Scale Up of Pore-Network Models into Reservoir Scale: Optimization of Inflow Control Devices Placement

SPE Journal ◽  
2021 ◽  
pp. 1-18
Author(s):  
Farzad Bashtani ◽  
Mazda Irani ◽  
Apostolos Kantzas
Keyword(s):  
Pore Structure ◽  
Capillary Pressure ◽  
Relative Permeability ◽  
Scale Up ◽  
Pore Network ◽  
Flow In Porous Media ◽  
Well Completion ◽  
Control Devices ◽  
Relative Permeability Curves ◽  
Capillary Pressure Curves

Summary Improvements to more advanced tools, such as inflow control devices (ICDs), create a high drawdown regime close to wellbores. Gas liberation within the formation occurs when the drawdown pressure is reduced below the bubblepoint pressure, which in turn reduces oil mobility by reducing its relative permeability, and potentially reducing oil flow. The key input in any reservoir modeling to compare the competition between gas and liquid flow toward ICDs is the relative permeability of different phases. Pore-network modeling (PNM) has been used to compute the relative permeability curves of oil, gas, and water based on the pore structure of the formation. In this paper, we explain the variability of pore structure on its relative permeability, and for a similar formation and identical permeability, we explain how other factors, such as connectivity and throat radius distribution, can vary the characteristic curves. By using a boundary element method, we also incorporate the expected relative permeability and capillary pressure curves into the modeling. The results show that such variability in the pore network has a less than 10% impact on production gas rates, but its effect on oil production can be significant. Another important finding of such modeling is that providing the PNM-created relative permeabilities may provide totally different direction on setting the operational constraints. For example, in the case studied in this paper, PNM-created relative permeability curves suggest that a reduction of flowing bottomhole pressure (FBHP) increases the oil rate, but for the case modeled with a Corey correlation, changes in FBHP will not create any uplift. The results of such work show the importance of PNM in well completion design and probabilistic analysis of the performance, and can be extended based on different factors of the reservoir in future research. Although PNM has been widely used to study the multiphase flow in porous media in academia, the application of such modeling in reservoir and production engineering is quite narrow. In this study, we develop a framework that shows the general user the importance of PNM simulation and its implementation in day-to-day modeling. With this approach, the PNM can be used not just to provide relative permeability or capillary pressure curves on a core or pore- scale, but to preform simulations at the wellbore or reservoir scale as well to optimize the current completions.

scholarly journals Uncertainty span for relative permeability and capillary pressure by varying wettability and spatial flow directions utilizing pore scale modelling

2020 ◽  
Vol 146 ◽  
pp. 01002
Author(s):  
Thomas Ramstad ◽  
Anders Kristoffersen ◽  
Einar Ebeltoft
Keyword(s):  
Continental Shelf ◽  
Pore Structure ◽  
Capillary Pressure ◽  
Relative Permeability ◽  
Phase Flow ◽  
Pore Scale ◽  
Full Field ◽  
Scale Modelling ◽  
Relative Permeability Curves ◽  
Norwegian Continental Shelf

Relative permeability and capillary pressure are key properties within special core analysis and provide crucial information for full field simulation models. These properties are traditionally obtained by multi-phase flow experiments, however pore scale modelling has during the last decade shown to add significant information as well as being less time-consuming to obtain. Pore scale modelling has been performed by using the lattice-Boltzmann method directly on the digital rock models obtained by high resolution micro-CT images on end-trims available when plugs are prepared for traditional SCAL-experiments. These digital rock models map the pore-structure and are used for direct simulations of two-phase flow to relative permeability curves. Various types of wettability conditions are introduced by a wettability map that opens for local variations of wettability on the pore space at the pore level. Focus have been to distribute realistic wettabilities representative for the Norwegian Continental Shelf which is experiencing weakly-wetting conditions and no strong preference neither to water nor oil. Spanning a realistic wettability-map and enabling flow in three directions, a large amount of relative permeability curves is obtained. The resulting relative permeabilities hence estimate the uncertainty of the obtained flow properties on a spatial but specific pore structure with varying, but realistic wettabilities. The obtained relative permeability curves are compared with results obtained by traditional SCAL-analysis on similar core material from the Norwegian Continental Shelf. The results are also compared with the SCAL-model provided for full field simulations for the same field. The results from the pore scale simulations are within the uncertainty span of the SCAL models, mimic the traditional SCAL-experiments and shows that pore scale modelling can provide a time- and cost-effective tool to provide SCAL-models with uncertainties.

A Pore-Network-Modeling Approach To Predict Petrophysical Properties of Diatomaceous Reservoir Rock

2007 ◽  
Vol 10 (06) ◽  
pp. 597-608 ◽  
Author(s):  
Liping Jia ◽  
Cynthia Marie Ross ◽  
Anthony Robert Kovscek
Keyword(s):  
Multiphase Flow ◽  
Capillary Pressure ◽  
Network Model ◽  
Relative Permeability ◽  
Oil Recovery ◽  
Pore Network ◽  
Reservoir Rock ◽  
Pore Network Model ◽  
Rock Types ◽  
Capillary Pressure Curves

Summary A 3D pore-network model of two-phase flow was developed to compute permeability, relative permeability, and capillary pressure curves from pore-type, -size, and -shape information measured by means of high-resolution image analysis of diatomaceous-reservoir-rock samples. The diatomite model is constructed using pore-type proportions obtained from image analysis of epoxy-impregnated polished samples and mercury-injection capillary pressure curves for diatomite cores. Multiple pore types are measured, and each pore type has a unique pore-size and throat-size distribution that is incorporated in the model. Network results present acceptable agreement when compared to experimental measurements of relative permeability. The pore-network model is applicable to both drainage and imbibition within diatomaceous reservoir rock. Correlation of network-model results to well log data is discussed, thereby interpolating limited experimental results across the entire reservoir column. Importantly, our method has potential to predict the petrophysical properties for reservoir rocks with either limited core material or those for which conventional experimental measurements are difficult, unsuitable, or expensive. Introduction Model generation for reservoir simulation requires accurate entering of physical properties such as porosity, permeability, initial water saturation, residual-oil saturation, capillary pressure functions, and relative permeability curves. These functions and parameters are necessary to estimate production rate and ultimate oil recovery, and thereby optimize reservoir development. Accurate measurement and representation of such information is, therefore, essential for reservoir modeling. Relative permeability and capillary pressure curves are the most important constitutive relations to represent multiphase flow. Often, it is difficult to sample experimentally the range of relevant multiphase-flow behavior of a reservoir. In addition to the availability of rock samples, measurements are frequently time consuming to conduct, and conventional techniques are not suitable for all rock types (Schembre and Kovscek 2003). It is impossible, therefore, to measure all the unique relative permeability functions of different reservoir-rock types and variations within a rock type. This lack of constitutive information limits the accuracy of reservoir simulators to predict oil recovery. Simply put, other available data must be queried for their relevance to multiphase flow and must be used to interpret the available relative permeability and capillary pressure information.

A Machine Learning Methodology for Rock-Typing Using Relative Permeability Curves

2021 ◽  
Author(s):  
Carlos Esteban Alfonso ◽  
Frédérique Fournier ◽  
Victor Alcobia
Keyword(s):  
Machine Learning ◽  
Capillary Pressure ◽  
Relative Permeability ◽  
Fractional Flow ◽  
Flow Curves ◽  
Rock Types ◽  
Rock Typing ◽  
Relative Permeability Curves ◽  
Permeability Data

Abstract The determination of the petrophysical rock-types often lacks the inclusion of measured multiphase flow properties as the relative permeability curves. This is either the consequence of a limited number of SCAL relative permeability experiments, or due to the difficulty of linking the relative permeability characteristics to standard rock-types stemming from porosity, permeability and capillary pressure. However, as soon as the number of relative permeability curves is significant, they can be processed under the machine learning methodology stated by this paper. The process leads to an automatic definition of relative permeability based rock-types, from a precise and objective characterization of the curve shapes, which would not be achieved with a manual process. It improves the characterization of petrophysical rock-types, prior to their use in static and dynamic modeling. The machine learning approach analyzes the shapes of curves for their automatic classification. It develops a pattern recognition process combining the use of principal component analysis with a non-supervised clustering scheme. Before this, the set of relative permeability curves are pre-processed (normalization with the integration of irreducible water and residual oil saturations for the SCAL relative permeability samples from an imbibition experiment) and integrated under fractional flow curves. Fractional flow curves proved to be an effective way to unify the relative permeability of the two fluid phases, in a unique curve that characterizes the specific poral efficiency displacement of this rock sample. The methodology has been tested in a real data set from a carbonate reservoir having a significant number of relative permeability curves available for the study, in addition to capillary pressure, porosity and permeability data. The results evidenced the successful grouping of the relative permeability samples, according to their fractional flow curves, which allowed the classification of the rocks from poor to best displacement efficiency. This demonstrates the feasibility of the machine learning process for defining automatically rock-types from relative permeability data. The fractional flow rock-types were compared to rock-types obtained from capillary pressure analysis. The results indicated a lack of correspondence between the two series of rock-types, which testifies the additional information brought by the relative permeability data in a rock-typing study. Our results also expose the importance of having good quality SCAL experiments, with an accurate characterization of the saturation end-points, which are used for the normalization of the curves, and a consistent sampling for both capillary pressure and relative permeability measurements.

A New Unsteady-State Model for Calculating Oil-Water Relative Permeability of Low-Permeability/Ultra-Low Permeability Reservoirs

2012 ◽  
Vol 616-618 ◽  
pp. 964-969 ◽  
Author(s):  
Yue Yang ◽  
Xiang Fang Li ◽  
Ke Liu Wu ◽  
Meng Lu Lin ◽  
Jun Tai Shi
Keyword(s):  
Porous Media ◽  
Pressure Gradient ◽  
Capillary Pressure ◽  
Relative Permeability ◽  
Oil Reservoirs ◽  
Flow In Porous Media ◽  
Low Permeability ◽  
Threshold Pressure ◽  
Flow Process ◽  
Threshold Pressure Gradient

Oil and water relative permeabilities are main coefficients in describing the fluid flow in porous media; however, oil and water relative permeability for low - ultra low perm oil reservoir can not be obtained from present correlations. Based on the characteristics of oil and water flow in porous media, the model for calculating the oil and water relative permeability of low and ultra-low perm oil reservoirs, which considering effects of threshold pressure gradient and capillary pressure, has been established. Through conducting the non-steady oil and water relative permeability experiments, oil and water relative permeability curves influenced by different factors have been calculated. Results show that: the threshold pressure gradient more prominently affects the oil and water relative permeability; capillary pressure cannot influence the water relative permeability but only the oil relative permeability. Considering effects of threshold pressure gradient and capillary pressure yields the best development result, and more accordant with the flow process of oil and water in low – ultra low perm oil reservoirs.

Sign in / Sign up

Export Citation Format

Share Document