rock parameters are needed to define the physical extent of the reservoir and to evaluate the transmissibility during simulation run. The data must be in some input form.

Sources of permeability data

• Well test analysis (build up an fall of test) using Muskat method, Miller Dyes Hitchinson method, Horner method or type curve analysis

• Initial potential test• Regression analysis (Case history approach)• Laboratory measurement• Interference test

• Logging data (sonic or acoustic log)• Laboratory measurement• Published correlation

Sources of porosity data

Sources:Gross or Net isopach mapsStructural data (top structure-

bottom structural contour)Application

• Gross isopah maps give the correct evaluation of fluid flow characteristics to the simulator

• Net Isopach helps in determination of OOIP

Reservoir thickness data

Input form of rock data

• Reservoir rock data are generally available in discrete form. However, they are required at each and every point of the reservoir as the reservoir are divided into a number of grids.

• Singular or combined contour maps of the data are prepared first by interpretative or mechanical contouring method.

• Digitization of the contour maps before input to simulator.

Rock compressibility

• Sources: Laboratory measurement

Relative permeability data

• Required dataGas-oil, oil-water, gas-water relative permeability

• Sources: Lab measurement, using capillary pressure, Filed data, published correlation

Saturation data

• Sources: core data, log data & capillary pressure

• Requirement-estimation of reserve, flow characteristics

Capillary pressure

• Sources- laboratory measurement• Requirement- in IMPES equation to

quantify the pressure in each grid.

Production data

• Sources: field data• Production data of all the fluids must

be available continuously with time.• Smoothing of flow rate vs time is

necessary before feeding to the simulator.

• Input is best in the tabular form

Flow rate• Required by simulator to compute

producing capacity of a well within the system.

• Data are based on- Productivity index, Injectivity index, Optimum flow rate and maximum allowable flow rates.

Correlation of flow rates with BHP, GOR etc. parameters are generally made within the simulator to compute the above parameters.

Eg.- FBHP=ao+ax+bx2+cy+dy2+exy,

where ‘x’ is the production rate and ‘y’ is GOR

• Sources: Log data & Drilling records• Application: flow characteristic, well

perforation

Formation elevation data

Pseudo functions

A major challenge in Reservoir Engineering simulation is to develop the simplest model that will allow proper decisions to be made regarding the reservoir development and operations.

One of the approach is to use pseudo-functions in place of original parameters which will give much better result.

Pseudo functions-classification

Pseudo functions

Inter block

Vertical Equilibrium

Dynamic Equilibrium

Well

Inter block Pseudo-function

• Purpose: Describe flow between grid blocks.

Since interblock flow is controlled by relative permeability functions, which in turn depend on saturation, generating interblock pseudofunctions involves averaging the saturations in the block of interest. There are two basic kinds of interblock pseudofunctions:

Analytical or vertical equilibrium and Dynamic.

Vertical Equilibrium

h

h

xyxy

dzxh

dzzkh

k

0

0

)(1

)(1

owl

dzzk

dzzkzk

k h

xy

h

rlxy

rl ,,

)(

)()(

0

0

owl

dzz

dzzSz

Sh

h

l

l ,,

)(

)()(

0

0

Assumptions: • Capillary pressure is small. • Permeability in vertical direction

is high• Equilibrium in flow exists always

Dynamic equilibrium

• No vertical equilibrium• Flow rate in vertical direction is much low

compared to lateral direction.• Total production or flooding time is

divided into number of time steps as done in simulation. Vertical equilibrium is assumed at a particular time step for a block of grids.

• Pseudofunctions are determined for that particular time step.

• Correlation is made for different time steps.

• Thus, 3D model can be converted to 2D cross sectional model with almost same accuracy as in 3D.

Well Pseudofunction

• Necessity: Because of converging flow pattern and coning phenomena associated with the wells, special techniques are required if one desires to capture these effects in well blocks of a cartesian simulator.

Methods to derive well Pseudo functions

• Project the effect of vertical flow by he way of pseudorelative permeability into well blocks.

• Semi Analytical method• Transplantation of pseudo

functions generated by a well coning model into a cartesian model.

History MatchingWhat it is? Process of modifying the existing model data until a reasonable comparison is made with the observed data is called history matching.

Why?To make any sensible predictions with the

simulator because the same mechanisms operative in the history period of the reservoir still be operative in the future prediction period.

Simulator must adequately describe the geometrical configuration, rock properties, fluid properties and flow characteristics.

Data used in the simulator must be modified until simulator produced

ObjectiveDetermine reservoir description which will minimize the difference between the observed parameters and predicted parameters.

History Matching

History matching parameters

• Pressure• Flow Rates• Gas Oil Ratio• Water Oil Ratio

Feedback Control Logic for History Matching

Process of history matching is characterized by a feed back loop

Mechanics of history matching

There are several parameters which can be varied either singly or collectively to minimize the differences between observed data and calculated data by the simulator.

Modifications are made on the parameters given in the following table

Two fundamental Controllable Processes in

History Matching

• Quantity of fluid in the system at any time and its distribution within the reservoir.

• Movement of fluid within the system under existing potential gradient.

Contd…

Parameters adjusted in history matching

• Reservoir and aquifer transport capacities, (kh)res and (kh)aq

• Reservoir and aquifer storage (fhA)• Relative permeability function• Capillary pressure function• Original saturation function Modification of these parameters

enables to change the matching parameters e.g. production data, GOR, pressure, flow rates.

Modifications using rock data

If gradient between the low pressure area and high pressure area is too high compared to the actual one, predicted production rate will be much high compared to the field data. This could be adjusted by

– Move the fluid from high pressure to low pressure zone by a change in rock permeability (increase the value)

– Decrease the oil in place in high pressure area either by (i) decreasing porosity (ii) decreasing thickness (iii) decreasing oil saturation (iv) all of the above

– Increase oil in place in the low pressure area either by (i) increasing porosity (ii) increasing thickness (iii) increasing oil saturation (iv) all of the above

Most likely change is the modification of rock permeability.

Observation: Predicted production rate is much higher than actual

• Usual error involved in fluid data are caused by faulty input. Misplaced decimal or incorrect exponent can cause an order of magnitude error in the input, and hence the out put.

Example (1) : No noticeable draw down in the pressure in the model even after considerable withdrawal of fluid.

Reason: Rock compressibility is too high by an order of magnitude; causing the effects of very low or negligible saturation change.

Action: use correct compressible data.

Example (2): Water saturation appears to increase in model without any injection or influx of water.

Reason: Input rock compressibility is too low, causing free volume to develop in the free space. This free volume is filled with immobile fluid, i.e. (usually) water.

Action: Correct the rock compressibility.

Modifications using Fluid Data

Example of history matching

Automatic history matching Purpose : • To remove the drudgery from the history matching

process by letting the computer do most of the work, including analysis.

• There are several algorithms presented in the literature that are meant to do just this.

• All automatic history matching algorithms use the principle of nonlinear optimization to achieve the best match of the observed data. In order to do this, an objective function is defined based on the history matching parameter. This objective function is usually a function representing a measure of total error between predicted and observed data. The strategy is to minimize this error to yield the best match. Table given in next slide summarizes the basic equations.

Summary: the basic equations.

Forecasting • The ultimate goal of any modeling effort is

forecasting. The modeling involved in reservoir simulation is no exception.

• It is therefore imperative to ensure that a model has the necessary predictive capability before using it as a forecasting tool.

• As we have learned, we ensure predictive capability by formulating an accurate representation of the reservoir, properly solving the resulting equations, and proving the validity of the model through history matching.

• Once we have taken these steps, the simulator is ready for its primary purpose of forecasting.

Prediction Study

Key parameters in prediction

UpdatingRarely do we have available all the

information that we need at the beginning of a simulation study. In fact, a basic tenet of engineering is using the available information—as inadequate as it may be—to come up with a "best" solution. This solution is then improved as more information becomes available. This process called updating. There are two methods of updating in reservoir simulation: updating the reservoir model itself, and revising the simulation approach.

Prepare Report

Any Query?

Questions• What do you mean simulation?• Name some application o reservoir simulation• Name the most frequently used reservoir simulation model.• What are the different parameters of history matching?• Name one or two pseudofunctions.• What are the various boundary conditions used in reservoir simulation?• How do you select the number of grids/cells for simulation?• State one difference between Neumann and Dirichlet boundary conditions.• For a reservoir having sealed fault, which boundary condition will you apply?• For conning, what type of grid (BC or CP) will you apply? • Write two basic differences between compositional model and black oil model.• For volatile oil and gas condensate reservoir what type of model should be used and

why?• Name some history matching parameter.• Name some numerical solution methods• What are the different grid geometry. Name one situation where elliptical grid is used.• Where spherical flow pattern is used?

• In compositional model, if number of components be 5, how many equations are to be solved?

• What is necessity of data preparation for simulation?