Multiscale Description and Upscaling of Fluid Flow in Subsurface Reservoirs

Nœtinger, B.; Zargar, Ghasem

doi:10.2516/ogst:2004010

Cited by 18 publications

(7 citation statements)

References 60 publications

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“…Wettability state of the porous medium would, in turn, strongly affect how the water/oil displacement occurs in an oil reservoir. Similarly, the reservoir heterogeneity takes a large range of spatial scales and has an imperative role in fluid flow channeling Noetinger and Zargar, 2004). Accordingly, the characterization of parameters such as heterogeneity effect (Morton-Thompson and Woods, 1993), site of injection and production wells (Alvarado and Manrique, 2010;Henderson et al, 2015;Monteagudo and Firoozabadi, 2007;Morton-Thompson and Woods, 1993) that lead to the control of the injection process could potentially be decisive.…”

Section: Introductionmentioning

confidence: 99%

Experimental and CFD studies on determination of injection and production wells location considering reservoir heterogeneity and capillary number

Ahmadi

Ghandi

Riazi

et al. 2019

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

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The in-depth knowledge of reservoir heterogeneity is imperative for identifying the location of production and injection wells. The present study aimed at experimentally investigating the process of water flooding in the viscous oil-saturated glass micromodels, which contain layers with different permeability where the fractures were placed in different locations. Computational Fluid Dynamics (CFD) simulations of flooding were also conducted to study the impact of different water flow rates and wettability states. The results showed that the fractures, which have a deviation with the trend of maximum pressure gradient line, would widen the water path and vice versa. The performance of injection wells would increase the recovery factor by 18% if these would be located in the zones with high permeability for low flow rates of water. With changes in wettability state from water to oil wet conditions, the oil production will increase by 11%. Computational Fluid Dynamics results also indicated that an increase in the capillary number from 0.8 × 10−6 to 1.6 × 10−5, would cause the recovery factor to decrease as much as 14.34% while further increase from 1.6 × 10−5 to 2.24 × 10−5, the oil production will increase by 9.5%. Comparison between the obtained oil recoveries indicates that the maximum oil recoveries will happen when the injector well is located in the zone where ascending permeability, capillary number greater than 4.81 × 10−6 and also fracture with the most deviation with pressure gradient line (i.e. angular pattern) are gathered in an area between the injection and production wells.

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Section: Introductionmentioning

confidence: 99%

Experimental and CFD studies on determination of injection and production wells location considering reservoir heterogeneity and capillary number

Ahmadi

Ghandi

Riazi

et al. 2019

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

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“…Flow is assumed to be linear and steady-state. This method is described in several papers [28][29][30][31]. It was shown to provide satisfactory results provided the coarse cell size is very large compared with the correlation length of permeability [32].…”

Section: The Numerical Local Methodsmentioning

confidence: 99%

About the Use of Quality Indicators to Reduce Information Loss When Performing Upscaling

Preux

2014

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

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-Hydrocarbon reservoirs are characterized by the spatial distributions of petrophysical properties. These spatial characteristics are usually derived from well data and seismic information. To study a reservoir, the engineers build a fine geological model, also called a geostatistical model, to represent the field. The purpose is to capture as well as possible the peculiarities and heterogeneity of the true reservoir. At this stage, performing a flow simulation with such detailed geological models is just too time-demanding. Therefore, a possibility consists of upscaling the geological model to an upscaled mesh, thus resulting in a coarse reservoir model for which fluid flow can be numerically simulated in a reasonable amount of time. The coarse grid blocks of this reservoir model are attributed equivalent petrophysical properties related to the properties populating the fine grid blocks. These properties are upscaled, and so they do not capture all of the details of the fine model. In this paper, we investigate the potential of various numerical and easy to compute criteria, which help evaluate the information loss due to the upscaling process. Our final purpose is to provide and access the reliability of quality indicators, which make it possible to evaluate the quality of the upscaled reservoir model. The potential of this systematic and integrated study is illustrated with two types of numerical experiments based upon the SPE10 case. First, we apply different upscaling methods to determine coarse reservoir models. Quality indicators are computed for each of them so that we identify the most suitable upscaling methods. Then, the upscaled models are input to flow simulators to check the accuracy of our quality estimations. Second, we also investigate the influence of coarsening and try to determine from the computed quality indicators the coarse cell size above which too much information is lost.Re´sume´-À propos de l'utilisation d'indicateurs de qualite´afin de re´duire la perte d'information lors d'un upscaling -Les re´servoirs pe´troliers sont caracte´rise´s par une distribution spatiale de proprie´te´s pe´trophysiques. Ces caracte´ristiques spatiales sont ge´ne´ralement de´rive´es de donne´es de puits et sismiques. Les inge´nieurs construisent alors un mode`le ge´ologique fin, aussi appeleḿ ode`le ge´ostatistique, pour repre´senter le champ. Le but est de capturer aussi bien que possible les particularite´s et l'he´te´roge´ne´ite´du re´servoir. À ce stade, re´aliser une simulation d'e´coulement avec ces mode`les ge´ologiques aussi de´taille´s est trop couˆteux en temps de calcul. Par conse´quent, une possibilite´consiste a`« upscaler » le mode`le ge´ologique sur une grille grossie`re, afin d'obtenir un mode`le de re´servoir grossier pour lequel l'e´coulement peut eˆtre simule´nume´riquement dans un laps de temps raisonnable. On attribue alors des proprie´te´s pe´trophysiques e´quivalentes aux cellules de la grille grossie`re. Meˆme si ces donne´es e´quivalentes sont obtenues a`partir des proprie´t...

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“…In this way, many goals will be pursued: data integration, reduce uncertainty as well as reduce computational costs. Noetinger and Zargar (2004) have studied the importance of having a multiscale qualification of fluid flow simulation from the perspective of a reservoir engineer in a comprehensive review. They have considered all relevant problems from the geological model to the simulation model [9].…”

Section: Introductionmentioning

confidence: 99%

“…Noetinger and Zargar (2004) have studied the importance of having a multiscale qualification of fluid flow simulation from the perspective of a reservoir engineer in a comprehensive review. They have considered all relevant problems from the geological model to the simulation model [9]. Considering the importance of identifying discontinuities and heterogeneity in reservoirs, traditional upscaling methods do not have the required efficiency in complex and heterogeneous reservoirs, and it is necessary for upscaling based on the variability.…”

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

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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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Multiscale Description and Upscaling of Fluid Flow in Subsurface Reservoirs

Cited by 18 publications

References 60 publications

Experimental and CFD studies on determination of injection and production wells location considering reservoir heterogeneity and capillary number

Experimental and CFD studies on determination of injection and production wells location considering reservoir heterogeneity and capillary number

About the Use of Quality Indicators to Reduce Information Loss When Performing Upscaling

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

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