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Uncertainty Who Cares

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Petrophysical Uncertainty

Petrophysical Uncertainty

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• 1. Petrophysical Uncertainty Who Cares?
• 2.
• Uncertainty – An Example
• Different Views on Uncertainty
• Uncertainty and Risk
• Probability Distributions – What do they mean.
• Monte-Carlo
• Model Based Workflow
• Uncertainty and Risk Registers
• Decision Analysis and Value of Information
• 3. Example NTG Uncertainty in Conglomerates
• ASSUMPTIONS:
• Oil in the clasts do not contribute towards recoverable reserves
• In FDP the clast volume has been excluded from the recoverable reserves
• PROBLEM
• The uncertainty in the estimated volume of clasts and their porosity leads to a high degree of uncertainty in the view on ‘Net Volume’.
Recoverable volume
• 4. Porosity Model Recoverable volume BUT THE EFFECTIVE POROSITY IS NOT THE TRUE POROSITY OF THE MATRIX NTG is estimated using a porosity cut-off
• 5. Porosity of Clast & Impact on NTG 0 0.2 0.4 0.6 0.8 1 PHI CLAST PHIT ERROR Clast Vol PHI Cut NTG 6% 8% 10% -10% -2% +10% +2% 3.7% 6.7% 9.7% 0% 0% The effective porosity calculated depends on the clast porosity. A Porosity cut-off combined with different estimates of porosity leads to different NTG values The Porosity cutoff depends on the porosity of the clasts Since an increase in the clast volume decreases the effective porosity, the matrix porosity is underestimated and a lower porosity cutoff is required.
• 6. Probability Distributions Unit 2 P90 = 0.03 P50 = 0.21 P10 = 0.61 But what does this mean ? I am most confident that I have a NTG less than 0.03. But this is an economic producing oil field. The uncertainty range is so large it tells me nothing about reality.
• 7. Alternative Model
• The NTG of the rock is the rock volume minus the volume of clasts. The NTG is equal to the the matrix volume (1-matrix volume).
• The matrix porosity should be corrected for the clast porosity
• The pore volume is calculated as the matrix volume multiplied by the matrix porosity
• 8. Observations
• Purely statistical analysis of petrophysical uncertainty can produce a a wide range of outcomes that is often unhelpful. It tells me nothing about reality (other than I don’t know the true value).
• Failure to recognise dependency leads to unrealistic ranges (e.g. the cut-off depends on the calculation of porosity)
• The uncertainty depends on the petrophysical model. Different models produce different views of uncertainty
• 9. What Do We Mean By Uncertainty
• Quote:
• Saturation exponent, cementation factor, Rw and Co-Cw used for water saturation calculation are not in agreement with measured data. Partly because of lack of sufficient high quality core data from xxxx. This will be addressed with recent conventional core and sidewalls that we have recovered. The uncertainty of +/- 5% in water saturation calculation needs to improve as much as possible once we get results of SCAL measurements in progress .
• Is +/- 5% reasonable
• Is it +/-5 % s.u. or +/- 5% of the value.
• What is the saturation anyway
• Lets spend 2 years and 200,000 USD collecting SCA data to reduce the uncertainty to +/- 2%
• Why – what’s the value
• 10. Uncertainty Is Nothing Without Risk
• Uncertainty: I don’t know if it going to rain on the 22 nd of September 2009.
• What’s the risk?
• It depends on what I am doing
• On the 22 nd of September I am going to work, as usual. So the fact it is raining changes nothing – I will still go to work
• I have a risk mitigation plan to carry an umberlla.
• 11. Uncertainty Is Nothing Without Risk
• Uncertainty: I don’t know if it going to rain on the 22 nd of September 2009.
• The oil business is good, it my wife’s birthday, so I am going to spend £8,000 on a surprise garden party to celebrate.
• So what’s the risk:
• I have to cancel the party and I already paid the caterer and band. I cancel the part and loose £8000 (but it is the thought that counts).
• Mitigation Plan
• Events insurance
• An alternative ‘indoor venue’.
• 12. Defining Uncertainty and Risk
• Uncertainty: The lack of complete certainty, that is, the existence of more than one possibility. The &quot;true&quot; outcome/state/result/value is not known.
• Measurement of Uncertainty: A set of probabilities assigned to a set of possibilities. Example: &quot;There is a 60% chance this market will double in five years“
• Risk: A state of uncertainty where some of the possibilities involve a loss, catastrophe, or other undesirable outcome
• Measurement of Risk: A set of possibilities each with quantified probabilities and quantified losses. Example: &quot;There is a 40% chance the proposed oil well will be dry with a loss of \$12 million in exploratory drilling costs&quot;.
• 13. Risk – A Bad Example
• 14. Risk?
• The uncertainty of +/- 5% in water saturation calculation needs to improve as much as possible once we get results of SCAL measurements in progress .
• The risk is?
• Project decisions?
• Value of the decision?
• 15. Probability
• Probability Distribution – What Does It Mean
• Frequentists talk about probabilities only when dealing with well defined random experiments. The probability of a random event denotes the relative frequency of occurrence of an experiment's outcome, when repeating the experiment (i.e tossing a coin, rolling a dice).
• Bayesians , however, assign probabilities to any statement whatsoever, even when no random process is involved. Probability, for a Bayesian, is a way to represent an individual's degree of belief in a statement, given the evidence.
• 16. Probability Distributions Average? P50??
• 17. Probability
• P50?
• You toss a coin 10 times:
• What is the average outcome?
• Heails, Taiads
• What is the P50
• There is a 50% chance of it being heads and a 50% chance of it being tails
• The P50 value is?
• It will land on it’s edge
• This has 0 chance of occurring
• Probability tells you about the likelihood of it being a higher or lower value – Not the probability of that exact value occurring.
• Probability - What matters is the view on risk?
• Are you prepared to gamble £10 on the coin toss being heads
• Are you prepared to gamble £5000
• The probability is the same – it is the perception of risk that changes.
• I lose £10.00: I am not buying a round tonight
• I lose £5000: my children don’t get to eat for a month
• The useful probability depends on the attitude to risk (risk averse or risk taker).
• 18. Monte Carlo Simulations – A Petrophysical Favourite
• 19. Monte-carlo simulation
• 20. Monte-Carlo Simulations Pitfalls
• Failure to capture co-dependencies – leadas to unreasonable HHH and LLL cases
• The uncertainty range in out can be unrealistically large (and unhelpful)
• Produces combinations that will never occur in nature
• Uncertainty Range is Independent of the petrophysical or subsurface model
• It is better to constrain the range of inputs using distinctive subsurface models
• 21. Creation of Subsurface Models
• 22. Model Based Approach Subsurface Model 1 Uncertainty Parameter 1 Uncertainty Parameter 2 Uncertainty Parameter 3 Uncertainty Parameter n Monte-Carlo Rules For Dependency Outcome 1 Outcome 2 VOI New Data Risk(s) Acceptable Subsurface Model 2 Subsurface Model n Model Risk(s) no Model valid for Project Decision Gate yes Project Economics Note: different models Can produce different risks. Requires roll-up of models And simulation on a lower resolution
• 23. Uncertainty & Risk Register
• 24. Uncertainty & Risk Register Theme Impact of Event Prevention Actions RISK EVENT Residual Risk L/I Mitigation If Event Occurs Decision Gate at Which Risk is Accepted Likelihood/ Impact (L/I)
• 25. VALUE OF INFORMATION I will drill horizontal wells since the Maximum ENPV is 36.5
• 26. VALUE OF INFORMATION VOI = 39.5 – 36.5 = \$3.0 M
• 27. Conclusions
• Petrophysical Uncertainty Who Cares?
• uncertainty of +/- 5% in water saturation calculation needs to improve as much as possible???
• It easy to generate large volume of statistical data for petro physical parameters, this is particularly true for Monte-Carlo simulations.
• Is the uncertainty range realistic for a single subsurface model. Are dependencies within the model captured. Does the uncertainty range require multiple subsurface models
• Risk: What is the impact of the uncertainty on the decisions to be taken. Does the impact risk require a reduction the uncertainty. Depends on how feel about the risk.
• Can the risk be mitigated
• What data can collected to reduce the uncertainty and what is the impact of reduced uncertainty on the risk
• What is the value of new information
• At what stage in the project should I accept the uncertainty and related risk(s).
• 28. END