Proxy models for caprock pressure and temperature dynamics during steam-assisted gravity drainage process

Abstract The usage of first principles-based dynamic models for advanced control, monitoring and optimization of petroleum reservoirs is constrained by the large scale nature of the models. The parametric uncertainty makes the process even more challenging; hence, development of proxy models is an attractive proposition. In this work, proxy models are developed from spatio-temporal data of pressure and temperature in the caprock during steam-assisted gravity drainage operation. The first proxy model addresses the issue of reduced-order dynamic modelling of the caprock pressure and temperature fields based on proper orthogonal decomposition and system identification using data from the first principles model. The second proxy model takes the first step towards dynamic analysis of factor of safety in reservoir management by modelling the evolution of clusters of high, medium and low pressure regions using graph theory and subspace modelling. System theoretic properties of these proxy models and their practical relevance is also analysed.

[1]  Andy J. Keane,et al.  Recent advances in surrogate-based optimization , 2009 .

[2]  Ronald L. Wasserstein,et al.  Monte Carlo: Concepts, Algorithms, and Applications , 1997 .

[3]  L. Ricardez‐Sandoval,et al.  Dynamic reduced order modeling of an entrained-flow slagging gasifier using a new recirculation ratio correlation , 2017 .

[4]  Rajan G. Patel,et al.  Real‐time feedback control of SAGD wells using model predictive control to optimize steam chamber development under uncertainty , 2018 .

[5]  Prashanth Siddhamshetty,et al.  Model order reduction of nonlinear parabolic PDE systems with moving boundaries using sparse proper orthogonal decomposition: Application to hydraulic fracturing , 2018, Comput. Chem. Eng..

[6]  R. Chalaturnyk,et al.  Caprock Safety Factor Assessment of SAGD Projects , 2015 .

[7]  Jan Dirk Jansen,et al.  Generation of Low-Order Reservoir Models Using System-Theoretical Concepts , 2003 .

[8]  L. Ricardez‐Sandoval,et al.  Reduced-Order Modeling of a Commercial-Scale Gasifier Using a Multielement Injector Feed System , 2017 .

[9]  P. Collins Geomechanical Effects on the SAGD Process , 2007 .

[10]  Denis Igorevich Zubarev,et al.  Pros and Cons of Applying Proxy-models as a Substitute for Full Reservoir Simulations , 2009 .

[11]  A. Chatterjee An introduction to the proper orthogonal decomposition , 2000 .

[12]  R. Chalaturnyk,et al.  Real-time reservoir model updating in thermal recovery: Application of analytical proxies and Kalman filtering , 2015 .

[13]  Vinay Prasad,et al.  Proxy Modeling of the Production Profiles of SAGD Reservoirs Based on System Identification , 2015 .

[14]  M. Carlson An Analysis of the Caprock Failure at Joslyn , 2012 .

[15]  R. Chalaturnyk,et al.  Reservoir Characterization: Application of Extended Kalman Filter and Analytical Physics- Based Proxy Models in Thermal Recovery , 2011 .

[16]  Eduardo Gildin,et al.  Closed-Loop Reservoir Management: Do we need complex models? , 2011 .

[17]  Michael S. Eldred,et al.  Uncertainty Quantification In Large Computational Engineering Models , 2001 .

[18]  Joseph Sang-Il Kwon,et al.  Development of local dynamic mode decomposition with control: Application to model predictive control of hydraulic fracturing , 2017, Comput. Chem. Eng..

[19]  Prashanth Siddhamshetty,et al.  Temporal clustering for order reduction of nonlinear parabolic PDE systems with time-dependent spatial domains: Application to a hydraulic fracturing process , 2017 .

[20]  P. A. Slotte,et al.  Response Surface Methodology Approach for History Matching and Uncertainty Assessment of Reservoir Simulation Models , 2008 .

[21]  Roger M. Butler,et al.  Steam-Assisted Gravity Drainage: Concept, Development, Performance And Future , 1994 .

[22]  Eduardo Gildin,et al.  Permeability Parametrization Using Higher Order Singular Value Decomposition (HOSVD) , 2013, 2013 12th International Conference on Machine Learning and Applications.

[23]  Matthias Haupt,et al.  Efficient Surrogate Modelling of Nonlinear Aerodynamics in Aerostructural Coupling Schemes , 2014 .

[24]  Jalali Jalal,et al.  COALBED METHANE RESERVOIR SIMULATION AND UNCERTAINTY ANALYSIS WITH ARTIFICIAL NEURAL NETWORKS , 2010 .

[25]  J. Jansen,et al.  Closed-loop reservoir management , 2005 .

[26]  G. Chavent,et al.  History Matching by Use of Optimal Theory , 1975 .

[27]  Arun K. Tangirala,et al.  Principles of System Identification , 2014 .

[28]  van Gm Essen,et al.  A two-level strategy to realize life-cycle production optimization in an operational setting , 2013 .

[29]  P. C. Shah,et al.  Reservoir History Matching by Bayesian Estimation , 1976 .

[30]  Athanasios C. Antoulas,et al.  Approximation of Large-Scale Dynamical Systems , 2005, Advances in Design and Control.

[31]  Prodromos Daoutidis,et al.  Graph representation and decomposition of ODE/hyperbolic PDE systems , 2017, Comput. Chem. Eng..