Robust Waterflooding Optimization of Multiple Geological Scenarios

Essen, Gijs van; Zandvliet, Maarten; Hof, Paul M.J. Van den; Bosgra, О.H.

doi:10.2523/102913-ms

Cited by 34 publications

(57 citation statements)

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“…The sub-optimality of the probabilistic outcomes can be severe, if the optimized model performance is heavily-dependent on particular geologic features and/or their specific spatial arrangements as represented in the subsurface realization subject to optimization. The robust optimization scheme (see, for example, van Essen et al 2009), which accounts for the effects of subsurface uncertainties in a more integrated fashion within the optimization workflow, is more suitable for such problems. In the robust optimization approach, the expected value of the objective function with or without a penalty on its variability is optimized.…”

Section: Key Enablers For Efficient Iup Pattern Optimization and Uncementioning

confidence: 99%

Techniques for Effective Simulation, Optimization, and Uncertainty Quantification of the In-situ Upgrading Process

et al. 2013

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Strongly temperature-dependent compositional flow and transport, chemical reactions, delivery of energy into the subsurface through downhole heaters, and complex natural fracture architecture render the dynamic modeling of In-situ Upgrading Process (IUP) a computationally challenging endeavor for carbonate extra-heavy oil resources. Economic performance indicators for such recovery methods can be considerably enhanced via simulation or simulation-based proxy models within an optimization framework (e.g., maximizing Net Present Value through achieving an optimal compromise between hydrocarbon recovery and the number of heater wells). The IUP is endowed with uncertain subsurface parameters as in the case of other recovery mechanisms. The number of subsurface uncertainties is especially large and the impacts of these uncertainties are intricate when IUP is applied to a complex naturally-fractured carbonate reservoir. Simulation results must reflect the impacts of these uncertainties; hence they should always deliver "expected-value production functions" and their attached uncertainty ranges, in short, the "error bars". Both the optimization and the uncertainty quantification workflows require (typically multiple) multi-scenario simulations, and are therefore very compute intensive.We describe our recent developments in simulation techniques, optimization algorithms, tool capabilities, and highperformance computing protocols that in unison form a massively parallel simulation/optimization/uncertainty-quantification workflow, in which it is almost equally easy to produce recovery time-functions with an attached uncertainty range, as it is to run a single simulation. Uncertainties are integral part of the simulation models in our dynamic modeling workflow. Our inhouse simulation platform supports various optimization and uncertainty quantification methods, such as conventional as well as robust optimization using a novel Simultaneous Perturbation and Multivariate Interpolation technique, Experimental Design, and Monte Carlo simulation, that can be linked together through a unified script-based interface, to carry out optimization in the presence of subsurface uncertainties and to quantify the impact of these uncertainties on simulation results. Application of our massively parallel dynamic modeling workflow is illustrated on a proprietary IUP recovery method for a complex naturally fractured extra-heavy oil (bitumen) reservoir as example. After briefly explaining these recovery processes and the modeling approach, we show the techniques (including their accompanying application results) that (1) notably accelerate the (single-model) simulation process, (2) effectively identify the predominant subsurface uncertainties, (3) rapidly optimize heater-producer patterns under the influence of predominant subsurface uncertainties, and (4) efficiently compute expected-value production functions with error bars.

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Section: Key Enablers For Efficient Iup Pattern Optimization and Uncementioning

confidence: 99%

Techniques for Effective Simulation, Optimization, and Uncertainty Quantification of the In-situ Upgrading Process

et al. 2013

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“…Sarma et al (2005b) used the adjoint method to compute the expectation of the NPV with respect to the controls, and the uncertainty of the reservoir description is propagated using the probability collocation method (PCM). van Essen et al (2006) also considered using the expectation of the NPV as the objective function. They used an ensemble of reservoir models to reflect the uncertainty of the geological model and solved the adjoint equations for each realization.…”

Section: Ensemble-based Optimization (Enopt)mentioning

confidence: 99%

Efficient Ensemble-Based Closed-Loop Production Optimization

Chen

Oliver

Zhang³

2008

All Days

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With the advances in smart well technology, substantially higher oil recovery can be achieved by intelligently managing the operations in a closed-loop optimization framework. The closed-loop optimization consists of two parts: geological model updating and production optimization. Both of these parts require gradient information to minimize or maximize an objective function: squared data mismatch or the net present value (or other quantities depending on financial goals), respectively. Alternatively, an ensemble-based method can acquire the gradient information through the correlations provided by the ensemble. Computation of the optimal controls in this way is nearly independent of the number of control variables, reservoir simulator and simulation solver. In this paper, we propose an ensemble-based closed-loop optimization method that combines a novel ensemble-based optimization scheme (EnOpt) with the ensemble Kalman filter (EnKF). The EnKF has recently been found suitable for sequential data assimilation in large-scale nonlinear dynamics. It adjusts reservoir model variables to honor observations and propagates uncertainty in time. The EnOpt optimizes the expectation of the net present value based on the updated reservoir models. The proposed method is fairly robust, completely adjoint-free and can be readily used with any reservoir simulator. The ensemble-based closed-loop optimization method is illustrated with a waterflood example subject to uncertain reservoir description. Results are compared with other possible reservoir operation scenarios, such as, wells with no controls, reactive control, and optimization with known geology. The comparison shows that the ensemble-based closed-loop optimization is able to history match the main geological features and increase the net present value to a level comparable with the hypothetical case of optimizing based on known geology.

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“…Yeten et al (2003) described an approach to account for geological uncertainty during well-location optimization with the aid of multiple models. Van Essen et al (2009) provides a list of references of non-petroleum engineering applications that incorporated uncertainty within the modeling and control framework. From these, they imported the terminology 'robust optimization' in the petroleum literature, i.e.…”

Section: Introductionmentioning

confidence: 99%

Ensemble-Based Multi-Objective Optimization of On-Off Control Devices Under Geological Uncertainty

Fonseca

Leeuwenburgh²,

Rossa³

et al. 2015

SPE Reservoir Simulation Symposium

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We consider robust ensemble-based (EnOpt) multi-objective production optimization of on-off inflow control devices (ICDs) for a sector model inspired on a real-field case. The use of on-off valves as optimization variables leads to a discrete control problem. We propose a re-parameterization of such discrete controls in terms of switching times, i.e. we optimize the time at which a particular valve is either open or closed. This transforms the discrete control problem into a continuous control problem which can be efficiently handled with the EnOpt method. Additionally this leads to a significant reduction in the number of controls which is expected to be beneficial for gradient quality when using approximate gradients. We consider an ensemble of sector models where the uncertainty is described by different permeability, porosity, net-to-gross and initial water saturation fields. The controls are the ICD settings over time in the three horizontal injection wells, with approximately 15 ICDs per well. Different optimized strategies resulting from different initial strategies were compared. We achieved a mean 4.2% increase in expected NPV at a 10% discount rate compared to a traditional pressure maintenance strategy. Next, we performed a sequential bi-objective optimization, and achieved an increase of 9.2% in the secondary objective (25% discounted NPV to emphasize short-term production gains) for a minimal decrease of 1% in the primary objective (0% discounted NPV to emphasize long-term recovery gains), as averaged over the 100 geological realizations. The workflow was repeated for alternative numbers of ICDs showing that having fewer control options lowers the expected value for this particular case. The results demonstrate that ensemble-based optimization workflows are able to produce improved robust recovery strategies for realistic field sector models against acceptable computational cost.

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Robust Waterflooding Optimization of Multiple Geological Scenarios

Cited by 34 publications

References 0 publications

Techniques for Effective Simulation, Optimization, and Uncertainty Quantification of the In-situ Upgrading Process

Techniques for Effective Simulation, Optimization, and Uncertainty Quantification of the In-situ Upgrading Process

Efficient Ensemble-Based Closed-Loop Production Optimization

Ensemble-Based Multi-Objective Optimization of On-Off Control Devices Under Geological Uncertainty

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