goal to manage water flooding of a reservoir so as to optimize oil production

GOAL To manage water flooding of a reservoir so as to optimize oil production. To employ an optimal model-based control framework that uses uncertain parameter updating and a particular reduced-order model based on the update. INTRODUCTION The accuracy of the parameters impacts the prediction capability of the reservoir model that is used for closed-loop reservoir management. It is justified to update the uncertain model parameters for any model-based application. Latin hypercube Hammersley sequence sampling (LHHS) technique is used for efficient propagation of the uncertain parameters through the reservoir model to quantify the uncertain parameters and their effects on the model results. Markov chain Monte Carlo (MCMC) is one approach to address parameter updating; however MCMC requires excessive executions of the first-principles reservoir model to generate the updates. To avoid this computational burden, a method such as Partial least squares (PLS) will be used to determine the relationships between the uncertain parameters and the model’s predictions. MCMC combined with PLS provides real-time efficient updates of the uncertain parameters. Another updating approach is to employ an ensemble Kalman filter (EnKF). The EnKF updates the uncertain parameters when new measured data are available. In this manner, estimates of the current state of the model account for the uncertainties that are in the state variables and the measurements. After updating, a reduced-order model can be identified to enable efficient real-time optimization. Framework Example : 5-spot pattern reservoir Closed-loop Reservoir Management Using a Reduced-order Model-based Control & Uncertainty Propagation Framework Grant Title: Closed-loop Reservoir Management Using a Reduced-order Model-based Control & Uncertainty Propagation Framework Grant Number: 0927796 NSF Program: Dynamical Systems PI Name(s): Karlene A. Hoo Updated Results •MCMC •EnKF •Production REFERENCES •Chen, Y and KA Hoo (2010). Compt. & Chem. Enging., 34(10), 1597- 1605 •Chen, Y and KA Hoo (2010). ADCONIP, under review •Chen, Y and KA Hoo (2010). Int. J. Sys. Sci. , under review True porosity MCMC approach: Y m (n): measurements, Θ: uncertain parameter. EnKF approach: Y f : forecast, Y a : assimilated estimates of the states. Schematic of a two- dimensional reservoir and wells ↓: water injection well ↑: oil production well Posterior distribution Updated porosity Prior distribution True permeability Updated permeability MCMC: (◇) oil; (△) water EnKF: (+) oil; (▽) water Reference: (o) oil; (*) water MCMC: blue EnKF: green Reference: red Plant Model- based Controlle r Reduced- order Model Y m Model Y a (n (n ) Y f (n) (n) Θ k MCMC LHHS Parameter Updates PLS Model Θ k+1 k+1 Paramet er Updates EnKF Y m Y m Y(n) Y(n)

Upload: raymond-martin

Post on 30-Dec-2015

27 views

Category:

Documents

0 download

Report

Download

Embed Size (px):

DESCRIPTION

Closed-loop Reservoir Management Using a Reduced-order Model-based Control & Uncertainty Propagation Framework Grant Title: Closed-loop Reservoir Management Using a Reduced-order Model-based Control & Uncertainty Propagation Framework - PowerPoint PPT Presentation

TRANSCRIPT

Page 1: GOAL To manage water flooding of a reservoir so as to optimize oil production

INTRODUCTIONThe accuracy of the parameters impacts the prediction capability of the reservoir model that is used for closed-loop reservoir management. It is justified to update the uncertain model parameters for any model-based application. Latin hypercube Hammersley sequence sampling (LHHS) technique is used for efficient propagation of the uncertain parameters through the reservoir model to quantify the uncertain parameters and their effects on the model results. Markov chain Monte Carlo (MCMC) is one approach to address parameter updating; however MCMC requires excessive executions of the first-principles reservoir model to generate the updates. To avoid this computational burden, a method such as Partial least squares (PLS) will be used to determine the relationships between the uncertain parameters and the model’s predictions. MCMC combined with PLS provides real-time efficient updates of the uncertain parameters. Another updating approach is to employ an ensemble Kalman filter (EnKF). The EnKF updates the uncertain parameters when new measured data are available. In this manner, estimates of the current state of the model account for the uncertainties that are in the state variables and the measurements. After updating, a reduced-order model can be identified to enable efficient real-time optimization.

Framework Example: 5-spot pattern reservoir

Closed-loop Reservoir Management Using a Reduced-order Model-based Control & Uncertainty Propagation Framework

Grant Title: Closed-loop Reservoir Management Using a Reduced-order Model-based Control & Uncertainty Propagation FrameworkGrant Number: 0927796 NSF Program: Dynamical Systems PI Name(s): Karlene A. Hoo

Updated Results•MCMC

•EnKF

•Production

REFERENCES•Chen, Y and KA Hoo (2010). Compt. & Chem. Enging., 34(10), 1597-1605•Chen, Y and KA Hoo (2010). ADCONIP, under review•Chen, Y and KA Hoo (2010). Int. J. Sys. Sci. , under review

True porosity

MCMC approach: Ym(n): measurements, Θ: uncertain parameter. EnKF approach: Yf: forecast, Ya: assimilated estimates of the states.

Schematic of a two-dimensional reservoir and wells

↓: water injection well↑: oil production well

Posterior distribution

Updated porosity

Prior distribution

True permeability

Updated permeability

MCMC: (◇) oil; (△) water

EnKF: (+) oil; (▽) water

Reference: (o) oil;