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Reservoir Engineering ----- Goals
Development of oil & gas fields in an optimal and
economical manner
Maximize hydrocarbon recovery at minimum cost
• How much hydrocarbon is present?
• How much can be recovered?
• How fast can it be recovered?
Hydrocarbon in-place
Hydrocarbon Reserves (Producible quantity)
Rate and duration of Production (field life)
Reservoir
performance
analysis
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Reservoir performance analysis
Five methods, namely – Analogical, Experimental, Statistical, Analytical and
Mathematical
Analogical method
• Using mature reservoir properties that are similar to the target reservoir
to predict the behavior of the reservoir
• This method is especially useful when there is a limited available data
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Reservoir performance analysis
Analogical method - case study
Deeper reservoir- Assam Asset: Depth:>4000m, Rec: 6% of OIIP, Phi: 8-12%, Perm: 10-40 mD, Flowing
strings: 7
• One probable reason for higher recovery - more wells drilled into deeper reservoir
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Reservoir performance analysis
Experimental method
• Measure the reservoir characteristics in the laboratory models and scale
these results to the entire hydrocarbon accumulations
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Reservoir performance analysis
Statistical Method
• In this method, the past performance of numerous reservoirs is
statistically accounted for to derive the empirical correlations which are
used for future predictions
• It may be described as a 'formal extension of the analogical method'
The analytical approach
• In most of the cases, the fluid flow inside the porous rock is too
complicated to solve mathematically
• Systematic and logical approaches are used to predict future
performance
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Reservoir performance analysis
Mathematical method
• Applied basic conservation of laws
• The three basic equations:
• Material balance equation or continuity equation
• Equation of motion or momentum equation – Darcy’s Law
• PVT or equation of state
• These three equations are expressed for different phases of the flow in the
reservoir and combine to obtain single equations for each phase of the
flow
• The mathematical method traditionally includes material balance
equation and decline curve methods
• The advanced mathematical model – reservoir simulation
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Material Balance Calculations --- Tank model
We N=OIIP
Assumptions:
• The material balance calculations are
based on tank model
• Homogeneous pore volume, gas
cap, aquifer
• Constant temperature
• Uniform pressure distribution
• Uniform hydrocarbon saturation
distribution
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Decline Curve
Decline Curve
• The rate of oil production decline generally follows one of the following
mathematical forms: exponential, hyperbolic and harmonic.
The following assumptions apply to the decline curve analysis
• The past processes continue to occur in the future
• Operation practices are assumed to remain same
Major producing field of Assam, Ult Res: 38000 Mm3
Generated by OFM
• Recovery: 42% of Ult Reserves
• In 2047 recovery: 50% of Ult.Res
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What is Reservoir Simulation
The process of mimicking and inferring the fluid flow behavior in a
petroleum reservoir system through the use of physical and mathematical
models
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Why Reservoir Simulation
Best estimate for development planning/performance review
Understanding of present setup of field responsible for
exploitation
Justify investment decision, review expected/ pending profile
Reduce uncertainties
Best tools for Reservoir Engineers
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Reservoir Simulation ----- Concept
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Reservoir Simulation --- Equations
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Darcy’s Law
• Practically all reservoir simulation studies involve the use of Darcy's law
• It is important to understand the assumptions behind this momentum balance
equation
• The fluid is homogenous, single-phase
• No chemical reaction takes place between the fluid and the porous medium
• Laminar flow condition prevails
• Permeability is a property of the porous medium, which is independent of
pressure, temperature and the flowing fluid
L
P
Q
kA
001127
.
0
Q = flow rate, bbl/d
A = cross sectional area, ft2
μ = fluid viscosity, cp
K = permeability, md
P = pressure, psi
L = length, ft
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Reservoir Simulation --- Equations
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Equation of State (EOS)
Equation of state (EOS) or PVT behaviour
The material balance in reservoir simulation is usually done on the
following basis
• For gas, the real gas law is used
• The liquid phase has dissolved gas, which is a linear function of
pressure (black oil)
• Water is characterized as a liquid of low compressibility, which
is a linear function of pressure
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Reservoir Simulation Model build-up
• Preparation of earth model – Static model
• Up-scaling of static model - Coarsening
• Preparation of dynamic model – Simulation model
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Reservoir Simulation --- Static
model
• Incorporate structural and fault framework into the model
with gridding
• Petrophysical modelling –property modelling
• Volumetric calculation
Structural modelling
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Reservoir Simulation --- Static
model
• Petrophysical modelling
Porosity distribution
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Reservoir Simulation --- Static
model • Petrophysical modelling
Saturation distribution
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Reservoir Simulation --- Static
model • Volumetric calculation
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Reservoir Simulation --- Static
model
After completion of static model -----
• Earth model prepared
• Model with structure top-bottom & faults
• Porosity distribution
• SW (water saturation) distribution
• NTG distribution
• OWC/ GOC if present
• Define FVF (PVT data for volume calculation)
• Volumetric estimation - OIIP
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Up-scaling --- Static
model
• Scale up the structure and properties onto a coarser grid
• Up-scaling is the process of creating a coarser (lower resolution)
grid based on the geological grid which is more appropriate for
simulation
• Static model can contain tens of million cells
• Simulation is usually suitable one lakh to one million cells
• To prepare simulation grid as close to orthogonal
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Up-scaling --- Static Model
Up-scaled the model (Static to Dynamic)
Up-
scaled
the
model
Static model with 1 m
vertical thickness
Dynamic model with 3 m
vertical thickness
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Up-scaling --- Static Model
Up-scaled the model (Static to Dynamic)
Static
model
1m thick
Dynamic
model
3 m thick
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Up-scaling --- Static Model
0 100
0
Permeability,
mD
Stratigraph
ic Model
Dynamic
model
Static model
Log scaled
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Reservoir Simulator --- Black Oil
Conventional “Black Oil” simulators
Simulators (IMEX/ECLIPSE-100)
• Oil & Gas phases are represented by one ‘component’
• Assumes composition of gas & oil components are
constant with pressure & time
• Assumes temperature is constant throughout the reservoir
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Reservoir Simulation --- Dynamic modeling
Preparation of dynamic model & Data Input
• Permeability modelling
• Define fluid properties – PVT of water, oil & gas
• Define dynamic flow property – relative permeability
• Define rock property - compressibility
• Define capillary pressure
• Define initial pressure of the model
• Define aquifer – if present
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Dynamic Model ---Permeability
modeling
1. Porosity-permeability correlation based on laboratory generated
basic core data
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Dynamic Model --- PVT Data
• PVT data of water, oil & gas
• Laboratory generated PVT data of oil & gas
• Water PVT data – water FVF, water compressibility & water viscosity
• Surface density of water, oil & gas
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Dynamic Model --- Relative Permeability
• Relative permeability of water, oil & gas system
• Laboratory generated SCAL data is used
• If different regions are defined in the model, region-wise rel-perm data have to
incorporated – if available
• If more than one set of rel-perm data are available, normalize will be done to
average and again de-normalize is to be done
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Dynamic Model --- Capillary Preesure
• Capillary pressure is used to define the fluid distribution in the reservoir
• Laboratory generated capillary pressure data is used
Transform lab capillary pressure data to reservoir conditions
Pc (Reservoir conditions) = Pc (Lab) x (Sigma.Cos(theta))res /(Sigma.Cos(theta))lab
Typical values of Sigma.Cos(Theta) are;
Lab
Air-Water: 72
Oil-Water: 42
Air-Mercury: 367
Air-oil : 24
Reservoir
Water-oil : 26
Water-gas : 50
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Dynamic Model --- Aquifer
• Define aquifers, describing the type, size and connections of the acting
aquifer.
• Aquifer modeling is a method of simulating large amounts of water (or
gas) connected to the reservoir whereby it is not essential to know how
the fluid moves in it, but rather how it affects our reservoir.
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Dynamic Model --- Aquifer
modeling
• Numerical aquifer: A set of cells in the simulation grid is used
to represent the aquifer. Their position in the model is irrelevant
• Fetkovich aquifer: The aquifer flow model is similar to the well
inflow model. It is best suited for smaller aquifer that may
approach a pseudo steady-state condition quickly
• Carter Tracy Aquifer: It uses tables of dimensionless time
versus a dimensionless pressure as influence function
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Reservoir Simulation ---Initialization
The purpose of initialization is to specify or obtain the initial conditions
of the model – pressure, saturation and solution GOR
Equilibrium condition has been assigned in the model considering
• OWC and GOC
• Initial pressure – region wise
• Capillary pressure – if required
• Run the model
• If no error, compare the volume (OIIP) with static model
• Dynamic model is ready
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Dynamic Model --- Well data
Well Data
• Well locations, trajectory, completions
• Workover history – Z/T, squeeze, HF
• Production rates of oil, water and gas as a function of time
• Pressure history of the wells – bottom hole pressure (flowing
& static), build-up pressures
• Injection history – rates, fluids, pressure, etc
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Reservoir Simulation --History matching & prediction
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Reservoir Simulation ---History matching
• Normally the most time-consuming phase of a simulation study
• Used to demonstrate the validity of the simulation model
• Input historical production rates of oil/gas then simulator calculates
pressures and secondary products (GOR, WC, etc.) to be compared
with history
• If needed, compare calculated and actual performance of individual
wells
• Adjust model input parameters to achieve an acceptable match
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Reservoir Simulation ---History Matching
Parameter Adjustment: History Matching
• Adjust reservoir permeability to match field pressure gradients
• Adjust permeability and areal extent of shales or other low-perm
zones to match vertical fluid movement
• Adjust relative permeability-saturation relationships to match
dynamic saturation distributions and pressure gradients
• Adjust aquifer size, thickness, and permeability to match the
amount and distribution of natural water influx
• Use pore volume multiplier to adjust OIIP of the model
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History matching & Uncertainty
The following variables are often considered to be
indeterminate (high uncertainty):
- Pore volume
- Permeability
- Transmissibility
- Kv/Kh ratio
- Rel. perm. curves
- Aquifer properties
- Mobile oil volumes
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History matching & Uncertainty
The following variables are often considered to be determinate (low
uncertainty):
- Porosity
- Gross thickness
- Net thickness
- Structure (reservoir top/bottom/extent)
- Fluid properties
- Rock compressibility
- Capillary pressure
- Datum pressure
- Original fluid contact
- Production rates
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History Matching --- Case Study
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History Matching --- Case Study
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History Matching --- Case Study
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History Matching --- Sensitivity
Analysis
• Deeper reservoir of Assam
• Initial run
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History Matching --- Sensitivity
Analysis
Tornado Plot
Uncertainty & Optimization: Petrel-Eclipse
CMOST: CMG
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History Matching --- Sensitivity
Analysis
• Deeper reservoir of Assam
• Initial run
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History Matching --- Sensitivity
Analysis Well- wise history match
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Prediction
After satisfactory history matching, field performance is predicted
Limiting Conditions
• FBHP
• Water Cut
• Well abandonment rate
• Variant-I: Base case (considering available producers and injectors)
• Variant-II
• Variant-III
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Prediction ---- Base Case
• Base case: Field performance is predicted with available OP (9)
and WI (7)
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Prediction --- New Infill Locations
Conventional Method
• New locations are generally identified based on average
remaining oil saturation and current pressure map after
history matching
• Locations are predicted on history matched model with
available producers and injectors
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Prediction --- New Infill Locations
Average So map after history
Conventional Method
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Prediction --- New Infill Locations
Simulation Opportunity Index method, SPE 148103
• SOI is calculated on history matched model and identified 3 locations and
subsequently predicted with other producers and injectors
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Prediction --- New Infill Locations
Simulation Opportunity Index method, SPE 148103
SOI Map
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Prediction --- New Infill Locations
Opportunity Index Method, SPE 122915
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Prediction --- New Infill Locations
Opportunity Index Method, SPE 122915
Opportunity Index Map
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Prediction --- New Infill Locations
Case:2: Available producers +3 new development locations
Np: 2.130 MMm3, Recovery: 32.87% of model OIIP
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Development Scheme
Case-1: BAU case (Business as usual) – with existing OP and WI or GI
Recovery:
Case-2: With new development OP- depletion case
Recovery:
Case-3: With new development OP + WI – IOR Scheme
Recovery:
Case-4: With new development OP + WI + EOR – EOR Scheme
Recovery:
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Types of Reservoir Simulators
Special-purpose simulators can model compositional,
thermal, and chemical processes in EOR projects
• Compositional simulators can model performance of volatile-
oil and gas-condensate reservoirs in which phase
compositions vary widely with pressure (GEM, Eclipse-300)
• Thermal-process simulators can model steam cycling and
steam flooding (STAR, Eclipse-500/Eclipse Thermal)
• Chemical-processes simulators can model polymer injection,
surfactant flooding, and flooding with alkaline solutions (STAR,
ECLIPSE-100)
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Reservoir Simulation --- Compositional
• It is useful when the behavior of the hydrocarbons is complex—
condensate or volatile crude oil, or miscible gas injection
developments fall in this category
• In black oil simulation, flow is considered in terms of oil, water and
gas where no mass transfer between the phases is considered
(except that of gas between oil and gaseous phase). In
Compositional simulation flow is considered in terms of oil, water
and gas but mass transfer between phases is also considered.
• In Black-oil simulation, summation of phase saturations is unity
while in the compositional simulation summation of mole fraction
of different chemical components is unity.
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Reservoir Simulation --- Thermal
• It is used to simulate thermal EOR processes like In-situ combustion,
Steam injection, etc.
• Here, heat energy conservation is also performed.
• Thermal recovery methods are typically used in heavy oil reservoirs where
the oil viscosity is high at reservoir temperatures, but reduces as the
temperature increases
A number of thermal recovery processes can be simulated
• Steam injection, such as cyclic steam injection (huff and puff), steam flood,
or steam
• Hot fluid or gas injection
• Well bore heaters
• Combustion
