History matching and production optimization under uncertainties – Application of closed-loop reservoir management

Silva, Vinicius L. S.; Emerick, Alexandre A.; Couto, Paulo; Alves, José L. D.

doi:10.1016/j.petrol.2017.07.037

Cited by 43 publications

(20 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In fact, they observed that simulated responses did not capture the reference model, which represented the real reservoir. Silva et al (2017) also presented a closed-loop process for the UNISIM-I-D benchmark with ES-MDA. The main difference from the work of Morosov and Schiozer (2016) was the use of distance-dependent localization.…”

Section: Es-mda and The Localization Techniquementioning

confidence: 99%

“…Avansi and Schiozer, (2015) selected the range of the uncertain parameters based on the knowledge of the Namorado field while avoiding values that are not compatible with the case. Finally, because UNISIM-I-H is a benchmark case and different authors have been using it (Morosov and Schiozer 2016;Silva et al 2017;Soares et al 2018), it is important to keep some basic features of the case in order to validate and compare different methodologies.…”

Section: Application: Unisim-i-hmentioning

confidence: 99%

See 1 more Smart Citation

A novel localization scheme for scalar uncertainties in ensemble-based data assimilation methods

Soares

Maschio

Schiozer

2019

J Petrol Explor Prod Technol

View full text Add to dashboard Cite

History matching, also known as data assimilation, is an inverse problem with multiple solutions responsible for generating more reliable models for use in decision-making processes. An iterative ensemble-based method (Ensemble Smoother with Multiple Data Assimilation-ES-MDA) has been used to improve the solution of history-matching processes with a technique called distance-dependent localization. In conjunction, ES-MDA and localization can obtain consistent petrophysical images (permeability and porosity). However, the distance-dependent localization technique is not used to update scalar uncertainties, such as relative permeability; therefore, the variability for these properties is excessively reduced, potentially excluding plausible answers. This work presents three approaches to update scalar parameters while increasing the final variability of these uncertainties to better scan the search space. The three approaches that were developed and compared using a benchmark case are: binary correlation coefficient (BCC), based on correlation calculated by ES-MDA through cross-covariance matrix C f MD (BCC-C MD); BCC, based on a correlation coefficient between the objective functions and scalar uncertainties (R) (BCC-R); and full correlation coefficient (FCC). We used the work of Soares et al. (J Pet Sci Eng 169:110-125, 2018) as a base case to compare the approaches because although it showed good matches with geologically consistent petrophysical images, it generated an excessive reduction in the scalar parameters. BCC-C MD presented similar results to the base case, excessively reducing the variability of the scalar uncertainties. BCC-R increased the variability in the scalar parameters, especially for BCC with a higher threshold value. Finally, FCC found many more potential answers in the search space without impairing data matches and production forecast quality.

show abstract

Section: Es-mda and The Localization Techniquementioning

confidence: 99%

Section: Application: Unisim-i-hmentioning

confidence: 99%

A novel localization scheme for scalar uncertainties in ensemble-based data assimilation methods

Soares

Maschio

Schiozer

2019

J Petrol Explor Prod Technol

View full text Add to dashboard Cite

show abstract

“…Even though using the full set of models leads to the maximum representativeness in terms of the known geological uncertainty, techniques for scenario reduction can be considered to make the computational cost more tractable. In that sense, we applied a scenario reduction approach based on minimizing the total weight of the discarded models, following the ideas implemented by [1,16,23,24,28].…”

Section: Scenario Reductionmentioning

confidence: 99%

“…Regarding the computational cost reduction, two alternatives are usually employed. The reduction of the reservoir simulation cost by means of the construction of reduced order models [5,7,21], and scenario reduction as a strategy to select representative scenarios from the full ensemble of geological realizations [1,3,19,27,28].…”

Section: Introductionmentioning

confidence: 99%

“…Even though one must note that the sequential approach could be done iteratively until convergence of the objective function for both well controls and optimal positions, there is no theoretical work that show that this sequential approach is, indeed, convergent [26]. Furthermore, we also apply a scenario reduction technique [28] to alleviate the computational cost of the optimization process.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Stochastic optimization strategies applied to the OLYMPUS benchmark

et al. 2019

View full text Add to dashboard Cite

In this work, we discuss the application of stochastic optimization approaches to the OLYMPUS case, a benchmark challenge which seeks the evaluation of different techniques applied to well control and field development optimization. For that matter, three exercises have been proposed, namely, (i) well control optimization; (ii) field development optimization; and (iii) joint optimization. All applications were performed considering the so-called OLYMPUS case, a synthetic reservoir model with geological uncertainty provided by TNO (Fonseca 2018). Firstly, in the well control exercise, we successfully applied an ensemble-based approximate gradient method in a robust optimization formulation. Secondly, we solve the field development exercise using a genetic algorithm framework designed with special features for the problem of interest. Finally, in order to evaluate further gains, a sequential optimization approach was employed, in which we run one more well control optimization based on the optimal well locations. Even though we utilize relatively well-known techniques in our studies, we describe the necessary adaptations to the algorithms that enable their successful applications to real-life scenarios. Significant gains in the expected net present value are obtained: in exercise (i) a gain of 7% with respect to reactive control; for exercise (ii) a gain of 660% with respect to a initial well placement based on an engineering approach; and for (iii) an extra gain of 3% due to an additional well control optimization after the well placement optimization. All these gains are obtained with an affordable computational cost via the extensive utilization of high-performance computing (HPC) infrastructure. We also apply a scenario reduction technique to exercise (i), with similar gains obtained in the full ensemble optimization, however, with substantially inferior computational cost. In conclusion, we demonstrate how the state-of-the-art optimization technology available in the model-based reservoir management literature can be successfully applied to field development optimization via the conscious utilization of HPC facilities. Keywords Field development optimization • Well control optimization • Joint optimization • Ensemble-based optimization • Genetic algorithm • OLYMPUS challenge • Uncertainty quantification Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

show abstract

Analysing the role of caprock morphology on history matching of Sleipner CO₂ plume using an optimisation method

Ahmadinia

Shariatipour

2020

Greenhouse Gases

View full text Add to dashboard Cite

Geological carbon storage is a promising solution to reduce the CO2 concentration in the atmosphere to ameliorate the effects of global warming from the greenhouse effect. Among feasible storage options, deep saline aquifers are believed to have the largest storage capacity for the gas collected from industrial processes. The first CO2 storage project at a commercial scale in a saline aquifer is in the Sleipner field of the Utsira storage formation in Norway. The long ongoing storage operation in the Sleipner field has been the subject of several past studies attempting to recreate the observed injected CO2 plume migration behaviour. History matching is a method to adjust the input parameters of the model in a way to minimise the mismatch between the simulated and the actual production data in reservoir engineering and applicable to carbon sequestration. Typical parameters adjusted in history matching are porosity, absolute and relative permeability data. In this study, we used an adjoint‐based optimisation tool and showed the importance of caprock morphology in finding an accurate plume match. Using a set of synthetic models, we initially minimised the mismatch between the observed and simulated CO2 plume outline by modifying the caprock topographical details. After testing the optimisation tool on the synthetic models, we applied the methodology to the Sleipner benchmark 2019 model and improved the plume match by locally adjusting caprock elevation within seismic detection limits. We subsequently improved the match by calibrating porosity, permeability, CO2 density and injection rate together in an experiment in which we calibrated all the parameters, including the caprock morphology, to find a better match. The results showed an improvement of around 8% (compared with the original model) in the plume match resulting from an average absolute elevation change of 3.23 m in the model while keeping the other parameters constant. Calibrating the porosity, permeability, CO2 density and injection rate resulted in a 5% improvement in the match, and once caprock morphology was included in the optimisation process, the match improvement increased by 16%. We changed the caprock elevation within a range lower than the seismic detection limit, and results showed that even a few metres variations in the elevation have significant impacts on the plume migration and trapping mechanism in the Sleipner model. The method presented in this work results in a better match than the original seismic data for the Sleipner model. © 2020 The Authors. Greenhouse Gases: Science and Technology published by Society of Chemical Industry and John Wiley & Sons, Ltd.

show abstract

History matching and production optimization under uncertainties – Application of closed-loop reservoir management

Cited by 43 publications

References 47 publications

A novel localization scheme for scalar uncertainties in ensemble-based data assimilation methods

A novel localization scheme for scalar uncertainties in ensemble-based data assimilation methods

Stochastic optimization strategies applied to the OLYMPUS benchmark

Analysing the role of caprock morphology on history matching of Sleipner CO₂ plume using an optimisation method

Contact Info

Product

Resources

About

History matching and production optimization under uncertainties – Application of closed-loop reservoir management

Cited by 43 publications

References 47 publications

A novel localization scheme for scalar uncertainties in ensemble-based data assimilation methods

A novel localization scheme for scalar uncertainties in ensemble-based data assimilation methods

Stochastic optimization strategies applied to the OLYMPUS benchmark

Analysing the role of caprock morphology on history matching of Sleipner CO2 plume using an optimisation method

Contact Info

Product

Resources

About

Analysing the role of caprock morphology on history matching of Sleipner CO₂ plume using an optimisation method