The Calculation of Waterflood Recovery From Steady-State Relative Permeability Data

10.2118/759-g ◽  
1957 ◽  
Vol 9 (05) ◽  
pp. 64-66 ◽  
Author(s):  
J.G. Richardson
Keyword(s):  
Steady State ◽  
Relative Permeability ◽  
Permeability Data

Steady-State Measurements of Relative Permeability for Polymer/Oil Systems

1982 ◽  
Vol 22 (01) ◽  
pp. 79-86 ◽  
Author(s):  
F.N. Schneider ◽  
W.W. Owens
Keyword(s):  
Steady State ◽  
Relative Permeability ◽  
Oil Recovery ◽  
Water Saturation ◽  
Flow Behavior ◽  
Residual Effect ◽  
Sweep Efficiency ◽  
Residual Resistance ◽  
Reservoir Permeability ◽  
Permeability Data

Abstract Means for increasing tertiary oil recoveries from previously waterflooded viscous oil reservoirs are receiving added attention today as a result of industry-wide efforts to improve U.S. oil producing rates and reserves. Injection of a bank of polymer solution that precedes injection of a miscible slug (e.g., a micellar fluid) can reduce reservoir permeability contrasts and result in improvement of the sweep efficiency of the process. To evaluate the potential magnitude of improved recovery and economics of prior polymer slug injection, there is a need for basic polymer/oil relative permeability data for use in performance evaluation calculations. Such relative permeability data were measured by steady-state procedures on a suite of 18 out-crop and formation core samples ranging, in permeability from about 50 to 1,200 md. Six different polyacrylamide polymers were tested, and resistance and residual resistance data were obtained on each. Data were obtained in both oil-wet and water-wet systems. The observation in these studies was that the presence of polymers in the water phase had a significant and consistent effect, lowering water relative permeability over the entire water saturation range. In many of the tests, the presence of flowing polymer or its residual effect during subsequent brine flow had no effect on oil relative permeability. In several tests, polymer contact actually improved oil mobility through increases in oil relative permeability at all levels of oil saturation. Permeability level and polymer type produced no clear-cut differences in flow behavior. The obvious differences in core wettability resulted in widely varying relative permeability characteristics, but again the effect of polymer contact was about the same, qualitatively, as obtained on the water-wet cores. Introduction The steady decline of U.S. oil reserves and rapidly, increasing, prices obtained for each barrel of crude produced are strong incentives to maximize recoveries for all reservoirs. Various enhanced oil recovery techniques are being tested and used for recovering some of the oil left behind after conventional waterflooding. The added recovery achievable with such processes, however, is influenced to a large degree by one of the same factors leading to inefficient waterflooding - i.e., reservoir heterogeneity. Numerous laboratory studies using, both physical and mathematical models, plus numerous field projects, have shown that when contrasts in reservoir permeability increase, recovered by any external injection recovery process decreases as a result of reduced sweep efficiency. Thus, if recoveries from the more heterogeneous reservoirs are to be maximized, procedures must be developed for reducing the permeability contrasts before application of an EOR process or by mobility adjustment within the process itself. Preinjection of polymers in advance of a micellar flood has been proposed as a means for improving reservoir sweep efficiency by reducing permeability contrasts. Laboratory tests of this process demonstrated that, in both linear and five-spot stratified systems, the residual resistance effect achieved by preinjection of poly-acrylamide polymers resulted in improved sweep and additional recovery by subsequent micellar flooding. In the one reported field test of this process, tertiary oil was mobilized and recovered, but insufficient data are available to indicate whether the preinjected polymer resulted in improved sweep efficiency. Mathematical model studies provide a reliable means for evaluating potential benefits of polymer preinjection. However, such studies require input data that permit the model to simulate the physical processes that may occur in the reservoir. This laboratory study was conducted to provide such data. SPEJ P. 79^

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.

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