New upscaling technique for compositional reservoir simulations of miscible gas injection

Rios, Victor de Souza; Santos, Luiz Otávio Schmall dos; Quadros, Franco Borges; Schiozer, Denis José

doi:10.1016/j.petrol.2018.12.061

Cited by 14 publications

(4 citation statements)

References 8 publications

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“…discriminant analysis, goodness-of-fit testing, hazard rate estimation, intensity function estimation and regression, in which the main idea is to estimate the density function at a given point using neighboring observations [26][27][28]. A typical form for the multivariable kernel density function is shown in equation (1).…”

Section: Research Methodology 21 Bandwidth Methodsmentioning

confidence: 99%

“…Upscaling of the geological fine model is required because computational costs can make it unpractical to perform compositional simulations in fine-scale models. Therefore, upscaling of fine-scale geological models is important and the use of coarse-scale models in reservoir simulation is necessary to reduce computational time [1]. To develop a reduced-order model from a fine scale model, first, we must create a grid model with reduced spatial resolution, and then, capture reservoir features to a new coarse scale model using a suitable upscaling method.…”

Section: Introductionmentioning

confidence: 99%

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Hierarchical simultaneous upscaling of porosity and permeability features using the bandwidth of kernel function and wavelet transformation in two dimensions: Application to the SPE-10 model

Azad

Kamkar‐Rouhani

Tokhmechi

et al. 2021

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

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In this paper, two methods of kernel bandwidth and wavelet transform are used for simultaneous upscaling of two features of hydrocarbon reservoir. In the bandwidth method, the criterion for upscaling is the cell variability, and by calculating the optimal bandwidth and determining the distance matrix, the upscaling process is performed in a completely non-uniform and unregularly manner. In areas with extreme variability, the bandwidth is considered small enough to maintain the fine scale characteristics of model. Conversely in homogenous areas, with the choice of large bandwidth, the maximum rate of upscaling will occur. The bandwidth upscaling algorithm is an iterative and hierarchical algorithm. The bandwidth method, unlike conventional scale-up methods, focuses on how to upgrid cells and, by determining the optimal averaging window, we will have the least loss information for the fine scale model. Upscaling is a pre-processing to building a simulator model with lower cell number, and thus, reducing volume and computational cost, while maintaining and retaining the basic information of the fine model. Due to the various variability of the reservoir features, the attribute upscaling pattern differs, and in order to show the variability of two features in the upscaling model simultaneously, it is suggested in this paper to upscale two features simultaneously. For simultaneous upscaling, we applied two different approaches; minimum and maximum bandwidth. Moreover, wavelet transformation is applied to upscaling the model. Then, as a result, the variance of the scale-up models based on wavelet is about one-third of the variance of the bandwidth method. Simulation results show that the bandwidth method is a good approach for upscaling the heterogeneous reservoirs.

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Section: Research Methodology 21 Bandwidth Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hierarchical simultaneous upscaling of porosity and permeability features using the bandwidth of kernel function and wavelet transformation in two dimensions: Application to the SPE-10 model

Azad

Kamkar‐Rouhani

Tokhmechi

et al. 2021

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

View full text Add to dashboard Cite

show abstract

“…In upscaling, as another way to simplify the physics, the equivalent petrophysical properties at a coarser scale are calculated [133]. Upscaling has been implemented for a wide range of objectives in reservoir modeling [134][135][136][137].…”

Section: Multi-fidelity Models (Mfm)mentioning

confidence: 99%

A Review of Proxy Modeling Highlighting Applications for Reservoir Engineering

2022

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Numerical models can be used for many purposes in oil and gas engineering, such as production optimization and forecasting, uncertainty analysis, history matching, and risk assessment. However, subsurface problems are complex and non-linear, and making reliable decisions in reservoir management requires substantial computational effort. Proxy models have gained much attention in recent years. They are advanced non-linear interpolation tables that can approximate complex models and alleviate computational effort. Proxy models are constructed by running high-fidelity models to gather the necessary data to create the proxy model. Once constructed, they can be a great choice for different tasks such as uncertainty analysis, optimization, forecasting, etc. The application of proxy modeling in oil and gas has had an increasing trend in recent years, and there is no consensus rule on the correct choice of proxy model. As a result, it is crucial to better understand the advantages and disadvantages of various proxy models. The existing work in the literature does not comprehensively cover all proxy model types, and there is a considerable requirement for fulfilling the existing gaps in summarizing the classification techniques with their applications. We propose a novel categorization method covering all proxy model types. This review paper provides a more comprehensive guideline on comparing and developing a proxy model compared to the existing literature. Furthermore, we point out the advantages of smart proxy models (SPM) compared to traditional proxy models (TPM) and suggest how we may further improve SPM accuracy where the literature is limited. This review paper first introduces proxy models and shows how they are classified in the literature. Then, it explains that the current classifications cannot cover all types of proxy models and proposes a novel categorization based on various development strategies. This new categorization includes four groups multi-fidelity models (MFM), reduced-order models (ROM), TPM, and SPM. MFMs are constructed based on simplifying physics assumptions (e.g., coarser discretization), and ROMs are based on dimensional reduction (i.e., neglecting irrelevant parameters). Developing these two models requires an in-depth knowledge of the problem. In contrast, TPMs and novel SPMs require less effort. In other words, they do not solve the complex underlying mathematical equations of the problem; instead, they decouple the mathematical equations into a numeric dataset and train statistical/AI-driven models on the dataset. Nevertheless, SPMs implement feature engineering techniques (i.e., generating new parameters) for its development and can capture the complexities within the reservoir, such as the constraints and characteristics of the grids. The newly introduced parameters can help find the hidden patterns within the parameters, which eventually increase the accuracy of SPMs compared to the TPMs. This review highlights the superiority of SPM over traditional statistical/AI-based proxy models. Finally, the application of various proxy models in the oil and gas industry, especially in subsurface modeling with a set of real examples, is presented. The introduced guideline in this review aids the researchers in obtaining valuable information on the current state of PM problems in the oil and gas industry.

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“…The incorrect splitting of the phases due to numerical diffusion, which causes the components to flow in the wrong phase, is also common. Pseudo functions can be applied to compensate for the numerical effects and the presence of small-scale flow phenomena such as molecular diffusion, viscous fingering, gravity tonguing, and so on (Darman et al, 2002;Rios et al, 2019). The compositional upscaling of miscible floods has received noteworthy attention by many authors.…”

Section: Introductionmentioning

confidence: 99%

Assisted upscaling of miscible CO¬2-enhanced oil recovery floods using an artificial-neural-network-based optimisation algorithm

Ogbeiwi¹,

Stephen²

2023

Preprint

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The fine-scale compositional simulations required to accurately model miscible CO2 flooding are unrealistic and highly computationally expensive, and upscaling procedures are required to approximate the behaviour of these fine-scale grids on more realistic coarse-scale models. These procedures include the pseudoisation of relative permeabilities which ensures the matching of large-scale effects such as the volumetric fluxes of the phases and the use of transport coefficients to better represent small-scale interactions such as the time-dependent flux of the components within the hydrocarbon phases (molecular diffusion). Most times, a mismatch between the phase fluxes of the integrated fine-scale and that of the coarse-scale is observed. The accuracy of the upscaling results can be improved by tuning some of the derived upscaled coarse-scale quantities such as the alpha factors, absolute permeability or the relative permeabilities endpoints. This process is generally computationally expensive and can be treated as a reservoir history matching problem. In this study, we present a framework to represent the dynamics of small-scale molecular diffusion, and macro-scale heterogeneity-induced channelling associated with miscible CO2 displacements on upscaled coarser grid reservoir models. The approach applied was based on the pseudoisation of relative permeability and transport coefficients and was applied in two Society of Petroleum Engineers (SPE) benchmark reservoir models. Our results showed that the use of efficiently tuned transport coefficients led to better results so that the derived pseudo-relative permeability functions can be neglected. To reduce the associated computational expense, we proposed a novel methodology for upscaling miscible floods where a neural-network-based genetic-algorithm assisted upscaling procedure was applied. The optimisation algorithm was applied to reduce the error between the predictions of the upscaled models and a data-driven approximation model significantly reduced the computational expense associated with the assisted tuning procedure. In summary, the framework presented in this study adequately represented the small- and large-scale behaviour associated with the miscible displacements on upscaled coarse-scale reservoir models.

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New upscaling technique for compositional reservoir simulations of miscible gas injection

Cited by 14 publications

References 8 publications

Hierarchical simultaneous upscaling of porosity and permeability features using the bandwidth of kernel function and wavelet transformation in two dimensions: Application to the SPE-10 model

Hierarchical simultaneous upscaling of porosity and permeability features using the bandwidth of kernel function and wavelet transformation in two dimensions: Application to the SPE-10 model

A Review of Proxy Modeling Highlighting Applications for Reservoir Engineering

Assisted upscaling of miscible CO¬2-enhanced oil recovery floods using an artificial-neural-network-based optimisation algorithm

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