Wanderley De Holanda, Rafael (2015-08). Capacitance Resistance Model in a Control Systems Framework: A Tool for Describing and Controlling Waterflooding Reservoirs. Master's Thesis. Thesis uri icon

abstract

  • The Capacitance Resistance Model (CRM) is a fast way for modeling and simulating gas and water flooding recovery processes, making it a great tool for improving flood management in real time. CRM is an input-output and material balance-based model, and uses only the most reliable data gathered throughout the production life of a flooded reservoir, which are bottom-hole pressures and production/injection rates. In this work, the CRM input-output relationship is explored by representing CRM in a control systems framework with state-space (SS) equations and transfer functions. Systems identification is applied for history matching using only production data to characterize the reservoir, evaluating interwell connectivities, time constants and productivity indices. A linear system SS equations define the relationship between inputs, outputs and states to completely describe system dynamics. We estimate the CRM parameters using a grey-box system identification algorithm, where production rates are computed simulating the system with SS-CRM instead of using ODE solutions as in prior works. The matrix form of the CRM history matching and a sensitivity analysis to the CRM parameters estimates are presented. Minimal realizations and reduced order models are easily obtained with the SS-CRM approach. The performance of three types of CRM formulations are analyzed: integrated (ICRM), producer based (CRMP), injector-producer based (CRMIP). Also, the methodology developed here are tested in three different reservoir setups: 1) homogeneous with flow barriers; 2) channelized; 3) shoreface environment. The new formulation in terms of state-space allows to write the CRM in a matrix representation, this provides more insight into reservoir behavior and is computationally faster. SS-CRM facilitates closed loop reservoir management by enabling CRM's use for linear control algorithms, which can improve tracking performance and predictability, and is amenable to real time optimization. Expressing the history matching problem using matrices provides structure and facilitates its implementation. CRM represented as a multi-input multi-output model is easier to apply in fields with large number of wells.
  • The Capacitance Resistance Model (CRM) is a fast way for modeling and simulating gas and water flooding recovery processes, making it a great tool for improving flood management in real time. CRM is an input-output and material balance-based model, and uses only the most reliable data gathered throughout the production life of a flooded reservoir, which are bottom-hole pressures and production/injection rates. In this work, the CRM input-output relationship is explored by representing CRM in a control systems framework with state-space (SS) equations and transfer functions. Systems identification is applied for history matching using only production data to characterize the reservoir, evaluating interwell connectivities, time constants and productivity indices.

    A linear system SS equations define the relationship between inputs, outputs and states to completely describe system dynamics. We estimate the CRM parameters using a grey-box system identification algorithm, where production rates are computed simulating the system with SS-CRM instead of using ODE solutions as in prior works. The matrix form of the CRM history matching and a sensitivity analysis to the CRM parameters estimates are presented. Minimal realizations and reduced order models are easily obtained with the SS-CRM approach. The performance of three types of CRM formulations are analyzed: integrated (ICRM), producer based (CRMP), injector-producer based (CRMIP). Also, the methodology developed here are tested in three different reservoir setups: 1) homogeneous with flow barriers; 2) channelized; 3) shoreface environment.

    The new formulation in terms of state-space allows to write the CRM in a matrix representation, this provides more insight into reservoir behavior and is computationally faster. SS-CRM facilitates closed loop reservoir management by enabling CRM's use for linear control algorithms, which can improve tracking performance and predictability, and is amenable to real time optimization. Expressing the history matching problem using matrices provides structure and facilitates its implementation. CRM represented as a multi-input multi-output model is easier to apply in fields with large number of wells.

publication date

  • August 2015