Effect of Time Stepping Strategy on Adjoint-based Production Optimization

Volkov, Oleg; Voskov, Denis

doi:10.3997/2214-4609.20141894

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…Using the capabilities of ADETL, an Automatic Differentiation General Purpose Research Simulator (ADGPRS) was developed (Voskov and Tchelepi, 2012). ADGPRS is a unified reservoir simulation framework providing an extensive set of nonlinear formulations (Voskov, 2012;Zaydullin et al, 2013); flexible spatial discretization (Zhou et al, 2011); extended physics models (Iranshahr et al, 2013;Zaydullin et al, 2014a;Garipov et al, 2016); inverse capabilities (Kourounis et al, 2010;Volkov and Voskov, 2016); geothermal formulation (Wong et al, 2015). We use ADGPRS for the implementation and performance comparisons in this study.…”

Section: Introductionmentioning

confidence: 99%

Operator-based Linearization for Non-isothermal Multiphase Compositional Flow in Porous Media

Khait¹,

Voskov²

2016

Proceedings

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SUMMARYNon-isothermal multiphase compositional simulation is based on the solution of governing equations describing mass and energy transfer in the subsurface. The solution strategy requires a linearization of strongly nonlinear governing equations describing the process. Usually, a Newton-based method is used for the linearization that demands an assembly of a Jacobian matrix and residuals for a fully coupled system of equations. Recently, a new linearization approach was proposed for compositional problems and tested for simulation of binary compositional and low-enthalpy geothermal flow. The key idea of the approach is the transformation of discretised mass conservation equations to an operator form with separate space-dependent and state-dependent components. This transformation provides an opportunity for an approximate representation of exact physics (physical properties) of the problem. Specifically, each term of conservation equations is represented as a product of two different operators. The first operator depends on a current physical state of a system and contains different properties such as density, viscosity, relative permeability, etc. The second operator captures both spatially altered properties such as permeability and the rest of state variables such as pressure in the discrete approximation of the gradient. At the pre-processing stage, all state-dependent operators are uniformly parametrized within the physical space of the problem (pressure-composition intervals). During the simulation process, a multi-linear interpolation is applied to approximate the first type of operators, while the second type of operators is processed based on the conventional approach. In this work, we have extended this approach to general purpose simulation. We introduced the operator-based parametrization of mass and energy conservations equation based on the pressure, composition, temperature, and porosity. In addition, the approach has been extended and tested on truly multi-component systems of practical interest. The accuracy and robustness of the new method have been tested against the results of simulations based on the conventional approach.

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Section: Introductionmentioning

confidence: 99%

Operator-based Linearization for Non-isothermal Multiphase Compositional Flow in Porous Media

Khait¹,

Voskov²

2016

Proceedings

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Optimizing Production Schedule of Coalbed Methane Wells Using a Stochastic Evolution Algorithm

Hu¹,

Zhang

Wang

et al. 2020

Mathematical Problems in Engineering

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Production optimization of coalbed methane (CBM) is a complex constrained nonlinear programming problem. Finding an optimal decision is challenging since the coal seams are generally heterogeneous with widespread cleats, fractures, and matrix pores, and the stress sensitivities are extremely strong; the production of CBM wells needs to be adjusted dynamically within a reasonable range to fit the complex physical dynamics of CBM reservoirs to maximize profits on a long-term horizon. To address these challenges, this paper focuses on the step-down production strategy, which reduces the bottom hole pressure (BHP) step by step to expand the pressure drop radius, mitigate the formation damage, and improve CBM recovery. The mathematical model of CBM well production schedule optimization problem is formulated. The objective of the optimization model is to maximize the cumulative gas production and the variables are chosen as BHP declines of every step. BHP and its decline rate constraints are also considered in the model. Since the optimization problem is high dimensional, nonlinear with many local minima and maxima, covariance matrix adaptation evolution strategy (CMA-ES), a stochastic, derivative-free intelligent algorithm, is selected. By integrating a reservoir simulator with CMA-ES, the optimization problem can be solved successfully. Experiments including both normal wells and real featured wells are studied. Results show that CMA-ES can converge to the optimal solution efficiently. With the increase of the number of variables, the converge rate decreases rapidly. CMA-ES needs 3 or even more times number of function evaluations to converge to 100% of the optimum value comparing to 99%. The optimized schedule can better fit the heterogeneity and complex dynamic changes of CBM reservoir, resulting a higher production rate peak and a higher stable period production rate. The cumulative production under the optimized schedule can increase by 20% or even more. Moreover, the effect of the control frequency on the production schedule optimization problem is investigated. With the increases of control frequency, the converge rate decreases rapidly and the production performance increases slightly, and the optimization algorithm has a higher risk of falling into local optima. The findings of this study can help to better understanding the relationship between control strategy and CBM well production performance and provide an effective tool to determine the optimal production schedule for CBM wells.

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Effect of Time Stepping Strategy on Adjoint-based Production Optimization

Cited by 2 publications

References 19 publications

Operator-based Linearization for Non-isothermal Multiphase Compositional Flow in Porous Media

Operator-based Linearization for Non-isothermal Multiphase Compositional Flow in Porous Media

Optimizing Production Schedule of Coalbed Methane Wells Using a Stochastic Evolution Algorithm

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