## High Order Adjoint Derivatives using ESDIRK Methods for Oil Reservoir Production Optimization

Publication: Research - peer-review › Article in proceedings – Annual report year: 2012

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**High Order Adjoint Derivatives using ESDIRK Methods for Oil Reservoir Production Optimization.** / Capolei, Andrea; Stenby, Erling Halfdan; Jørgensen, John Bagterp.

Publication: Research - peer-review › Article in proceedings – Annual report year: 2012

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*ECMOR XIII – 13th European Conference on the Mathematics of Oil Recovery.*pp. A42.

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*ECMOR XIII – 13th European Conference on the Mathematics of Oil Recovery.*(pp. A42)

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*ECMOR XIII – 13th European Conference on the Mathematics of Oil Recovery.*2012. A42.

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TY - GEN

T1 - High Order Adjoint Derivatives using ESDIRK Methods for Oil Reservoir Production Optimization

A1 - Capolei,Andrea

A1 - Stenby,Erling Halfdan

A1 - Jørgensen,John Bagterp

AU - Capolei,Andrea

AU - Stenby,Erling Halfdan

AU - Jørgensen,John Bagterp

PY - 2012

Y1 - 2012

N2 - In production optimization, computation of the gradients is the computationally expensive step. We improve the computational efficiency of such algorithms by improving the gradient computation using high-order ESDIRK (Explicit Singly Diagonally Implicit Runge-Kutta) temporal integration methods and continuous adjoints . The high order integration scheme allows larger time steps and therefore faster solution times. We compare gradient computation by the continuous adjoint method to the discrete adjoint method and the finite-difference method. The methods are implemented for a two phase flow reservoir simulator. Computational experiments demonstrate that the accuracy of the sensitivities obtained by the adjoint methods are comparable to the accuracy obtained by the finite difference method. The continuous adjoint method is able to use a different time grid than the forward integration. Therefore, it can compute these sensitivities much faster than the discrete adjoint method and the finite-difference method. On the other hand, the discrete adjoint method produces the gradients of the numerical schemes, which is beneficial for the numerical optimization algorithm. Computational experiments show that when the time steps are controlled in a certain range, the continuous adjoint method produces gradients sufficiently accurate for the optimization algorithm and somewhat faster than the discrete adjoint method.

AB - In production optimization, computation of the gradients is the computationally expensive step. We improve the computational efficiency of such algorithms by improving the gradient computation using high-order ESDIRK (Explicit Singly Diagonally Implicit Runge-Kutta) temporal integration methods and continuous adjoints . The high order integration scheme allows larger time steps and therefore faster solution times. We compare gradient computation by the continuous adjoint method to the discrete adjoint method and the finite-difference method. The methods are implemented for a two phase flow reservoir simulator. Computational experiments demonstrate that the accuracy of the sensitivities obtained by the adjoint methods are comparable to the accuracy obtained by the finite difference method. The continuous adjoint method is able to use a different time grid than the forward integration. Therefore, it can compute these sensitivities much faster than the discrete adjoint method and the finite-difference method. On the other hand, the discrete adjoint method produces the gradients of the numerical schemes, which is beneficial for the numerical optimization algorithm. Computational experiments show that when the time steps are controlled in a certain range, the continuous adjoint method produces gradients sufficiently accurate for the optimization algorithm and somewhat faster than the discrete adjoint method.

BT - ECMOR XIII – 13th European Conference on the Mathematics of Oil Recovery

T2 - ECMOR XIII – 13th European Conference on the Mathematics of Oil Recovery

SP - A42

ER -