Derivation of Relative Permeability Curves from Capillary Pressure Curves for Tight Sandstone Reservoir Based on Fractal Theory

2016 ◽  
Author(s):  
Xiuyu Wang ◽  
Xiaoqiu Wang ◽  
Jianfu Wang ◽  
Yu Pu ◽  
Shenglai Yang
Keyword(s):  
Capillary Pressure ◽  
Relative Permeability ◽  
Fractal Theory ◽  
Tight Sandstone ◽  
Sandstone Reservoir ◽  
Relative Permeability Curves ◽  
Capillary Pressure Curves

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.

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.

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 COMPREHENSIVE MODEL FOR OIL–WATER RELATIVE PERMEABILITIES IN LOW-PERMEABILITY RESERVOIRS BY FRACTAL THEORY

Fractals ◽  
2020 ◽  
Vol 28 (03) ◽  
pp. 2050055
Author(s):  
HAIBO SU ◽  
SHIMING ZHANG ◽  
YEHENG SUN ◽  
XIAOHONG WANG ◽  
BOMING YU ◽  
...  
Keyword(s):  
Fractal Dimension ◽  
Capillary Pressure ◽  
Relative Permeability ◽  
Fractal Theory ◽  
Low Permeability ◽  
Comprehensive Model ◽  
Relative Permeabilities ◽  
Oil Water ◽  
Seepage Characteristics ◽  
Low Permeability Reservoirs

Oil–water relative permeability curve is an important parameter for analyzing the characters of oil and water seepages in low-permeability reservoirs. The fluid flow in low-permeability reservoirs exhibits distinct nonlinear seepage characteristics with starting pressure gradient. However, the existing theoretical model of oil–water relative permeability only considered few nonlinear seepage characteristics such as capillary pressure and fluid properties. Studying the influences of reservoir pore structures, capillary pressure, driving pressure and boundary layer effect on the morphology of relative permeability curves is of great significance for understanding the seepage properties of low-permeability reservoirs. Based on the fractal theory for porous media, an analytically comprehensive model for the relative permeabilities of oil and water in a low-permeability reservoir is established in this work. The analytical model for oil–water relative permeabilities obtained in this paper is found to be a function of water saturation, fractal dimension for pores, fractal dimension for tortuosity of capillaries, driving pressure gradient and capillary pressure between oil and water phases as well as boundary layer thickness. The present results show that the relative permeabilities of oil and water decrease with the increase of the fractal dimension for tortuosity, whereas the relative permeabilities of oil and water increase with the increase of pore fractal dimension. The nonlinear properties of low-permeability reservoirs have the prominent significances on the relative permeability of the oil phase. With the increase of the seepage resistance coefficient, the relative permeability of oil phase decreases. The proposed theoretical model has been verified by experimental data on oil–water relative permeability and compared with other conventional oil–water relative permeability models. The present results verify the reliability of the oil–water relative permeability model established in this paper.

Nonequilibrium capillary pressure and relative permeability curves of porous media

AIChE Journal ◽  
2003 ◽  
Vol 49 (10) ◽  
pp. 2472-2486 ◽  
Author(s):  
C. D. Tsakiroglou ◽  
M. A. Theodoropoulou ◽  
V. Karoutsos
Keyword(s):  
Porous Media ◽  
Capillary Pressure ◽  
Relative Permeability ◽  
Relative Permeability Curves

The importance of optimal geological zonation and choice of rock parameters in dynamic reservoir simulation: a case study for the Laminaria field

2018 ◽  
Vol 58 (2) ◽  
pp. 683 ◽  
Author(s):  
Peter Behrenbruch ◽  
Tuan G. Hoang ◽  
Khang D. Bui ◽  
Minh Triet Do Huu ◽  
Tony Kennaird
Keyword(s):  
Capillary Pressure ◽  
Relative Permeability ◽  
Reservoir Simulation ◽  
History Matching ◽  
Laboratory Data ◽  
Analysis Data ◽  
Scale Model ◽  
Core Analysis ◽  
Relative Permeability Curves ◽  
Rock Parameters

The Laminaria field, located offshore in the Timor Sea, is one of Australia’s premier oil developments operated for many years by Woodside Energy Ltd. First production was achieved in 1999 using a state-of-the-art floating production storage and offloading vessel, the largest deployed in Australian waters. As is typical, dynamic reservoir simulation was used to predict reservoir performance and forecast production and ultimate recovery. Initial models, using special core analysis (SCAL) laboratory data and pseudos, covered a range of approaches, field and conceptual models. Initial coarser models also used straight-line relative permeability curves. These models were later refined during history matching. The success of simulation studies depends critically on optimal gridding, particularly vertical definition. An objective of the study presented is to demonstrate the importance of optimal and detailed vertical zonation using Routine Core Analysis data and a range of Hydraulic Flow Zone Unit models. In this regard, the performance of a fine-scale model is compared with three alternative, more traditional and coarse models. Secondly the choice of SCAL rock parameters may also have a significant impact, particularly relative permeability. This paper discusses the use of the more recently developed Carman-Kozeny based SCAL models, the Modified Carman-Kozeny Purcell (MCKP) model for capillary pressure and the 2-phase Modified Carman-Kozeny (2p-MCK) model for relative permeability. These models compare favourably with industry standard approaches, the use of Leverett J-functions for capillary pressure and the Modified Brooks-Corey model for relative permeability. The benefit of the MCK-based models is that they have better functionality and far better adherence to actual laboratory data.

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