Refined ensemble-based waterflooding optimization subject to field-wide constraints

Tueros, Juan Alberto Rojas; Horowitz, Bernardo; Willmersdorf, Ramiro Brito; Oliveira, Diego Felipe Barbosa de

doi:10.1007/s10596-019-09866-z

Cited by 4 publications

(5 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The second term in equation ( 7) generates smooth control realizations for gradient computation. This does not guarantee smoothness of controls during the optimization process (Tueros et al, 2020). In this study, the realizations of control variables for ensemble-based satisfy all constraints (e.g., wells and capacity allowed).…”

Section: Computation Of the Gradient By Ensemble-based Methodsmentioning

confidence: 97%

“…The following steps are used to calculate the sensitivity matrix and to obtain the ensemble-based approximate gradient (Tueros et al, 2020):…”

Section: Computation Of the Gradient Using Sensitivity Matrixmentioning

confidence: 99%

“…In this study, we use the smoothing technique proposed by Tueros et al (2020) devised to avoid abrupt jumps in control trajectories during the optimization process. The strategy is based on adding a new term to the objective function of equation ( 4).…”

Section: Smoothing Factormentioning

confidence: 99%

“…We obtain the original optimization problem, if this factor is zero. In this study, we used b = 10 À3 for all examples as recommended by Tueros et al (2020). The gradient of the smoothing term can be easily computed analytically.…”

Section: Smoothing Factormentioning

confidence: 99%

“…The SQP algorithm is used to determine Kogen parameters, and well control trajectories in the waterflooding problem (Alim, 2013;Pinto et al, 2020;Tueros et al, 2020). SQP is a gradient-based algorithm, so derivatives are required.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Ensemble-based method with combined fractional flow model for waterflooding optimization

Oliveira

Horowitz

Tueros

2021

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

Self Cite

View full text Add to dashboard Cite

Proxy models are widely used to estimate parameters such as interwell connectivity in the development and management of petroleum fields due to their low computational cost and not require prior knowledge of reservoir properties. In this work, we propose a proxy model to determine both oil and water production to maximize reservoir profitability. The approach uses production history and the Capacitance and Resistance Model based on Producer wells (CRMP), together with the combination of two fractional flow models, Koval [Cao (2014) Development of a Two-phase Flow Coupled Capacitance Resistance Model. PhD Dissertation, The University of Texas at Austin, USA] and Gentil [(2005) The use of Multilinear Regression Models in patterned waterfloods: physical meaning of the regression coefficient. Master’s Thesis, The University of Texas at Austin, USA]. The proposed combined fractional flow model is called Kogen. The combined fractional flow model can be formulated as a constrained nonlinear function fitting. The objective function to be minimized is a measure of the difference between calculated and observed Water cut (Wcut) values or Net Present Values (NPV). The constraint limits the difference in water cuts of the Koval and Gentil models at the time of transition between the two. The problem can be solved using the Sequential Quadratic Programming (SQP) algorithm. The parameters of the CRMP model are the connectivity between wells, time constant and productivity index. These parameters can be found using a Nonlinear Least Squares (NLS) algorithm. With these parameters, it is possible to predict the liquid rate of the wells. The Koval and Gentil models are used to calculate the Wcut in each producer well over the concession period which in turn allows to determine the accumulated oil and water productions. To verify the quality of Kogen model to forecast oil and water productions, we formulated an optimization problem to maximize the reservoir profitability where the objective function is the NPV. The design variables are the injector and producer well controls (liquid rate or bottom hole pressure). In this work the optimization problem is solved using a gradient-based method, SQP. Gradients are approximated using an ensemble-based method. To validate the proposed workflow, we used two realistic reservoirs models, Brush Canyon Outcrop and Brugge field. The results are shown into three stages. In the first stage, we analyze the ensemble size for the gradient computation. Second, we compare the solutions obtained with the three fractional flow models (Koval, Gentil and Kogen) with results achieved directly from the simulator. Third, we use the solutions calculated with the proxy models as starting points for a new high-fidelity optimization process, using exclusively the simulator to calculate the functions involved. This study shows that the proposed combined model, Kogen, consistently generated more accurate results. Also, CRMP/Kogen proxy model has demonstrated its applicability, especially when the available data for model construction is limited, always producing satisfactory results for production forecasting with low computational cost. In addition, it generates a good warm start for high fidelity optimization processes, decreasing the number of simulations by approximately 65%.

show abstract

Section: Computation Of the Gradient By Ensemble-based Methodsmentioning

confidence: 97%

“…The following steps are used to calculate the sensitivity matrix and to obtain the ensemble-based approximate gradient (Tueros et al, 2020):…”

Section: Computation Of the Gradient Using Sensitivity Matrixmentioning

confidence: 99%

Section: Smoothing Factormentioning

confidence: 99%

Section: Smoothing Factormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Ensemble-based method with combined fractional flow model for waterflooding optimization

Oliveira

Horowitz

Tueros

2021

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

Self Cite

View full text Add to dashboard Cite

show abstract

Pioneer Field Pilot of Optimal Reservoir Management in Campos Basin

Oliveira

Pereira

Silveira

et al. 2019

SPE Reservoir Evaluation &Amp; Engineering

View full text Add to dashboard Cite

SummaryReservoir management in offshore fields is a challenging task, particularly for mature fields because of a typical excessive production of water and/or gas. Because of several constraints on facilities capacity, an assisted reservoir management process can deliver solutions to optimally operate offshore fields, seeking to increase oil production with better assessment of water and gas production and injection. Optimal reservoir management (ORM) can be applied aiming at maximizing reservoir performance and to deliver well controls applicable to field operations. In this work, we implemented an assisted optimization procedure to maximize overall oil production for a field offshore Brazil in Campos Basin.We applied our ORM technique in an important field offshore Brazil, where cumulative oil production is maximized by optimally controlling water rates through injection wells. Injection rates can vary with time, honoring operational requirements of smoothness. Geomechanical limits on injection pressures are considered to avoid loss of rock integrity, and platform constraints on overall production and injection are imposed at all times. Our approach deals with reservoir uncertainties described within a large set of calibrated simulation models to decide on optimal injection rates, taking into account possible risks.The model-based ORM under uncertainty that we developed showed gains in total oil production over 20 years of operation up to 7.2% with respect to the base strategy currently applied. On average, results show an increase of near 4% in oil production, with concomitant reduction in total water production and in overall water injection.To guarantee that the gains forecast by our study are feasible, a pilot test in the actual field has been implemented to verify the consistency between modeling and reality (data observation). We have chosen an area in the field to proceed with the optimal injection control pilot, aiming to check the quality of the uncertain models in comparison to the observed data in practice. The pilot area has been selected on the basis of aspects related to geological description, connectivity expected in the reservoir, and operational constraints. The results of 8 months of the pilot show clear coherence between models and reality that is well within the uncertainty range accepted at the reservoir of interest.To the best of our knowledge, it is the first time that an offshore field is actually operated on the basis of a set of controls obtained through an assisted ORM procedure, although it was performed at a pilot scale. Results suggest robust benefits under reservoir-uncertainties consideration, and large-scale application will take place soon, but that is outside the scope of this work. The pilot provided more confidence in field applications, leading to a broader perspective for full-field implementations.

show abstract

Practical Closed-Loop Reservoir Management Using Deep Reinforcement Learning

Nasir

Durlofsky

2023

SPE Journal

View full text Add to dashboard Cite

Summary Traditional closed-loop reservoir management (CLRM) entails the repeated application of history matching (based on newly observed data) followed by optimization of well settings. Existing treatments can provide well settings that fluctuate substantially between control steps, which may not be acceptable in practice. Another concern is that the project life (i.e., the time frame for the optimization) is often specified somewhat arbitrarily. In this work, we incorporate treatments for these important issues into a recently developed control-policy-based CLRM framework. This framework uses deep reinforcement learning (DRL) to train control policies that directly map observed well data to optimal well settings. Here, we introduce a procedure in which we train control policies, using DRL, to find optimal well bottomhole pressures (BHPs) for prescribed relative changes between control steps, with the project life also treated as an optimization variable. The goal of the optimizations is to maximize net present value (NPV), with project life determined such that a minimum acceptable rate of return (MARR) is achieved. We apply the framework to waterflooding cases involving 2D and 3D geological models. In the 3D case, realizations are drawn from multiple geological scenarios. Solutions from the control-policy approach are shown to be comparable, in terms of NPV, to those from deterministic realization-by-realization optimization and clearly superior to results from robust optimization over prior models. These observations hold for a range of specified MARR and relative-change values. The optimal well settings provided by the control policy display gradual ramping, consistent with operational requirements.

show abstract

Refined ensemble-based waterflooding optimization subject to field-wide constraints

Cited by 4 publications

References 38 publications

Ensemble-based method with combined fractional flow model for waterflooding optimization

Ensemble-based method with combined fractional flow model for waterflooding optimization

Pioneer Field Pilot of Optimal Reservoir Management in Campos Basin

Practical Closed-Loop Reservoir Management Using Deep Reinforcement Learning

Contact Info

Product

Resources

About