Olympus challenge—standardized workflow design for field development plan optimization under uncertainty

Schulze-Riegert, Ralf; Nwakile, Michael; Skripkin, Sergey; Whymark, Michelle; Baffoe, James; Geissenhoener, D.; Anton, Adrian; Meulengracht, Chresten; Ng, K. Jin

doi:10.1007/s10596-019-09905-9

Cited by 6 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Four rounds of 𝒩 r simulations plus an extra final fifth round of simulations for the forecast (𝒩 a = 5). The overall process might easily involve thousands of simulations [28].…”

Section: Problem Descriptionmentioning

confidence: 99%

“…While a single simulation of OLYMPUS can execute in less than 5 min (with the environment described in Section 6.3), it is interesting to note that to solve some of the problems posed by the challenge could require a computational effort of more than 1,000 simulations, as reported in [28]. Summing all the solution strategies that these authors applied to the challenge TNO proposed, it required them 18,900 simulations of OLYMPUS models.…”

Section: Reservoir Models For Validationmentioning

confidence: 99%

See 1 more Smart Citation

TunaOil: A Tuning Algorithm Strategy for Reservoir Simulation Workloads

Portella,

Prats,

Rodrigues

et al. 2022

Preprint

View full text Add to dashboard Cite

Reservoir simulations for petroleum fields and seismic imaging are known as the most demanding workloads for highperformance computing (HPC) in the oil and gas (O&G) industry. The optimization of the simulator numerical parameters plays a vital role as it could save considerable computational efforts. State-of-the-art optimization techniques are based on running numerous simulations, specific for that purpose, to find good parameter candidates. However, using such an approach is highly costly in terms of time and computing resources. This work presents TunaOil, a new methodology to enhance the search for optimal numerical parameters of reservoir flow simulations using a performance model. In the O&G industry, it is common to use ensembles of models in different workflows to reduce the uncertainty associated with forecasting O&G production. We leverage the runs of those ensembles in such workflows to extract information from each simulation and optimize the numerical parameters in their subsequent runs.To validate the methodology, we implemented it in a history matching (HM) process that uses a Kalman filter algorithm to adjust an ensemble of reservoir models to match the observed data from the real field. We mine past execution logs from many simulations with different numerical configurations and build a machine learning model based on extracted features from the data. These features include properties of the reservoir models themselves, such as the number of active cells, to statistics of the simulation's behavior, such as the number of iterations of the linear solver. A sampling technique is used to query the oracle to find the numerical parameters that can reduce the elapsed time without significantly impacting the quality of the results. Our experiments show that the predictions can improve the overall HM workflow runtime on average by 31%.

show abstract

“…Four rounds of 𝒩 r simulations plus an extra final fifth round of simulations for the forecast (𝒩 a = 5). The overall process might easily involve thousands of simulations [28].…”

Section: Problem Descriptionmentioning

confidence: 99%

Section: Reservoir Models For Validationmentioning

confidence: 99%

TunaOil: A Tuning Algorithm Strategy for Reservoir Simulation Workloads

Portella,

Prats,

Rodrigues

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

Advancements in field development planning through mathematical analysis for reserves estimation, casing design, accidental events and carbon dioxide storage

Giakoumi,

Konstantinou,

Papadimitriou

et al. 2024

Gas Science and Engineering

View full text Add to dashboard Cite

Optimizing Well Economic Limits Using Ensemble-Based Optimization on Olympus Field

Chang

Nffivdal²,

Lorentzen³

2020

Day 2 Tue, November 03, 2020

View full text Add to dashboard Cite

The Olympus field is an optimization case prepared by TNO to test different optimization methods and workflows for field development. The authors have joined the Olympus optimization challenges by solving various optimization problems for field development. Here, we consider the well control problem from a different perspective by using well economic limits as parameters to optimize the objective function - the Net Present Value (NPV). The Ensemble-based Optimization (EnOpt) method applied to this problem has been shown to be simple and efficient to handle geological uncertainty in many literatures. In this work, the performance of EnOpt is illustrated on the Olympus benchmark case. The economic limit determines when a well will be shutin with respect to the water-cut, which directly affects the calculation of NPV. After optimizing the economic limits, we see that the producers are not necessarily following the fixed value of economic limits to give the best production benefit in terms of NPV. Given a well control scheme in the field, the NPV can be further improved by optimizing the economic limits of each producer. Optimizing the economic limits is a novel aspect for Olympus case, as this was not within the original scope of the Olympus optimization challenge. This work also shows that the NPV is further improved compared to the values obtained by the authors during the Olympus optimization challenge by utilizing optimized well economic limits.

show abstract

Olympus challenge—standardized workflow design for field development plan optimization under uncertainty

Cited by 6 publications

References 12 publications

TunaOil: A Tuning Algorithm Strategy for Reservoir Simulation Workloads

TunaOil: A Tuning Algorithm Strategy for Reservoir Simulation Workloads

Advancements in field development planning through mathematical analysis for reserves estimation, casing design, accidental events and carbon dioxide storage

Optimizing Well Economic Limits Using Ensemble-Based Optimization on Olympus Field

Contact Info

Product

Resources

About