2008 ifac world congress: oil and gas production optimization - lost potential due to uncertainty,...
DESCRIPTION
The information content in measurements of offshore oil and gas production is often low, and when a production model is fitted to such data, uncertainty may result. If production is optimized with an uncertain model, some potential production profit may be lost. The costs and risks of introducing additional excitation are typically large and cannot be justified unless the potential increase in profits are quantified. In previous work it is discussed how bootstrapping can be used to estimate uncertainty resulting from fitting production models to data with low information content. In this paper we propose how lost potential resulting from estimated uncertainty can be estimated using Monte-Carlo analysis. Based on a conservative formulation of the production optimization problem, a potential for production optimization in excess of 2% and lost potential due to the form of uncertainty considered in excess of 0.5% was identified using field data from a North Sea field.TRANSCRIPT
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Oil and gas production optimization; lost potential due to uncertainty
Steinar M. Elgsæter, Norwegian University of Science and Technology (NTNU)
Olav Slupphaug, ABB
Tor Arne Johansen, NTNU
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Production optimization• Day-to-day optimization of production
Multiphase/Gas-lift/Production chokes/Well tests
u,d
y
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Current practice and challenges
• Production very difficult to model (multiphase pipe flow)
• Current practice: optimization with rigourous ”engineering” models, with varying success
• Fields operated such that there is little variation/excitation in data against which to fit models*
• ”feed forward” production optimization
* Elgsaeter, S.M., Slupphaug, O. And Johansen, T.A. (2007), Challenges in Parameter Estimation of Models for Offshore Oil and Gas Production Optimization, International Petroleum Technology Conference, Dubai, 4.Dec 2007, IPTC11728
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Excitation can ”close the loop”, but has costs and risk
* Elgsaeter,S.M., Slupphaug, O. and Johansen, T.A.(2008), Production Optimization; System Identification and Uncertainty Estimation, Intelligent Energy ’08, Amsterdam, Feb 2008, SPE112186
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Deadlock
Practicioners do not see the value of excitation and hence will not allow us to implement it
We need to implement excitation to doucment its value
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”Data-driven” models• ”Stationary” (no dynamic terms)• Motivated by the concepts of system identification
– Simple/pragmatic/inferred from data
• Locally valid • Model the oil, gas and water produced from each well, fitted to
measured total rates of oil, gas and water• Two versions for comparison:
”well test””production choke kernel” (modeled disturbance )
”gas-lift kernels”(decision variables)
Fitted paramters
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Real-world data set
• Offshore North Sea field
• 20 gas-lifted platform wells
• Data:– 5 months
– ”normal operations”
– Apparenlty some excitation but not excitation is not aboundant
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Quantifying model uncertainty• Bootstrapping
– ”Multiple model”
– Computationally intensive
– Re-solves parameter estimation problem many times with resampled data-sets
– Multi-variable uncertainty estimates
• Result: – ”uncertainty fans” which
express information content or lack thereof in data
– Several houndred similar but different paramters which are all plausible
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What is the cost of not being able to differentiate among the models identified?
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Production optimization
• Profits(M) and constraints(c) are related to modeled rates of oil, gas and water– Profit: total produced oil– Constraint 1: max total produced gas (<= current rate)– Constraint 2: max total produced water (<= current rate)– Constraint 3: (Uprc is design variable)– Constraint 4: (flow assurance on some wells)
Model
Constraints
Profit
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Realized and lost potential
• Approach:estimate distributions for Po and Lu using Monte-Carlo simulation
• If there is little excitation and model uncertainty as a result, we cannot expect to realize all the potential of production optimization
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Monte-Carlo simulation of loss due to uncertainty
• Solve production optimization problem for all models identified using bootstrapping
• For each plausible model identified using bootstrapping (model t)
– For each plausible model identified using bootstrapping (model f)
• Simulate how much of the potential production optimization is able to realize if we implement decision variable found by optimization with (model f) when in reality production is described by (model t)
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• Results model-dependent• 1.order model results in more
conservative losses (and with less variation)
• Lu and Po depend on choice of Uprc,
the ratio between the two less so• Analysis indicates that
– a potential in excess of 2% exists– as much as half of this potential
may be unrealized unless excitation is introduced
Blue:1.order(linear) modelGreen:2.order modelTop: upper 95. percentileCenter: meanBottom lower 95.percentile
ResultsPo(%)
Lu (%)
Lu/Po
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Conclusions
• A way to estimate the value of closed-loop modeling/optimization that is consistent with data – attempt to ”let the data speak”
• A tool for communicating the value of closing-the-loop to non-control engineers. – The link between how a field is operated and the success of subsequent
production optimization is highlighted
– This analysis can hopefully convince practicioners to operate their fields in a manner which introduces more excitation
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Thank you for your attention