Production from unconventional formations,such as shales,has significantly increased in recent years by stimulating large portions of a reservoir through the application of horizontal drilling and hydraulic fracturing...Production from unconventional formations,such as shales,has significantly increased in recent years by stimulating large portions of a reservoir through the application of horizontal drilling and hydraulic fracturing.Although oil shales are heavily dependent on oil prices,production forecasts remain positive in the North-American region.Due to the complexity of hydraulically fractured tight formations,reservoir numerical simulation has become the standard tool to assess and predict production performance from these unconventional resources.Many of these unconventional fields are immense,consisting of multistage and multiwell projects,which results in impractical simulation run times.Hence,simplification of large-scale simulation models is now common both in the industry and academia.Typical simplified models such as the“single fracture”approach do not often capture the physics of large-scale projects which results in inaccurate results.In this paper we present a simple,yet rigorous workflow that generates simplified representative models in order to achieve low simulation run times while capturing physical phenomena which is fundamental for accurate calculations.The proposed workflow is based on consideration of representative portions of a large-scale model followed by postprocess scaling to obtain desired full model results.The simplified models that result from the application of the proposed workflow for a single well and a multiwell case are compared to full-scale models and the“single fracture”model.Comparison of fluid rates and cumulative production show that accurate results are possible for simplified models if all important components for a particular case are taken into account.Finally,application of the workflow is shown for a heterogeneous field case where prediction studies can be carried out.展开更多
Machine learning and data-driven methods have started to transform the study of surfaces and interfaces.Here,we review how data-driven methods and machine learning approaches complement simulation workflows and contri...Machine learning and data-driven methods have started to transform the study of surfaces and interfaces.Here,we review how data-driven methods and machine learning approaches complement simulation workflows and contribute towards tackling grand challenges in computational surface science from 2D materials to interface engineering and electrocatalysis.Challenges remain,including the scarcity of large datasets and the need for more electronic structure methods for interfaces.展开更多
文摘Production from unconventional formations,such as shales,has significantly increased in recent years by stimulating large portions of a reservoir through the application of horizontal drilling and hydraulic fracturing.Although oil shales are heavily dependent on oil prices,production forecasts remain positive in the North-American region.Due to the complexity of hydraulically fractured tight formations,reservoir numerical simulation has become the standard tool to assess and predict production performance from these unconventional resources.Many of these unconventional fields are immense,consisting of multistage and multiwell projects,which results in impractical simulation run times.Hence,simplification of large-scale simulation models is now common both in the industry and academia.Typical simplified models such as the“single fracture”approach do not often capture the physics of large-scale projects which results in inaccurate results.In this paper we present a simple,yet rigorous workflow that generates simplified representative models in order to achieve low simulation run times while capturing physical phenomena which is fundamental for accurate calculations.The proposed workflow is based on consideration of representative portions of a large-scale model followed by postprocess scaling to obtain desired full model results.The simplified models that result from the application of the proposed workflow for a single well and a multiwell case are compared to full-scale models and the“single fracture”model.Comparison of fluid rates and cumulative production show that accurate results are possible for simplified models if all important components for a particular case are taken into account.Finally,application of the workflow is shown for a heterogeneous field case where prediction studies can be carried out.
基金support from the UKRI Future Leaders Fellowship program(MR/S016023/1,MR/X023109/1)a UKRI Horizon grant(ERC StG,EP/X014088/1)+1 种基金a Leverhulme Trust research project grant(RPG-2019-078)a UKRI Horizon grant(MSCA,EP/Y024923/1),a UFO(Unkonventionelle Forschung)postdoctoral fellowship grant by the Austrian province of Styria.
文摘Machine learning and data-driven methods have started to transform the study of surfaces and interfaces.Here,we review how data-driven methods and machine learning approaches complement simulation workflows and contribute towards tackling grand challenges in computational surface science from 2D materials to interface engineering and electrocatalysis.Challenges remain,including the scarcity of large datasets and the need for more electronic structure methods for interfaces.
