COPYRIGHT. Why Take This Module? Why Take This Module? Headlights function with two purposes: The R & R process also has two fundamental purposes:

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1 Why Take This Module? Headlights function with two purposes: A. Give guidance to the driver on a dark road B. Help other drivers be aware of its presence The R & R process also has two fundamental purposes: A. Show the path forward to adding reserves, indicating opportunities and investment reserves B. Give stake-holders an indication of what the asset is capable of in future Why Take This Module? Imagine an incorrect choice of geologic model and accompanying depositional environment Using volumetric methods, applied to the incorrect geologic model, we obtain 10 billion cubic meters (353 billion cubic feet) of original gas in place However, the reservoir engineer using the two asset wells material balance (P/Z) after five years of production, can only account for 0.5 billion cubic meters (18 billion cubic feet) of original gas in place What went wrong? 1

2 Why Take This Module? After five years of production the material balance is only accounting for the parts of the fluvial reservoir connected to the two wells Part of the error is in the incorrect geologic model, since that will probably pull down the volumetric method estimate However, there is a large error in the lack of appreciation of the disconnected nature of fluvial reservoirs Why Take This Module? 2

3 Learning Objectives Reserves and Resources Core The Underlying Business of Reserves Management This section will cover the following learning objectives: Explain importance of integration with other disciplines Recognize calculations using the volumetric formulas for gas and oil Explain the importance of dividing into flow units for dynamic reserves in reservoir simulation Describe what reserves management is and how to do it 3

4 Reserves Management Reserves management is a key aspect for the reservoir engineer throughout field life Basin Discovery Play Exploration Delineation Prospect Definitions Abandonment Disposal Tertiary RESERVES MANAGEMENT Secondary PRODUCTION Primary Both R & R estimates are uncertain Development Maximum resource size Minimum resource size Scope for future recovery (SFR) Maximum reserves Minimum reserves Reserve classification recognizes uncertainty 4

5 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 5

6 Resources versus Reserves Recovery Factor (RF) Reserves Resource (Recoverable hydrocarbons) (Hydrocarbons-in-place) Technical Factors Reservoir Quality Reservoir Drive Method of Operation Influenced by: Economic Factors Oil and Gas Prices Concession Terms/Taxes Costs Market Contract 6

7 Not All Hydrocarbons are Reserves Not yet discovered No market Not economic with current technology Cannot be produced within license term 7

8 Common International Hydrocarbon Contracts 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 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 Open the supplied spreadsheet: NRI-PS-NPI_Contract_Comparison_of_Reserves.xlsx Gas versus Oil Field Impacts of Contracts Gas Field Reserves 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 8

9 Gas Well Performance Pressure psig [MPa] Gas Rate MMSCF/D [MMSCM/D] 6000 [41] 4000 [28] 2000 [18] [3.4] 100 [2.8] 80 [2.3] 60 [1.7] 40 [1.1] 20 [0.6] 0 Surface Pressure Potential Rate Actual Rate No Compression (60% Recovery) Gas Contract Terminology ACQ Reservoir Pressure 1600 psig [11 MPa] Time Annual Contract Quantity Stage I (30% Recovery) Compression 800 psig [6 MPa] Stage II (10% Recovery) Total volume of gas to be sold during the year 500 psig [3 MPa] Tail Gas DCQ SDC MDR Daily Contract Quantity ACQ/365: average daily gas sales rate during a year Seller s Delivery Capacity or Max Daily Rate The maximum daily rate that the seller must be able to produce (generally specified monthly) LF Load Factor = DCQ/SDC Low load factor means a lot of facility/production capacity sits idle for much of the time 9

10 Typical Gas Contract Requirements [MMscm/d] MMscf/d 180 [5.1] 160 [4.5] 140 [4.0] 120 [3.4] 100 [2.8] 80 [2.3] 60 [1.7] 40 [1.1] 20 [0.6] 0 Jan Feb Mar Apr May Jun SDC Take DCQ Jul Aug Sep Oct Nov Dec 10

11 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. Area Thickness Porosity Element Net to gross HC saturation Recoverability (reserves) Reserve Determination Value Mapping Derivation Well logs, seismic; models Well logs, cores Well logs, seismic; models Petrophysics Interpretation Flow models Reserve Determination Volumetric model comparison and corroboration Integrating both: Disciplines most importantly geology and geophysical Production System Subsurface and surface functions 11

12 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 Study Methods Range of estimates Production profile Cumulative Volumetric Performance Actual Recovery Rate High Relative Risk Time Low Field phase Appraisal Devel. Field review/special projects 12

13 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 13

14 Human Factors Affecting Estimates 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 Estimated ultimate recovery Initial Reserve Growth Initial Recovery Estimate Time 14

15 Learning Objectives This section has covered the following learning objectives: Explain importance of integration with other disciplines Recognize calculations using the volumetric formulas for gas and oil Explain the importance of dividing into flow units for dynamic reserves in reservoir simulation Describe what reserves management is and how to do it 15

16 Learning Objectives Reserves and Resources Core Analysis Principles of Uncertainty in Reservoirs This section will cover the following learning objectives: Describe the importance of reservoir uncertainty List some typical reservoir uncertainties and their categories Understand the relationship of statistical parameters to probabilistic 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 Distribution Function (EDF) are derived from the PDF and are just two different 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 16

17 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 Quantify range/distribution of HIIP, reserves development plans and production profiles Identify key variables which contribute significantly to uncertainty, appraisal targets Identify interventions to mitigate against potential downsides or to exploit potential upsides 17

18 Does a Structured Approach Help? Bias can lead to an unrepresentative view of a project s uncertainty Properly assessing uncertainty ranges and their physical meaning is the foundation to making good reserve estimates You can use a structured approach to estimate uncertainties and incorporate them into a realistic range of forecasts 18

19 Ranges and Distributions Probability Density Function (PDF) Cumulative Distribution Function (CDF) Probability of Occurrence (%) Value Probability of Value or less (%) Value Communicating Uncertainties in an Assessment Management requires ranges of STOOIP, rates, EUR and the linkage to value These forecasts should incorporate the full range of uncertainty A project is characterized by the entire S-curve, not just the Expected Value When you have translated this to this S-curve, you have communicated all of the risks and uncertainties into it Projects will have different risk profiles based on the shape of their S-curves Probability of Value or less (%) Value The S-curve 19

20 Ranges and Distributions Uncertainty is generally treated as a continuous probability model where the result can vary anywhere between a selected minimum and maximum Probability Ranges and Distributions Triangular I know the min, most likely, and the max Discrete Uniform I know the max and min, but otherwise I have no idea as to what lies between Range of Values Normal I know an approximate range, and I know it is symmetrical about the most likely Smaller range of likely outcomes, smaller uncertainty Larger range of likely outcomes, larger uncertainty Log-Normal Histogram I know the approximate range, but the probability is asymmetric, positively skewed toward higher values Double Triangular I know an approximate range, and I know it is symmetrical about the most likely 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 I known it is log normal, but I only know the limits, so I ll use a simpler function, until I know better 20

21 Ranges and Distributions Probability Density Function (PDF) Forecast: Zone 1 Recoverable Oil (mmbo) 5,000 Trials Frequency Chart 108 Outliers Cumulative Distribution Function (CDF) Forecast: Zone 1 Recoverable Oil (mmbo) 5,000 Trials Cumulative Chart 108 Outliers Mean = Certainty is 97.84% from 4.8 to 33.8 Mode: Most likely For this example Mean = Median: Equal probability of being bigger or smaller than (P50) Certainty is 97.84% from 4.8 to 33.8 Mean: 16.6 Median: 15.4 Mode: 12.9 Exceedance: Another Way to Look at Distributions and their Combination PDFs HISTOGRAM Mean: Expected value or average EDFs (derived from PDFs)

22 Exceedance: Another Way to Look at Distributions and their Combination PDFs EDFs (derived from PDFs) HISTOGRAM P(x) x Exceedance: Another Way to Look at Distributions and their Combination PDFs HISTOGRAM EDFs (derived from PDFs) P(x) x

23 Statistical Parameters from the CDF Forecast: One hundred 10,000 Trials Cumulative Chart 9,912 Displayed Mean = P90 or is it P10? P10 or is it P90? P50 Recognizing Uncertainty in the CDF Cumulative probability reserves greater than this volume 90% 50% Proven Probable expectation 10% Possible Reserve Volume 23

24 Exceedance versus Cumulative E Ʃ P(x) x Cumulative at this P(x) to value of x Exceedance at this P(x) to value of x 24

25 Combination of Parameters Methods 1 Generate curves for individual parameters 2 Combining parameters to obtain an estimate for a reservoir Monte Carlo method Latin hypercube Parametric method Combining Distributions Combining (e.g., adding or multiplying) two or more probability distributions is not always a simple task You can combine mean values to get resultant mean values But you generally cannot simply combine other points on the distribution P10 a + P10 b + P10 c P10 total in most cases There are mathematic formulas that can combine some distributions, or that provide close approximations with others. A practical way to combine distributions is by Monte Carlo simulation Randomly samples each input distribution Combines (e.g., adds or multiplies) Sorts the resultant distribution. Accuracy generally increases with the number of iterations 25

26 Learning Objectives This section has covered the following learning objectives: Describe the importance of reservoir uncertainty List some typical reservoir uncertainties and their categories Understand the relationship of statistical parameters to probabilistic 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 Distribution Function (EDF) are derived from the PDF, and are just two different 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 26

27 Learning Objectives Reserves and Resources Core Static and Dynamic Uncertainties in R & R Estimates This section will cover the following learning objectives: Describe static uncertainties Describe the differences between various reserve calculation approaches, including deterministic, scenario, and probabilistic Describe the hybrid impacts of obtaining deterministic and scenario approaches incorporating probabilistic analysis, which is the best approach the industry has found to get the best of both types of approaches 27

28 Learning Objectives This section has covered the following learning objectives: Describe static uncertainties Describe the differences between various reserve calculation approaches, including deterministic, scenario, and probabilistic Describe the hybrid impacts of obtaining deterministic and scenario approaches incorporating probabilistic analysis, which is the best approach the industry has found to get the best of both types of approaches 28

29 Learning Objectives Reserves and Resources Core Deterministic Volumetric Model and Use in Flow Models This section will cover the following learning objectives: Estimate original oil-in-place and gas-in-place given maps and average 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 29

30 Volumetric Calculations NTG, ϕ, Sw A NTG= hnet hgross hgross Boi 1. Gross bulk volume = A h gross 2. Net bulk volume = A h gross NTG 3. Pore volume = A h gross NTG ϕ 4. Hydrocarbon pore volume = A h gross NTG ϕ (1-S w ) 5. Hydrocarbons-in-place = A h gross NTG ϕ (1-S w )/ B oi Volumetric Hydrocarbon in Place Calculation Oil Zone at Undisturbed Conditions Hydrocarbon Volume HCVOL = A * h * (N/G) * ϕ * (1 - S w ) Reservoir Volume Net to Gross Water Saturation Porosity Rock with Hydrocarbon Total of Bulk Volume Oil Water h Reservoir A = area Clay Sand h * (N/G) Matrix You may want to PAUSE a moment to review this slide. 30

31 P or osity Log Porosity Reserves and Resources Core Volumetric Estimates of OHIP Net Reservoir Thickness Net pay thickness: The portion of the gross thickness that contains porous, permeable, and hydrocarbon-bearing rock Usually estimated using petrophysical cut-offs Porosity > 0.10 Sw irr < 0.70 Permeability > 1.0 md (oil), 0.1 md (gas) The cut-off values vary depending on lithology, pore size distribution, 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) Pay used in the volumetric formula H NET, after applying appropriate cut-offs You may want to PAUSE a moment to review this slide. Depth Porosity 31

32 Hydrocarbon Pore Volume vs. Depth Plot Volumetric calculation that describes the hydrocarbon volume distribution vs. depth in the reservoir Used when reservoir will be (or is) impacted by encroachment of water or gas Natural water encroachment Aquifer influx Gas cap expansion Assumptions Artificial injection Downdip water injection Gas injection at crest of structure Encroachment into the reservoir occurs as a level plane (i.e., contacts move uniformly) Bulk Volume by Depth Increment Δ V b = (h/3) * [A i + A i+1 + (A i *A )^0.5 i+1 ] 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 each depth increment ft [-1753 m] 1 mi [1.6 km] 32

33 Appropriate Recovery Factor and Reserves Recovery factor is most appropriately determined by combining: Detailed reservoir description Development plans (number of wells, type of facilities, reservoir mechanisms employed) Economics and market constraints Range of Recovery Efficiencies Ultimate Recovery = Hydrocarbons Initially in Place * Recovery Factor Reserves = Ultimate Recovery Cumulative Production 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 33

34 Range of Recovery Factors Gas Reservoirs % OGIP Key Variables High Permeability, Volumetric Low Permeability, Volumetric 70 90% Water Drive 50 70% Gas Condensate Reservoirs Abandonment Pressure 40 60% Well Spacing Aquifer Strength Producing Rate % OGIP % OOIP Key Variables Pressure Depletion 70 90% 30 70% Water Drive 50 70% 40 65% 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: Condensate Yield API Gravity Aquifer Strength Producing Rate 93.6 million barrels You have produced 70.5% of your EUR that you calculate from the field 34

35 Going Forward from the Static View 1 Build static geo-model within the design space 2 Identify the unique uncertainty variables and ranges 3 Generate geo-models, honoring the available data 4 As the field is produced compare these geomodels to the dynamic constraints 35

36 Learning Objectives This section has covered the following learning objectives: Estimate original oil-in-place and gas-in-place given maps and average 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 36

37 Learning Objectives Reserves and Resources Core Volumetric and Flow Models This section will cover the following learning objectives: Discuss the need for integration across disciplines, especially the geology 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) and dynamic (flow) view of the reservoir Explain the business goals of models for estimating reserves 37

38 Integration with Disciplines Werner Karl Heisenberg Theoretical Physicist Reserves Evaluation Resource Based Approach Hydrocarbon volume in place Recovery factor If you bring people from different disciplines together, then you will achieve unexpected insights! Position Reserves Scope for Recovery Momentum x p h Uncertainty Principle Development plan Production forecast Project Based Approach Hydrocarbon volume in place Development plan Production forecast Economic evaluation Reserves Scope for Recovery 38

39 Reserves Process Output Geology Petrophysics Cores Wireline Surface Seismic Structural Geology Sedimentology Reservoir Study Reservoir geometry Structure Fault pattern Aquifer size Well pattern Areal sand development Permanent Monitoring Reservoir geological model Sand type Mineralogy Porosity Horizontal/ vertical permeability Layering Shale extension Fracture Well Testing Reservoir performance Pressure decline Well productivity history Fluid flow data Relative permeabilities Residual oil/ gas saturations LWD / MWD Pressures Fluid Contacts PVT Data Well performance Completion Impairment Stimulation Petrophysical analysis Reservoir fluid Oil/gas PVT data Viscosities Reservoir Studies Material Balance / sector modelling / full field simulation Well patterns (both production and injection) Production forecasts / Estimation of Ultimate Recovery 39

40 Types of Reserve Determination Models Analogy Early Stages of Development Volumetric or Static Method Decline Analysis Material balance calculations for oil and gas reservoirs Flow Models Reservoir Simulation 40

41 Oil Material Balance Oil material balance is a simple mass balance between the fluids produced from a reservoir and the expansion of the fluids remaining in the reservoir Oil material is written in terms of OOIP, given that you know the following: How much oil has been produced Average reservoir pressure How fluids expand versus pressure Rock compressibility A special form of the material balance from Havlena and Odeh is shown, where the slope is N or OOIP F P = P1 P = P2 Original Fluids = Remaining Fluids + Produced Fluids Upward curve indicates water influx E o +E f,w slope = N 41

42 Gas Material Balance Gas material balance is a mass balance between the gas produced from a reservoir and the expansion of the gas remaining in the reservoir Gas material balance is usually used to estimate OGIP and ultimate gas recovery given you know: How much gas has been produced Reservoir pressure How gas expands vs. pressure Abandonment pressure Rock compressibility P/Z P = P1 Original Gas = Initial Pressure/Z Abandonment Pressure/Z Cum Gas Prod - BCF P = P2 Remaining Gas + Produced Gas Original Gas In Place Gas Paban 42

43 Static vs Dynamic Material Balance Ground Gas Water 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?

44 Proven Reserve Constraints Can Include: Immediate offsets to existing wells Locations between existing wells with demonstrated pressure communication Oil shown to exist down to Lowest Known Oil (LKO) What Are the Implications of These Cases? Existing wells Proven undeveloped locations 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 Decline Curve Projection: 320 million barrels of oil Reservoir Simulation (after risking): 330 million barrels of oil Reservoir Simulation (un-risked): 430 million barrels of oil Click PAUSE to review these comparative cases. 44

45 Static Uncertainty of Connectivity Connectivity is the ability of fluids to move from one portion of the reservoir 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 Decline Curve Projection: 125 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 the reservoir 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 Decline Curve Projection: 320 million barrels of oil Reservoir Simulation (after risking): 330 million barrels of oil Reservoir Simulation (un-risked): 430 million barrels of oil 45

46 Learning Objectives This section has covered the following learning objectives: Discuss the need for integration across disciplines, especially the geology 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) and dynamic (flow) view of the reservoir Explain the business goals of models for estimating reserves 46

47 Learning Objectives Reserves and Resources Core Linking Physical Models to Probabilistic Results This section will cover the following learning objectives: Describe the differences, advantages, and disadvantages between the following: A. Deterministic approaches B. Scenario approach and the use of the decision tree in reserves evaluation C. Probabilistic analysis Derive deterministic and scenario approaches from the probabilistic approach Use design of experiments as an add-on to probabilistic analysis to improve selecting the right models 47

48 Why Do We Need Real Physical Cases? Since assets are complex reservoir systems that are difficult to analyze using conventional reservoir engineering methods Reserve Management requires a thorough understanding of: Original Hydrocarbon-in-Place and it s distribution in the reservoir Well deliverability under changing reservoir conditions Recovery estimates to support business planning, investments, and reserves bookings Field development planning requires tools that can evaluate alternatives that have dimensional aspects (e.g., well locations, completion intervals, operating conditions and limitations) Deterministic is a Real Physical Case Deterministic Directly linked to physical models (maps, development plans, etc.) Does not capture or quantify full range of uncertainty Required for booking reserves Probabilistic Cannot be uniquely linked to a single physical reservoir model Captures the full range of uncertainties Cannot be used for reserves directly 48

49 Integrating Real Physical Cases and Probabilistic Models Both real physical cases and probabilistic models are needed for a comprehensive subsurface assessment One hybrid method integrates deterministic cases and probabilistic methods and gets the required real physical cases Use real physical cases to calculate an outcome for each set of uncertainty values Capture the full range of uncertainty A series of deterministic cases can create a probabilistic assessment Logically link physical models into Low-BTE-High scenarios that are technically defendable Use these models to test alternative development plans and upside/downside scenarios 49

50 Scenario Outputs of Original Oil in Place High Medium R & R Low Case CDF: P10 P20 EDF: P80 P90 Scenario Outputs of Original Oil in Place R & R Low Medium High Case CDF: P40 P60 EDF: P40 P60 BTE: P50 50

51 Scenario Outputs of Original Oil in Place High Medium R & R Decision Trees Low Case CDF: P80 P95 EDF: P20 P5 51

52 Learning Objectives This section has covered the following learning objectives: Describe the differences, advantages, and disadvantages between the following: A. Deterministic approaches B. Scenario approach and the use of the decision tree in reserves evaluation C. Probabilistic analysis Derive deterministic and scenario approaches from the probabilistic approach Use design of experiments as an add-on to probabilistic analysis to improve selecting the right models 52

53 Reserves and Resources Core Tracking of Reserves and Transfers within Legal and Professional Framework Learning Objectives This section will cover the following learning objectives: Describe how to track reserves with time and information gained through the industry standard Recognize how the law in the United States defines reserves Describe the risk, uncertainty, and commerciality definitions that drive the standardized process between reserve estimates Discuss how resources become reserves Define 1P, 2P, and 3P reserves 53

54 Security Exchange Commission Reg Reserves 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. 1 2 Future quantities of petroleum expected to be recovered from naturally occurring underground accumulations 3 Working separately, the 4 Society of Petroleum Engineers (SPE) and the World Petroleum Congresses (WPC) produced definitions for known accumulations (adopted in 1996) Attempts to standardize reserves terminology began in the mid 1930s Preferred standards across the industry 54

55 Estimation of Reserves Methods of Estimation are Called: Deterministic if a single best estimate of reserves is made based on known geological, engineering, and economic data There is a high degree of confidence that the quantities will be recovered Probabilistic when the known geological, engineering, and economic data are used to generate a range of estimates and their associated probabilities There is at least 90% probability that the quantities actually recovered will equal or exceed the estimate 55

56 Resource Classification System PRODUCTION Total Hydrocarbons Initially In Place Discovered Hydrocarbons Initially In Place Commercial Sub- Commercial Undiscovered Hydrocarbons Initially In Place Proven Low Estimate Low Estimate 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 Total Petroleum Initially-In-Place (PIIP) Discovered PIIP Commercial Sub-Commercial Undiscovered PIIP RESERVES Proven plus Probable Best Estimate Unrecoverable Best Estimate Unrecoverable Proven plus Probable plus Possible CONTINGENT RESOURCES High Estimate PROSPECTIVE RESOURCES Production RESERVES 1P 2P 3P Proven Probable Possible CONTINGENT RESOURCES 1C 2C 3C Low Estimate Unrecoverable PROSPECTIVE RESOURCES Best Estimate Unrecoverable Range of Uncertainty High Estimate Categorization High Estimate Increasing Chance of Commerciality Classification 56

57 Project Maturity Prospective Resources 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 Total Petroleum Initially-In-Place (PIIP) Discovered PIIP Commercial Sub-Commercial Undiscovered PIIP Production RESERVES CONTINGENT RESOURCES Unrecoverable PROSPECTIVE RESOURCES Unrecoverable Range of Uncertainty Categorization Project Maturity Sub- Classes On Production Approved for Development Justified for Development Development Pending Development Unclarified or On Hold Development Unclarified or On Hold Prospect Lead Play Increasing Chance of Commerciality Classification 57

58 Learning Objectives This section has covered the following learning objectives: Describe how to track reserves with time and information gained through the industry standard Recognize how the law in the United States defines reserves Describe the risk, uncertainty, and commerciality definitions that drive the standardized process between reserve estimates Discuss how resources become reserves Define 1P, 2P, and 3P reserves 58

59 Learning Objectives Reserves and Resources Core Following R & R after Production Starts This section will cover the following learning objectives: Describe transfer mechanism from probable and possible to proven, once an asset is undergoing development Use a diagram of secondary and primary exploration objectives to describe how resources become reserves Recognize some limitations of the law on transfers to avoid overbooking 59

60 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? 60

61 Industry Resources Classification System Total Petroleum Initially-In-Place (PIIP) Discovered PIIP Commercial Sub-Commercial Undiscovered PIIP Production RESERVES 1P 2P 3P Proven Probable Possible CONTINGENT RESOURCES 1C 2C 3C Low Estimate Unrecoverable PROSPECTIVE RESOURCES Best Estimate Unrecoverable Range of Uncertainty Categorization High Estimate You Have a Contract to Drill, Prior to Discovery Classification Increasing Chance of Commerciality 61

62 Whoops, No Discovery, But You Have a Reservoir Whoops, No Discovery, But You Have a Reservoir Total Petroleum Initially-In-Place (PIIP) Discovered PIIP Commercial Sub-Commercial Undiscovered PIIP Production RESERVES 1P 2P 3P Proven Probable Possible CONTINGENT RESOURCES 1C 2C 3C Low Estimate Unrecoverable PROSPECTIVE RESOURCES Best Estimate Unrecoverable High Estimate Classification Increasing Chance of Commerciality Range of Uncertainty Categorization 62

63 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. Total Petroleum Initially-In-Place (PIIP) Discovered PIIP Commercial Sub-Commercial Undiscovered PIIP Production RESERVES 1P 2P 3P Proven Probable Possible CONTINGENT RESOURCES 1C 2C 3C Low Estimate Unrecoverable PROSPECTIVE RESOURCES Best Estimate Unrecoverable High Estimate Classification Increasing Chance of Commerciality Range of Uncertainty Categorization 63

64 Industry Resources Classification System Total Petroleum Initially-In-Place (PIIP) Discovered PIIP Commercial Sub-Commercial Undiscovered PIIP Proven Producing Reserves Production RESERVES 1P 2P 3P Proven Probable Possible CONTINGENT RESOURCES 1C 2C 3C Low Estimate Unrecoverable PROSPECTIVE RESOURCES Best Estimate Unrecoverable Range of Uncertainty Categorization High Estimate Classification Increasing Chance of Commerciality 64

65 Another Idea and the Process Continues Reserve Classification Total Petroleum Initially-In-Place (PIIP) Discovered PIIP Undiscovered PIIP Sub-Commercial Commercial Production RESERVES 1P 2P 3P Proven Probable Possible CONTINGENT RESOURCES 1C 2C 3C Low Estimate Unrecoverable PROSPECTIVE RESOURCES Best Estimate Unrecoverable Range of Uncertainty High Degree of Certainty of Estimate Low Proven Probable Possible (50%)* Proven Developed (PD) Producing (0%)* High Estimate (100%)* Increasing Chance of Commerciality Proven Developed Behind Pipe (75%)* Proven Developed Non- Producing (90%)* Proven Undeveloped (PUD) (50%)* 65

66 Learning Objectives This section has covered the following learning objectives: Describe transfer mechanism from probable and possible to proven, once an asset is undergoing development Use a diagram of secondary and primary exploration objectives to describe how resources become reserves Recognize some limitations of the law on transfers to avoid overbooking 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 PetroAcademy TM Applied Reservoir Engineering Skill Modules 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 Reserves and Resources Core Reservoir Surveillance Core Reservoir Surveillance Fundamentals Reservoir Management Core Reservoir Management Fundamentals 66