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Reserves and Resources Core

The Underlying Business of Reserves Management

This section will cover the following learning objectives:

Learning Objectives

Explain importance of integration with other disciplines

Recognize calculations using the volumetric formulas for gas andoil

Explain the importance of dividing into flow units for dynamicreserves in reservoir simulation

Describe what reserves management is and how to do it

Reserves and Resources Core ═══════════════════════════════════════════════════════════════════════════════════

©PetroSkills, LLC. All Rights Reserved. _________________________________________________________________________________________________________

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Reserves Management

RESERVESMANAGEMENT

PRODUCTION

Discovery

Delineation

Development

PrimarySecondary

Tertiary

Abandonment

Exploration

Basin

Play

Prospect

Disposal

Reserves management is a key aspect for the reservoir engineer throughout field life

Definitions

Both R & R estimates are uncertain

Reserve classification recognizes uncertainty

Maximum resource size

Minimum resource size

Scope for future recovery (SFR)

Maximum reserves

Minimum reserves



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Definitions

Proven reserves should be based on current economic conditions, including all factors affecting the viability of the projects …. the term is general and not restricted to costs and price only

Probable and possible reserves could be based on anticipated developments and/or the extrapolation of current economic conditions

Reservoir Engineering Functions

Main Functions Tied to Resources and Reserves

Estimation of the original hydrocarbons in place

Estimation of the recoverable reserves and recovery factors

Analysis of past performance

Prediction of future performance

Updating reservoir model as data improves



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Resources versus Reserves

Recovery Factor (RF)

Reserves

Resource

(Recoverable hydrocarbons)

(Hydrocarbons-in-place)

Technical Factors

Reservoir Quality

Reservoir Drive

Method of Operation

Economic Factors

Oil and Gas Prices

Concession Terms/Taxes

Costs

Market

Contract

Influenced by:



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Not All Hydrocarbons are Reserves

Not economic with current technology

Not yet discovered

No market

Cannot be produced

within license term



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Common International Hydrocarbon Contracts

Net Profits Interest (NPI) Rights holder has no

responsibility for costs

Rights holder recovers a percentage against the Operators Net Operating Cash Flow

Reserves are obtained by conversion back to volumes as a PS contract by the current year average price

Net Royalty Interest (NRI) Hydrocarbons are

a gross percentage of all production

Rights holder carries none of the costs, only enjoying benefits of production

Normally reserved for governments or mineral rights owners

Production Sharing (PS) Rights holder

carries all of the cost

Rights holder is allowed to recover costs by converting production to a currency amount and recovering capital/operating costs according to a percentage against cost of gas or oil

Open the supplied spreadsheet: NRI-PS-NPI_Contract_Comparison_of_Reserves.xlsx

Gas versus Oil Field Impacts of Contracts

Same elements as oil reserves, with difference in types of models that work best

High recovery requires significant reservoir pressure depletion

Often driven by sales contract

Strong link between surface and sub-surface

Gas Field Reserves



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Gas Well Performance

Actual Rate

Potential Rate

TailGas

CompressionNo Compression(60% Recovery) Stage I

(30% Recovery)

Stage II(10%

Recovery)

0

20

40

60

80

100

120

Time

Pressurepsig

[MPa]

Reservoir Pressure

Surface Pressure 1600 psig800 psig 500 psig0

2000

4000

6000

Gas RateMMSCF/D

[MMSCM/D]

[41]

[28]

[18]

[11 MPa][6 MPa]

[3 MPa]

[3.4]

[2.8]

[2.3]

[1.7]

[1.1]

[0.6]

= DCQ/SDC Low load factor means a lot of facility/production

capacity sits idle for much of the time

The maximum daily rate that the seller must be able to produce (generally specified monthly)

ACQ/365: average daily gas sales rate during a year

Total volume of gas to be sold during the year

Gas Contract Terminology

Annual Contract QuantityACQ

Daily Contract QuantityDCQ

Seller’s Delivery Capacity or Max Daily RateSDCMDR

Load FactorLF



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Typical Gas Contract Requirements

0

20

40

60

80

100

120

140

160

180

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

MM

scf/

dSDC

Take

DCQ[3.4]

[2.8]

[2.3]

[1.7]

[1.1]

[0.6]

[4.0]

[4.5]

[5.1]

[MM

scm

/d]



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Hydrocarbon Volume – Static View of Elements

In the reserves process, there is a creative positive tension between the static view of a Geoscientist and the dynamic view of a Reservoir Engineer.

Derivation Mapping

Well logs, seismic; models

Well logs, cores

Well logs, seismic; models

Petrophysics

Interpretation

Element Area

Thickness

Porosity

Net to gross

HC saturation

Recoverability (reserves)

Reserve Determination

Reserve Determination

Volumetric model comparison and corroboration

Integrating both:

Disciplines – most importantly geology and geophysical

Production System – Sub-surface and surface functions

Value

Flow models



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Offshore Asset

Asset Data Coverage

The magnitude of the uncertainty decreases with more data coverage, which may sometimes be correlative with time

Reserve and Resource Evaluation Throughout Field Life

Relative Risk

Production profile

Range of estimates

Study Methods

Field phase Appraisal Devel. Field review/special projects

Time

Rate

Cumulative

Low

High

Actual Recovery

Volumetric

Performance



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The Dynamic View – Integrated Production System

Symmetry: Does it Exist?

P10 and P90 Evenly Distributed Around Ultimate Predicted EUR (BBLS/M3/SCF/M TONS)

True reserves at this time are

dependent on:• Technology

available• Company view• Etc.

BTE



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Human Factors Affecting Estimates

Time

Estimated ultimate

recovery

Initial

Initial Recovery Estimate

Reserve Growth

Besides technical and economic factors, there are also human factors:

• Cognitive biases

• Heuristic biases

• Misdirected motivational systems

Estimates of ultimate recovery grow with time, improved understanding,technology, etc.

• Financial markets expect this growth

• Not always the case, resulting in overbooking



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This section has covered the following learning objectives:

Learning Objectives

Explain importance of integration with other disciplines

Recognize calculations using the volumetric formulas for gas andoil

Explain the importance of dividing into flow units for dynamicreserves in reservoir simulation

Describe what reserves management is and how to do it



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Analysis Principles of Uncertainty in Reservoirs



Learning Objectives

Describe the importance of reservoir uncertainty

List some typical reservoir uncertainties and their categories

Understand the relationship of statistical parameters toprobabilistic parameters

Explain what a Probability Distribution Function (PDF) is

Recognize that both the Cumulative Distribution Function (CDF) –also known as the S-Curve – and the Exceedance DistributionFunction (EDF) are derived from the PDF and are just twodifferent ways of presenting the same data

List some typical sources for uncertainty ranges

Describe the typical distribution shapes for reservoir uncertainties,and their advantages and disadvantages



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Probabilistic Approach to Hydrocarbons in Place

Utilizes uncertainty analysis as well as statistics and statistical rules for combining distributions

A range of distribution of values is constructed for each input parameter to a calculation

There are two ways to combine distributions with multiple variant input parameters

Resulting distributions are combined to produce a final estimate of Hydrocarbons Initially in Place (HIIP) ranges

Objectives of Probabilistic Method

Identify interventions to mitigate against potential downsides or to exploit potential upsides

Quantify range/distribution of HIIP, reserves development plans and production profiles

Identify key variables which contribute significantly to uncertainty, appraisal targets



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Does a Structured Approach Help?

You can use astructuredapproach toestimateuncertainties andincorporate theminto a realisticrange of forecasts

Properly assessinguncertainty rangesand their physicalmeaning is thefoundation tomaking goodreserve estimates

Bias can lead to anunrepresentativeview of a project’suncertainty



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Ranges and Distributions

Pro

babi

lity

of O

ccur

renc

e (%

)

Value

Probability Density Function (PDF) Cumulative Distribution Function (CDF)

Pro

babi

lity

of V

alue

or

less

(%

)

Value

Communicating Uncertainties in an Assessment

Pro

babi

lity

of V

alue

or

less

(%

)

Value

The “S-curve”

Management requires ranges ofSTOOIP, rates, EUR and thelinkage to value

These forecasts shouldincorporate the full range ofuncertainty

A project is characterized by theentire S-curve, not just theExpected Value

When you have translated this tothis S-curve, you havecommunicated all of the risks anduncertainties into it

Projects will have different riskprofiles based on the shape oftheir S-curves



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Smaller range of likely outcomes,smaller uncertainty

Range of Values

Larger range of likely outcomes, larger uncertainty

Pro

babi

lity

Uncertainty is generally treated as a continuous probability model where the result can vary anywhere between a selected minimum and maximum


Uniform

I know the max and min, but otherwise I have no idea as to what lies between

Log-Normal

I know the approximate range, but the probability is asymmetric, positively skewed toward higher values

Triangular

I know the min, most likely, and the max

Normal

I know an approximate range, and I know it is symmetrical about the most likely

Discrete

I know an approximate range, and I know it is symmetrical about the most likely

Histogram

I know distinct numbers that are integers of the parameter, i.e. the number of wells drilled cannot be a fraction of a well. Or my measurement is only accurate to an integer value

Double Triangular

I known it is log normal, but I only know the limits, so I’ll use a simpler function, until I know better



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Frequency Chart

Certainty is 97.84% from 4.8 to 33.8

Mean = 16.6.000

.006

.011

.017

.023

0

28.25

56.5

84.75

113

4.8 12.1 19.3 26.5 33.8

5,000 Trials 108 Outliers

Forecast: Zone 1 Recoverable Oil (mmbo)

Mode: Most likely

Probability Density Function (PDF)

Cumulative Chart

Certainty is 97.84% from 4.8 to 33.8

Mean = 16.6.000

.250

.500

.750

1.000

0

5000

4.8 12.1 19.3 26.5 33.8

5,000 Trials 108 Outliers

Forecast: Zone 1 Recoverable Oil (mmbo)

Median: Equal probability of being bigger or smaller than (P50)

Mean: Expected value or average

Cumulative Distribution Function (CDF)

For this example

Mean: 16.6 Median: 15.4 Mode: 12.9

Exceedance: Another Way to Look at Distributions and their Combination

HISTOGRAM

05

101520

1 2 3 4 5 6 7 8 9 10 11 12

0

5

10

15

20

1 2 3

0.0

20.0

40.0

60.0

80.0

100.0

1 3 5 7 9 11

0102030405060708090

100

1 2 3 4 5 6 7

PDFs EDFs (derived from PDFs)



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HISTOGRAM

05

101520

1 2 3 4 5 6 7 8 9 10 11 12

0

5

10

15

20

1 2 3

0.0

20.0

40.0

60.0

80.0

100.0

1 3 5 7 9 11

0102030405060708090

100

1 2 3 4 5 6 7


P(x) ≥ x


HISTOGRAM

05

101520

1 2 3 4 5 6 7 8 9 10 11 12

0

5

10

15

20

1 2 3

0.0

20.0

40.0

60.0

80.0

100.0

1 3 5 7 9 11

0102030405060708090

100

1 2 3 4 5 6 7


P(x) ≤ x



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Statistical Parameters from the CDF

Cumulative Chart

Mean = 349.86.000

.250

.500

.750

1.000

0

10000

305.58 327.72 349.86 372.00 394.13

10,000 Trials 9,912 Displayed

Forecast: One hundred

P90 or is it P10? P10 or is it P90?P50

Recognizing Uncertainty in the CDF

90%

Reserve Volume

Cumulative probability reserves greater than this volume

50%

10%

Proven

Probable “expectation”

Possible



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Exceedance versus Cumulative

E

x

P(x)

Exceedance at this P(x) ≥ to value of x

Cumulative at this P(x) ≤ to value of x

Ʃ



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Combination of Parameters Methods

Monte Carlo method Latin hypercube Parametric method

1 Generate curves for individual parameters

2 Combining parameters to obtain an estimate fora reservoir

Combining Distributions

Combining (e.g., adding ormultiplying) two or moreprobability distributions is notalways a simple task

You can combine mean valuesto get resultant mean values

But you generally cannotsimply combine other points onthe distribution

P10a + P10b + P10c ≠ P10total in most cases

There are mathematic formulasthat can combine somedistributions, or that provideclose approximations withothers.

A practical way to combine distributions is by Monte Carlo simulation

Randomly samples each inputdistribution

Combines (e.g., adds ormultiplies)

Sorts the resultant distribution.

Accuracy generally increaseswith the number of iterations



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Learning Objectives

Describe the importance of reservoir uncertainty

List some typical reservoir uncertainties and their categories

Understand the relationship of statistical parameters toprobabilistic parameters

Explain what a Probability Distribution Function (PDF) is

Recognize that both the Cumulative Distribution Function (CDF) –also known as the S-Curve – and the Exceedance DistributionFunction (EDF) are derived from the PDF, and are just twodifferent ways of presenting the same data

List some typical sources for uncertainty ranges

Describe the typical distribution shapes for reservoir uncertainties,and their advantages and disadvantages



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Static and Dynamic Uncertainties in R & R Estimates



Learning Objectives

Describe static uncertainties

Describe the differences between various reserve calculationapproaches, including deterministic, scenario, and probabilistic

Describe the hybrid impacts of obtaining deterministic andscenario approaches incorporating probabilistic analysis, which isthe best approach the industry has found to get the best of bothtypes of approaches



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Learning Objectives

Describe static uncertainties

Describe the differences between various reserve calculationapproaches, including deterministic, scenario, and probabilistic

Describe the hybrid impacts of obtaining deterministic andscenario approaches incorporating probabilistic analysis, which isthe best approach the industry has found to get the best of bothtypes of approaches



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Deterministic Volumetric Model and Use in Flow Models



Learning Objectives

Estimate original oil-in-place and gas-in-place given maps andaverage petrophysical data

Define the basis for net reservoir thickness

Explain grid overlay and contour methods of volumetric estimation

Calculate the mobile hydrocarbon volume in a reservoir

Use a HCPV plot with depth to determine reserves



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Volumetric Calculations

1. Gross bulk volume = A hgross

2. Net bulk volume = A hgross NTG

3. Pore volume = A hgross NTG ϕ4. Hydrocarbon pore volume = A hgross NTG ϕ (1-Sw )

5. Hydrocarbons-in-place = A hgross NTG ϕ (1-Sw )/ Boi

NTG=hnet

hgross

hgross

NTG, ϕ, Sw

Boi

A

Volumetric Hydrocarbon in Place Calculation – Oil Zone at Undisturbed Conditions

Rock with Hydrocarbon

Total of Bulk Volume

Oil

Water

Matrix

HCVOL = A * h * (N/G) * ϕ * (1 - Sw)

Hydrocarbon Volume

Reservoir Volume

Net toGross

Porosity

Water Saturation

Reservoir

A = area

hh * (N/G)Sand

Clay

You may want to PAUSE a moment to review this slide.PAUSE



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Volumetric Estimates of OHIP

Net pay thickness: The portion of the gross thickness thatcontains porous, permeable, and hydrocarbon-bearing rock

Usually estimated using petrophysical cut-offs

• Porosity > 0.10

• Swirr < 0.70

• Permeability > 1.0 md (oil), 0.1 md (gas)

The cut-off values vary depending on lithology, pore sizedistribution, hydrocarbon quality, and other considerations

Cut-offs are usually determined by

• Cased-hole logging

• Core analysis

• Well test results (DST or MDT)

• Production logging (flowmeter)

• Analogue reservoir values

• Hydrocarbon type (oil or gas)

Net Reservoir Thickness

P o r o s i t y L o g

8 0 0 0

8 0 10

8 0 2 0

8 0 3 0

8 0 4 0

8 0 5 0

8 0 6 0

8 0 7 0

8 0 8 0

8 0 9 0

8 10 0

0 0 . 0 5 0 . 1 0 . 15 0 . 2

P or o s it y

Porosity

Dep

th

You may want to PAUSE a moment to review this slide.PAUSE

Pay used in the volumetric formula H NET, after applying

appropriate cut-offs



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Used when reservoir will be (or is) impacted by encroachment of water or gas

Volumetric calculation that describes the hydrocarbon volume distribution vs. depth in the reservoir

Encroachment into the reservoir occurs as a level plane (i.e., contacts move uniformly)

Downdip water injection Gas injection at crest of

structure

Aquifer influx Gas cap expansion

Hydrocarbon Pore Volume vs. Depth Plot

Natural water encroachment Artificial injection

Assumptions

Bulk Volume by Depth Increment

Top of structure map

Bottom of structure map(when not underlain by water)

Contours define enclosed areas,Ai and contour interval, h

Pyramidal Formula for eachdepth increment

ΔVb = (h/3) * [Ai + Ai+1 + (Ai*Ai+1)^0.5]

1 mi [1.6 km]

-5750 ft[-1753 m]



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Appropriate Recovery Factor and Reserves

Reserves

= Ultimate Recovery –Cumulative Production

Recovery factor is most appropriately determined by combining:

Detailed reservoirdescription

Development plans(number of wells, type offacilities, reservoirmechanisms employed)

Economics and marketconstraints

Ultimate Recovery

= Hydrocarbons Initially in Place * Recovery

Factor

Range of Recovery Efficiencies

Oil Reserves % OOIP Key Variables

Undersaturated Expansion 3–5% Rock Compressibility

Solution Gas Drive 10–17%Gas-Oil Relative Permeability

Water Drive 40–60%Aquifer Strength,

Producing Rate

Gas Cap Expansion 40–60%Gas Cap Integrity,

Producing Rate

Gravity Drainage 60+% Formation Dip Permeability

Volatile Oil - Oil

- Gas

17–25%

60–80%Condensate Content of Separator Gas



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Range of Recovery Factors

Gas Reservoirs % OGIP Key Variables

High Permeability, Volumetric

70–90%Abandonment Pressure

Low Permeability, Volumetric

40–60% Well Spacing

Water Drive 50–70%Aquifer StrengthProducing Rate

Gas Condensate Reservoirs

% OGIP % OOIP Key Variables

Pressure Depletion 70–90% 30–70%Condensate Yield

API Gravity

Water Drive 50–70% 40–65%Aquifer StrengthProducing Rate

Going Forward from the Static View

One role of a reservoir engineer is deterministic• Provide a number at year end

• In the previous problem it is:– 93.6 million barrels

– You have produced 70.5% of your EUR that you calculate from the field



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Going Forward from the Static View

1Build static geo-model within the design space

2Identify the unique uncertainty variables and ranges

3Generate 25–50 geo-models, honoring the available data

4 As the field is produced compare these geo-models to the dynamic constraints



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Learning Objectives

Estimate original oil-in-place and gas-in-place given maps andaverage petrophysical data

Define the basis for net reservoir thickness

Explain grid overlay and contour methods of volumetric estimation

Calculate the mobile hydrocarbon volume in a reservoir

Use an HCPV plot with depth to determine reserves



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Volumetric and Flow Models



Learning Objectives

Discuss the need for integration across disciplines, especially thegeology and geophysics (G & G) functions

Recognize when models are used in the life of a field

List the advantages and disadvantages of flow models

Explain the importance of corroborating the static (volumetric) anddynamic (flow) view of the reservoir

Explain the business goals of models for estimating reserves



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Integration with Disciplines

If you bring people from different disciplines together, then you will achieve unexpected insights!

Theoretical Physicist1901 - 1976

Werner Karl Heisenberg

Position Momentum

∆x ∆p ≥ hUncertainty Principle

Reserves Evaluation

Resource Based

Approach

Hydrocarbonvolume in place

Recovery factor

Reserves

Scope for Recovery

Developmentplan

Production forecast

Project Based

Approach

Hydrocarbonvolume in place

Developmentplan

Production forecast

Economic evaluation

Reserves

Scope for Recovery



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Reserves Process Output

Surface Seismic

Well Testing

Wireline

Permanent Monitoring

Geology Petrophysics

PVT Data

Pressures – Fluid Contacts

Sedimentology

StructuralGeology

LWD / MWD

Cores

Reservoir Study

Sand type

Mineralogy

Porosity

Horizontal/vertical permeability

Layering

Shale extension

Fracture

Reservoir geological

modelPressure decline

Well productivity history

Reservoirperformance

Structure

Fault pattern

Aquifer size

Well pattern

Areal sand development

Reservoir geometry

Completion

Impairment

Stimulation

Wellperformance

Oil/gas PVT data

Viscosities

Reservoir fluid

Relative permeabilities

Residual oil/ gas saturations

Fluid flow data

Material Balance / sector modelling / full field simulation

Well patterns (both production and injection)

Production forecasts / Estimation of Ultimate Recovery

Reservoir Studies

Petrophysicalanalysis



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Decline Analysis

Volumetric or Static Method

Analogy

Reservoir Simulation

Material balance calculations for oil and gas reservoirs

Types of Reserve Determination Models

Early Stages of Development

Flow Models



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Oil Material Balance

Upward curve indicates water influx

slope = N

Eo+Ef,w

F

Oil material balance is a simplemass balance between the fluidsproduced from a reservoir and theexpansion of the fluids remainingin the reservoir

Oil material is written in terms ofOOIP, given that you know thefollowing:

• How much oil has been produced• Average reservoir pressure• How fluids expand versus

pressure• Rock compressibility

A special form of the materialbalance from Havlena and Odeh isshown, where the slope is N orOOIP

P = P1 P = P2

Original Fluids = Remaining Fluids + Produced Fluids



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Gas Material Balance

P = P1 P = P2

Original Gas = Remaining Gas + Produced Gas

Cum Gas Prod - BCF

P/Z

Initial Pressure/Z

Original GasIn Place

Gas Recovery@ Paban

Abandonment Pressure/Z

Gas material balance is a massbalance between the gasproduced from a reservoir andthe expansion of the gasremaining in the reservoir

Gas material balance is usuallyused to estimate OGIP andultimate gas recovery given youknow:

• How much gas has beenproduced

• Reservoir pressure

• How gas expands vs. pressure

• Abandonment pressure

• Rock compressibility



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Static vs Dynamic Material Balance

Gas

Water

Ground

Comparing Volumetric to Flow Model

Why compare volumetric to flow models?

What can the impact of water

be on the recovery factor for a gas? Why?

12345678910 12345678910



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Proven Reserve Constraints

Existing wells

Proven undeveloped locations

Can Include:

Immediate offsets toexisting wells

Locations betweenexisting wells withdemonstrated pressurecommunication

Oil shown to exist downto Lowest Known Oil(LKO)

What Are the Implications of These Cases?

Click PAUSE to review these comparative cases.

Case #1

Volumetric Calculation: 184 million barrels of oil

Decline Curve Projection: 125 million barrels of oil

Material Balance: 135 million barrels of oil

Case #2

Analogue: 350 million barrels of oil


Reservoir Simulation (after risking): 330 million barrels of oil

Reservoir Simulation (un-risked): 430 million barrels of oil



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Static Uncertainty of Connectivity

Connectivity is the ability of fluids to move from one portion of thereservoir to another

It is a function of one or more of these reasons:• Geobody configuration

• Barrier locations and seal(faults, shales, pinchouts)

• Horizontal permeability

• Vertical permeability

Case #1

Volumetric Calculation: 184 million barrels of oil


Material Balance: 135 million barrels of oil

Static Uncertainty of Connectivity

Connectivity is the ability of fluids to move from one portion of thereservoir to another

It is a function of one or more of these reasons:• Geobody configuration

• Barrier locations and seal(faults, shales, pinchouts)

• Horizontal permeability

• Vertical permeability

Case #2

Analogue: 350 million barrels of oil


Reservoir Simulation (after risking): 330 million barrels of oil

Reservoir Simulation (un-risked): 430 million barrels of oil



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Learning Objectives

Discuss the need for integration across disciplines, especially thegeology and geophysics (G & G) functions

Recognize when models are used in the life of a field

List the advantages and disadvantages of flow models

Explain the importance of corroborating the static (volumetric) anddynamic (flow) view of the reservoir

Explain the business goals of models for estimating reserves



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Linking Physical Models to Probabilistic Results


Learning Objectives

Describe the differences, advantages, and disadvantagesbetween the following:A. Deterministic approaches

B. Scenario approach and the use of the decision tree in reservesevaluation

C. Probabilistic analysis

Derive deterministic and scenario approaches from theprobabilistic approach

Use design of experiments as an add-on to probabilistic analysisto improve selecting the right models



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Why Do We Need Real Physical Cases?

Since assets are complex reservoir systems that are difficult toanalyze using conventional reservoir engineering methods

Reserve Management requires a thorough understanding of:

Original Hydrocarbon-in-Place and it’s distribution inthe reservoir

Well deliverability under changing reservoir conditions

Recovery estimates to support business planning,investments, and reserves bookings

Field development planning requires tools that can evaluatealternatives that have dimensional aspects (e.g., welllocations, completion intervals, operating conditions andlimitations)

Deterministic is a Real Physical Case

Directly linked tophysical models (maps,development plans, etc.)

Does not capture orquantify full range ofuncertainty

Required for bookingreserves

Deterministic

Cannot be uniquelylinked to a singlephysical reservoir model

Captures the full rangeof uncertainties

Cannot be used forreserves directly

Probabilistic



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Integrating Real Physical Cases and Probabilistic Models

Both real physical cases and probabilistic models are needed for acomprehensive subsurface assessment

One hybrid method integrates deterministic cases and probabilisticmethods and gets the required real physical cases

Use real physical cases tocalculate an outcome for eachset of uncertainty values

Capture the full range ofuncertainty

A series of deterministiccases can create aprobabilistic assessment

Logically link physical modelsinto Low-BTE-High scenariosthat are technicallydefendable

Use these models to testalternative development plansand upside/downsidescenarios



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Scenario Outputs of Original Oil in Place

R &

R

Case

Low

Medium

High

CDF: P10 – P20EDF: P80 – P90


R &

R

Case

Low

Medium

High

CDF: P40 – P60EDF: P40 – P60BTE: P50



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R &

R

Case

Low

Medium

High

CDF: P80 – P95EDF: P20 – P5

Decision Trees



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Learning Objectives

Describe the differences, advantages, and disadvantagesbetween the following:A. Deterministic approaches

B. Scenario approach and the use of the decision tree in reservesevaluation

C. Probabilistic analysis

Derive deterministic and scenario approaches from theprobabilistic approach

Use design of experiments as an add-on to probabilistic analysisto improve selecting the right models



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Tracking of Reserves and Transfers within Legal and Professional Framework


Learning Objectives

Describe how to track reserves with time and information gainedthrough the industry standard

Recognize how the law in the United States defines reserves

Describe the risk, uncertainty, and commerciality definitions thatdrive the standardized process between reserve estimates

Discuss how resources become reserves

Define 1P, 2P, and 3P reserves



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Security Exchange Commission Reg 210.4-10

“Proved oil and gas reserves are the estimated quantities of crude oil, natural gas and natural gas liquids which geologic and engineering data demonstrates with reasonable certainty to be recoverable on future years from known reservoirs under existing economic and operating conditions.”

Reserves

1 2

3 4

Future quantities of petroleum expected to be recovered from naturally occurring underground accumulations

Working separately, the Society of Petroleum Engineers (SPE) and the World Petroleum Congresses (WPC) produced definitions for known accumulations (adopted in 1996)

Preferred standards across the industry

Attempts to standardize reservesterminology began in the mid 1930s



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Estimation of Reserves

Methods of Estimation are Called:

Deterministic if a single best estimate of reservesis made based on known geological, engineering,and economic data

• There is a high degree of confidence that the quantitieswill be recovered

Probabilistic when the known geological,engineering, and economic data are used togenerate a range of estimates and their associatedprobabilities

• There is at least 90% probability that the quantitiesactually recovered will equal or exceed the estimate



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Resource Classification System

TotalHydrocarbons

Initially In Place

DiscoveredHydrocarbons

InitiallyIn Place

Sub-Commercial Low

EstimateBest

EstimateHigh

Estimate

Unrecoverable

LowEstimate

BestEstimate

HighEstimate

Unrecoverable

Commercial

PRODUCTION

Proven Proven plus

Probable

Proven plus

Probable plus

Possible

RESERVES

CONTINGENT RESOURCES

PROSPECTIVE RESOURCES

Undiscovered Hydrocarbons Initially

In Place

Industry Resources Classification System

Reserves

Discovered, recoverable, commercial, remaining

Proven, probably, possible

1P, 2P, 3P (now formalized)

Contingent Resources

Discovered, potentially recoverable, not yet commercial, remaining

1C, 2C, 3C (new terms)• Equivalent to low, best,

and high estimates

Prospective Resources

Undiscovered, potentially recoverable, potentially commercial, remaining

Low, best, and high estimates

Unrecoverable

Discovered or undiscovered, not recoverable

Tota

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um In

itial

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(P

IIP)

Dis

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PII

P

Com

mer

cial

Sub

-Com

mer

cial

Und

isco

vere

d P

IIP

Production

Unrecoverable

Unrecoverable

RESERVES



Proven Probable Possible

1P 2P 3P

1C 2C 3C

Low Estimate

Best Estimate

High Estimate

Range of Uncertainty

Incre

asin

g Ch

an

ce o

f Co

mm

erciality

Classificatio

n

Categorization



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Project Maturity – Prospective Resources

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IIP)

Dis

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PII

P

Com

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Sub

-Com

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cial

Und

isco

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d P

IIP

Production

Unrecoverable

Unrecoverable

RESERVES




Incre

asin

g Ch

an

ce o

f Co

mm

erciality

On Production

Approved for Development

Justified for Development

Development Pending

Development Unclarified or On Hold

Development Unclarified or On Hold

Prospect

Lead

Play

Project Maturity Sub-Classes

Prospect

A project associated with a potential accumulation that is sufficiently well defined to represent a viable drilling target

Lead

A project associated with a potential accumulation that is currently poorly defined and requires more data acquisition and/or evaluation in order to be classified as a prospect

Play

A project associated with a prospective trend of potential prospects, but which requires more data acquisition and/or evaluation in order to define specific leads or prospects

Classificatio

n

Categorization

12345678910



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Learning Objectives

Describe how to track reserves with time and information gainedthrough the industry standard

Recognize how the law in the United States defines reserves

Describe the risk, uncertainty, and commerciality definitions thatdrive the standardized process between reserve estimates

Discuss how resources become reserves

Define 1P, 2P, and 3P reserves



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Following R & R after Production Starts



Learning Objectives

Describe transfer mechanism from probable and possible toproven, once an asset is undergoing development

Use a diagram of secondary and primary exploration objectives todescribe how resources become reserves

Recognize some limitations of the law on transfers to avoidoverbooking



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Non-Commercial Reserves Prior to Discovery

What do you need to do to move this

project along?

Non-Commercial Reserves Prior to Discovery

• Terms of agreement?• Do you have a reservoir?• Offshore or onshore?• Producing characteristics?• Analog fields?• Where are potential sources?• How much source is available?• Migration path?• Where’s the “kitchen”?• Is it oil or gas?• Do you have a trap?• Does trap contain hydrocarbons?• Potential development costs?• Will it be commercial?• Can terms be renegotiated?• Should you partner with someone?• What did you miss?



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um In

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(P

IIP)

Dis

cove

red

PII

P

Com

mer

cial

Sub

-Com

mer

cial

Und

isco

vere

d P

IIP

Production

Unrecoverable

Unrecoverable

RESERVES




1P 2P 3P

1C 2C 3C

Low Estimate

Best Estimate

High Estimate


Incre

asin

g Ch

an

ce o

f Co

mm

erciality

Classificatio

n

Categorization

You Have a Contract to Drill, Prior to Discovery



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Whoops, No Discovery, But You Have a Reservoir

Whoops, No Discovery, But You Have a Reservoir

Tota

l Pet

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um In

itial

ly-I

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lace

(P

IIP)

Dis

cove

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PII

P

Com

mer

cial

Sub

-Com

mer

cial

Und

isco

vere

d P

IIP

Production

Unrecoverable

Unrecoverable

RESERVES




1P 2P 3P

1C 2C 3C

Low Estimate

Best Estimate

High Estimate


Incre

asin

g Ch

an

ce o

f Co

mm

erciality

Classificatio

n

Categorization



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Now You Have a Discovery

Here, you have a hydrocarbon-filled reservoir tested at commercial rates.

Now You Have a Discovery

Here, you have a hydrocarbon-filled reservoir tested at commercial rates.

Tota

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ly-I

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(P

IIP)

Dis

cove

red

PII

P

Com

mer

cial

Sub

-Com

mer

cial

Und

isco

vere

d P

IIP

Production

Unrecoverable

Unrecoverable

RESERVES




1P 2P 3P

1C 2C 3C

Low Estimate

Best Estimate

High Estimate


Incre

asin

g Ch

an

ce o

f Co

mm

erciality

Classificatio

n

Categorization



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Tota

l Pet

role

um In

itial

ly-I

n-P

lace

(P

IIP)

Dis

cove

red

PII

P

Com

mer

cial

Sub

-Com

mer

cial

Und

isco

vere

d P

IIP

Production

Unrecoverable

Unrecoverable

RESERVES




1P 2P 3P

1C 2C 3C

Low Estimate

Best Estimate

High Estimate


Incre

asin

g Ch

an

ce o

f Co

mm

erciality

Classificatio

n

Categorization

Proven Producing Reserves



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Another Idea and the Process Continues

Tota

l Pe

tro

leu

m In

itia

lly-I

n-P

lace

(P

IIP)

Dis

cove

red

PII

P

Co

mm

erc

ial

Su

b-C

om

mer

cia

l

Un

dis

cove

red

PII

P

Production

Unrecoverable

Unrecoverable

RESERVES




1P 2P 3P

1C 2C 3C

Low Estimate

Best Estimate

High Estimate


Increasing Chance of C

omm

erciality

Reserve Classification

High Degree of Certainty of Estimate Low

Proven Probable Possible(50%)*

Proven Undeveloped (PUD) (50%)*

(0%)*

Proven Developed (PD) Producing

(100%)*

Proven Developed Behind Pipe (75%)*

Proven Developed Non-Producing

(90%)*



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Learning Objectives

Describe transfer mechanism from probable and possible toproven, once an asset is undergoing development

Use a diagram of secondary and primary exploration objectives todescribe how resources become reserves

Recognize some limitations of the law on transfers to avoidoverbooking

PetroAcademyTM Applied Reservoir Engineering Skill Modules

This is Reservoir Engineering Core

Reservoir Rock Properties Core

Reservoir Rock Properties Fundamentals

Reservoir Fluid Core

Reservoir Fluid Fundamentals

Reservoir Flow Properties Core

Reservoir Flow Properties Fundamentals

Reservoir Fluid Displacement Core

Reservoir Fluid Displacement Fundamentals

Properties Analysis Management

Reservoir Material Balance Core

Reservoir Material Balance Fundamentals

Decline Curve Analysis and Empirical Approaches Core

Decline Curve Analysis and Empirical Approaches Fundamentals

Pressure Transient Analysis Core

Rate Transient Analysis Core

Enhanced Oil Recovery Core

Enhanced Oil Recovery Fundamentals

Reservoir Simulation Core


Reservoir Surveillance Core

Reservoir Surveillance Fundamentals

Reservoir Management Core

Reservoir Management Fundamentals




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