Derivation of Relative Permeability and Fractional Flow Behaviour From the Inversion of Saturation Logs in Horizontal Wells With Application to Water Shut-Off and Predicting Volumetric Sweep Efficiency

2007 ◽  
Author(s):  
Jeremy Harris
Keyword(s):  
Relative Permeability ◽  
Horizontal Wells ◽  
Flow Behaviour ◽  
Fractional Flow ◽  
Sweep Efficiency

A New Diagnostic Analysis Method for Waterflood Performance

2009 ◽  
Vol 12 (02) ◽  
pp. 341-351 ◽  
Author(s):  
Zhengming Yang
Keyword(s):  
Relative Permeability ◽  
Oil Recovery ◽  
Production Performance ◽  
Hydraulic Fractures ◽  
Diagnostic Analysis ◽  
Fractional Flow ◽  
Permeability Ratio ◽  
Sweep Efficiency ◽  
Widespread Application ◽  
Time Plot

Summary Despite the widespread application of reservoir simulation to study waterflood reservoirs, petroleum engineers still need simple predictive tools to forecast production decline, estimate ultimate oil recovery, and diagnose the production performance from the historical field data. On the basis of the Buckley-Leverett equation and the assumption of a semilog relationship between the oil-to-water relative permeability ratio and water saturation, a consistent analytical solution can be derived as:qoD (1 - qoD) = (EV /B)(1 / tD) where qoD is the oil fractional flow, tD is the fraction of cumulative liquid production to related formation volume, B is the relative permeability ratio parameter, and EV is the volumetric sweep efficiency. Two equivalent linear plots can be developed: a log-log plot and a reciprocal time plot. The log-log plot has a slope of -1 and intercept of EV /B. The reciprocal time plot has a slope of EV /B and an intercept value of 0. Both plots can be applied for the diagnostic analysis of waterflood reservoirs. Model and field case studies show the benefits of this technique as a production-decline analysis tool in forecasting the waterflood production decline and the ultimate oil recovery. This method can also be applied as a diagnostic tool to evaluate various aspects of waterflood performance. Examples include assessing waterflood maturity, calculating volumetric sweep efficiency, distinguishing the normal waterflood breakthrough from the premature water breakthrough through hydraulic fractures, and examining the consequences of operational changes. The appropriate use of this analytical method will help to optimize the field waterflood operation.

scholarly journals Experimental investigation of the displacement flow mechanism and oil recovery in primary polymer flood operations

2021 ◽  
Vol 3 (5) ◽  
Author(s):  
Ruissein Mahon ◽  
Gbenga Oluyemi ◽  
Babs Oyeneyin ◽  
Yakubu Balogun
Keyword(s):  
Relative Permeability ◽  
Oil Recovery ◽  
Polymer Flooding ◽  
Mobility Control ◽  
List Type ◽  
Motor Oil ◽  
Flow Mechanism ◽  
Fractional Flow ◽  
Recovery Method ◽  
Polymer Flood

Abstract Polymer flooding is a mature chemical enhanced oil recovery method employed in oilfields at pilot testing and field scales. Although results from these applications empirically demonstrate the higher displacement efficiency of polymer flooding over waterflooding operations, the fact remains that not all the oil will be recovered. Thus, continued research attention is needed to further understand the displacement flow mechanism of the immiscible process and the rock–fluid interaction propagated by the multiphase flow during polymer flooding operations. In this study, displacement sequence experiments were conducted to investigate the viscosifying effect of polymer solutions on oil recovery in sandpack systems. The history matching technique was employed to estimate relative permeability, fractional flow and saturation profile through the implementation of a Corey-type function. Experimental results showed that in the case of the motor oil being the displaced fluid, the XG 2500 ppm polymer achieved a 47.0% increase in oil recovery compared with the waterflood case, while the XG 1000 ppm polymer achieved a 38.6% increase in oil recovery compared with the waterflood case. Testing with the motor oil being the displaced fluid, the viscosity ratio was 136 for the waterflood case, 18 for the polymer flood case with XG 1000 ppm polymer and 9 for the polymer flood case with XG 2500 ppm polymer. Findings also revealed that for the waterflood cases, the porous media exhibited oil-wet characteristics, while the polymer flood cases demonstrated water-wet characteristics. This paper provides theoretical support for the application of polymer to improve oil recovery by providing insights into the mechanism behind oil displacement. Graphic abstract Highlights The difference in shape of relative permeability curves are indicative of the effect of mobility control of each polymer concentration. The water-oil systems exhibited oil-wet characteristics, while the polymer-oil systems demonstrated water-wet characteristics. A large contrast in displacing and displaced fluid viscosities led to viscous fingering and early water breakthrough.

Fast Upscaling of Polymer Flood Simulations Using Fractional Flow and Scaled Mobilities

2021 ◽  
Author(s):  
Hasan Al-Ibadi ◽  
Karl Stephen ◽  
Eric Mackay
Keyword(s):  
Relative Permeability ◽  
Control Method ◽  
Pressure Profile ◽  
Polymer Flooding ◽  
Mobility Control ◽  
Scale Model ◽  
Fine Scale ◽  
Fractional Flow ◽  
Water Viscosity ◽  
Flow Mobility

Abstract We introduce a pseudoisation method to upscale polymer flooding in order to capture the flow behaviour of fine scale models. This method is also designed to improve the predictability of pressure profiles during this process. This method controls the numerical dispersion of coarse grid models so that we are able to reproduce the flow behaviour of the fine scale model. To upscale polymer flooding, three levels of analysis are required such that we need to honour (a) the fractional flow solution, (b) the water and oil mobility and (c) appropriate upscaling of single phase flow. The outcome from this analysis is that a single pseudo relative permeability set that honours the modification that polymer applies to water viscosity modification without explicitly changing it. The shape of relative permeability can be chosen to honour the fractional flow solution of the fine scale using the analytical solution. This can result in a monotonic pseudo relative permeability set and we call it the Fractional-Flow method. To capture the pressure profile as well, individual relative permeability curves must be chosen appropriately for each phase to ensure the correct total mobility. For polymer flooding, changes to the water relative permeability included the changes to water viscosity implicitly thus avoiding the need for inclusion of a polymer solute. We call this type of upscaling as Fractional-Flow-Mobility control method. Numerical solution of the upscaled models, obtained using this method, were validated against fine scale models for 1D homogenous model and as well as 3D models with randomly distributed permeability for various geological realisations. The recovery factor and water cut matched the fine scale model very well. The pressure profile was reasonably predictable using the Fractional-Flow-Mobility control method. Both Fractional-Flow and Fractional-flow-Mobility control methods can be calculated in advance without running a fine scale model where the analysis is based on analytical solution even though produced a non-monotonic pseudo relative permeability curve. It simplified the polymer model so that it is much easier and faster to simulate. It offers the opportunity to quickly predict oil and water phase behaviour.

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.

Individual Fracture Efficiency Monitoring in Horizontal Wells by Using a New 3d Fine-Grid Temperature Modelling

2021 ◽  
Author(s):  
Vitaly Virt ◽  
Vladimir Kosolapov ◽  
Vener Nagimov ◽  
Andrey Salamatin ◽  
Yulia Fesina ◽  
...  
Keyword(s):  
Hydraulic Fracturing ◽  
Oil Recovery ◽  
Transient Analysis ◽  
Horizontal Wells ◽  
Sweep Efficiency ◽  
Fine Grid ◽  
Hydrodynamic Parameters ◽  
Multistage Hydraulic Fracturing ◽  
Multi Stage ◽  
Differential Efficiency

Abstract Profitable development of hard-to-recover reserves often involves drilling of horizontal wells with multistage hydraulic fracturing to increase the oil recovery factor. Usually to monitor the fracture sweep efficiency, pressure transient analysis is used. However, in case of several fractures this method delivers only average hydrodynamic parameters of the well-fracture system. This paper illustrates the value of temperature logging data and demonstrates possibilities of the 3-D thermo-mechanical modelling in evaluating the differential efficiency of multi-stage hydraulic fracturing.

scholarly journals Improved method for complete gas-brine imbibition relative permeability curves

2020 ◽  
Vol 146 ◽  
pp. 03003
Author(s):  
M. Ben Clennell ◽  
Cameron White ◽  
Ausama Giwelli ◽  
Matt Myers ◽  
Lionel Esteban ◽  
...  
Keyword(s):  
Relative Permeability ◽  
Gas Permeability ◽  
Test Methods ◽  
Standard Test ◽  
High Rate ◽  
Fractional Flow ◽  
Gas Reservoirs ◽  
Data Gap ◽  
Small Increments ◽  
Relative Permeability Curves

Standard test methods for measuring imbibition gas-brine relative permeability on reservoir core samples often lead to non-uniform brine saturation. During co-current flow, the brine tends to bank up at the sample inlet and redistributes slowly, even with fractional flow of gas to brine of 400:1 or more. The first reliable Rel Perm point is often only attained after a brine saturation of around Sw=40% is achieved, leaving a data gap between Swirr and this point. The consequent poor definition of the shape of the Rel Perm function can lead to uncertainty in the performance of gas reservoirs undergoing depletion drive with an encroaching aquifer or subjected to a water flood. We have developed new procedures to pre-condition brine saturation outside of the test rig and progress it in small increments to fill in the data gap at low Sw, before continuing with a co-current flood to the gas permeability end-point. The method was applied to series of sandstone samples from gas reservoirs from the NW Shelf of Australia, and a Berea standard. We found that the complete imbibition relative permeability curve is typically ‘S’ shaped or has a rolling over, convex-up shape that is markedly different from the concave-up, Corey Rel Perm curve usually fitted to SCAL test data. This finding may have an economic upside if the reservoir produces gas at a high rate for longer than was originally predicted based on the old Rel Perm curves.

Fractional Flow Behaviour and Transport Mechanisms During Low salinity and Protein Enzyme Bio-surfactant EOR Flooding

2020 ◽  
Author(s):  
Anthony Morgan ◽  
Lateef Akanji ◽  
Tinuola Udoh ◽  
Shaibu Mohammed ◽  
Prosper Anumah ◽  
...  
Keyword(s):  
Flow Behaviour ◽  
Transport Mechanisms ◽  
Fractional Flow ◽  
Low Salinity ◽  
Protein Enzyme
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