Relative Permeability Behavior of Oil- Water Systems in Wolfcamp and Eagle Ford Fractures

Presently, two-phase flow behavior through propped and unpropped fractures is poorly understood, and due to this fact, reservoir modeling using numerical simulation for the domain that contains fractures typically assumes straight-line relative permeability curves and zero capillary pressure in the fractures. However, there have been several studies demonstrating that both viscous and capillary dominated flow can be expected in fractured reservoirs, where non-linear fracture relative permeabilities must be used to accurately model these reservoirs. The objective of this study is to develop an understanding of the relative permeability of oil-water systems in fractures through experimental study. The experimental measurements conducted in this study were done using downhole cores from the Wolfcamp and the Eagle Ford Shale formations. The cores were cut to 1.5-in diameter and 6-in length testing samples. The specimens are saw-cut to generate a fracture along each sample first, and then conditioned in the reservoir fluid at the reservoir temperature for a minimum of 30 days prior to any testing. Wolfcamp and Eagle Ford formation oil and reconstituted brine with and without surfactants are used as the test fluids. The measurements were recorded at effective fracture closure stress and reservoir temperature. Also, real-time measurements of density, pressure, and flow rate are recorded throughout the duration of each test. Fluid saturation within the fracture was calculated using the mass continuity equation. The oil-water relative permeability was measured using the steady-state method. All measurements were conducted at reservoir temperature and at representative effective fracture closure stress. The data from the experimental measurements was analyzed using Darcy's law, and a clear relationship between relative permeability and saturation was observed. The calculated relative permeability curves closely follow the generalized Brooks-Corey correlation for oil-water systems. Furthermore, there was a significant difference in the relative permeability curves between the oil-water only systems and the oil-water surfactant systems. The result of this study is useful for estimating the expected oil production more realistically. It also provides information about the effect of surfactants on oil-water relative permeability for optimal design of fracture fluids.

Monitoring Dynamic Water Injection to Improve Oil Recovery Efficiency

Dynamic water, also known as smart water, injected at the end of conventional water flood by seawater, is known to show significant improvement in recovering additional oil. Different mechanisms have been proposed and lab measurements were conducted to understand the underlying process of additional oil recovery through dynamic water injection in lab conditions. In this work, we study the effects of different dynamic water injection scenarios on oil recovery in carbonate reservoirs based on reservoir simulations using representative fluid and rock properties with relative permeability curves obtained from core studies. To quantify the changes in measurable multiphysics properties due to dynamic water injection and reconcile multiphysics interpretation with additional oil recovery at field scale, a petrophysically consistent multiphysics effective property modeling is conducted. Based on the simulation results, dynamic water injection is shown to be effective in additional oil recovery at field scale post seawater injection. In addition, saturation changes caused by dynamic water injection result in detectable time-lapse contrast in the corresponding conductivity profiles, suggesting feasibility of the resistivity measurements to monitor dynamic water injection. This paper shows the advantages and benefits of petrophysically consistent multiphysics effective property modeling for a successful fluid monitoring design for quantifying the efficiency of dynamic water injection on additional oil recovery post seawater flood.

An Experimental Investigation of High and Low Salinity Waterflood Displacement Under the Steady-State Condition

Oil recovery by low salinity waterflood is significantly affected by fluid-fluid interaction through the micro-dispersion effect. This interaction influences rock wettability and relative permeability functions. Therefore, to gain a better insight into multiphase flow in porous media and perform numerical simulations, reliable relative permeability data is crucial. Unsteady-state or steady-state displacement methods are commonly used in the laboratory to measure water-oil relative permeability curves of a core sample. Experimentally, the unsteady-state core flood technique is more straightforward and less time-consuming compared to the steady-state method. However, the obtained data is limited to a small saturation range, and the associated uncertainty is not negligible. On the other hand, the steady-state method provides a more accurate dataset of two-phase relative permeability needed in the reservoir simulator for a reliable prediction of the high salinity and low salinity waterflood displacement performance. Considering the limitations of the unsteady state method, steady-state high salinity and low salinity brine experiments waterflood experiments were performed to compare the obtained relative permeability curves. The experiments were performed on a carbonate reservoir sample using a live reservoir crude oil under reservoir conditions. The test was designed so that the production and pressure drop curve covers a wider saturation range and provides enough data for analysis. Consequently, reliable relative permeability functions were obtained, initially, for a better comparison and prediction of the high salinity and the low salinity waterflood injections and then, to quantify the effect of low salinity waterflood under steady-state conditions. The results confirm the difference in relative permeability curves between high salinity and low salinity injections due to the micro-dispersion effect, which caused a decrease in water relative permeability and an increase in the oil relative permeability. These results also proved that low salinity brine can change the rock wettability from oil-wet or mixed-wet to more water-wet conditions. Furthermore, the obtained relative permeability curves extend across a substantial saturation range, making it valuable information required for numerical simulations. To the best of our knowledge, the reported data in this work is a pioneer in quantifying the impact of low salinity waterflood at steady-state conditions using a reservoir crude oil and reservoir rock, which is of utmost importance for the oil and gas industry.

Carbonate Karstified Oil Fields Geological Prediction and Dynamic Simulation Through Equivalent Relative Permeability Curves

Karst systems heterogeneity may become a nightmare for reservoir modelers in predicting presence, spatial distribution, impact on formation petrophysical characteristics, and particularly in dynamic behaviour prediction. Moreover, the very high resolution required to describe in detail the phenomena does not reconcile with the geo-cellular model resolution typically used for reservoir simulation. The scope of the work is to present an effective approach to predict karst presence and model it dynamically. Karst presence recognition started from the analysis of anomalous well behaviour and potential sources of precursors (logs, drilling evidence, etc.) to derive concepts for karst reservoir model. This first demanding step implies then characterizing each cell classified as karstified in terms of petrophysical parameters. In a two-phase flow, karst brings to fast travelling of water which leaves the matrix almost unswept. This feature was characterized through dedicated fine simulations, leading to an upscaling of relative permeability curves for a single porosity formulation. The workflow was applied to a carbonate giant field with a long production history under waterflood development. Firstly, a machine learning algorithm was trained to recognize karst features based on log response, seismic attributes, and well dynamic evidence, then a karst probability volume was generated and utilized to predict the karst presence in the field. Karst characterization just in terms of porosity and permeability is sufficient to model the reservoir when still in single phase, however it fails to reproduce observed water production. Karst provides a high permeability path for water transport: classical history match approaches, such as the introduction of permeability multipliers, proved to be ineffective in reproducing the water breakthrough timing and growth rate. In fact, the reservoir consists of two systems, matrix, and karst: however, the karst is less known and laboratory analysis shows relative permeability only for the matrix medium. The introduction of equivalent or pseudo-relative permeability curves, accounting for both the media, was crucial for correct modelling of the reservoir underlying dynamics, allowing a proper reproduction of water breakthrough timing and water cut (WCT) trends. The implementation of a dedicated pseudo relative permeability curve dedicated to karstified cells allowed to replicate early water arrival, thus bringing to a correct prediction of oil and water rates, also highlighting the presence of bypassed oil associated with water circuiting, particularly in presence of highly karstified cells.

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

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.

Numerical Investigation of Wettability Effects on Two-Phase Flow in Naturally Fractured Reservoirs Using Complex System Modelling Platform

Wettability is an essential component of reservoir characterization and plays a crucial role in understanding the dominant mechanisms in enhancing recovery from oil reservoirs. Wettability affects oil recovery by changing (drainage and imbibition) capillary pressure and relative permeability curves. This paper aims to investigate the role of wettability in matrix-fracture fluid transfer and oil recovery in naturally fractured reservoirs. Two experimental micromodels and one geological outcrop model were selected for this study. Three relative permeability and capillary pressure curves were assigned to study the role of matrix wettability. Linear relative permeability curves were given to the fractures. A complex system modelling platform (CSMP++) has been used to simulate water and polymer flooding in different wettability conditions. Comparing the micromodel data, CSMP++ and Eclipse validated and verified CSMP++. Based on the results, the effect of wettability alteration during water flooding is stronger than in polymer flooding. In addition, higher matrix-to-fracture permeability ratio makes wettability alteration more effective. The results of this study revealed that although an increase in flow rate decreases oil recovery in water-wet medium, it is independent of flow rate in the oil-wet system. Visualized data indicated that displacement mechanisms are different in oil-wet, mixed-wet and water-wet media. Earlier fracture breakthrough, later matrix breakthrough and generation and swelling of displacing phase at locations with high horizontal permeability contrast are the most important features of enhanced oil recovery in naturally fractured oil-wet rocks.

Experimental studies on influence of temperature on relative permeability curves in sandstone reservoirs

It is crucial to obtain the representative relative permeability curves for related numerical simulation and oilfield development. The influence of temperature on the relative permeability curve remains unclear. An unsteady method was adopted to investigate the influence of temperature (range from 25–130 °C) on the oil–water relative permeability curve of sandstone reservoirs in different blocks. Then, the experimental data was analyzed by using an improved Johnson–Bossler–Naumann method. Results reveal that with the increase in temperature within a certain temperature range: (1) the relative permeability of the oil and water phases increases; (2) the irreducible water saturation increases linearly, whereas the residual oil saturation decreases nonlinearly, and the oil recovery factor increases; and (3) the saturation of two equal permeability points moves to the right, and hydrophilicity becomes stronger. The findings will aid future numerical simulation studies, thus leading to the improvement of oil displacement efficiency.

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

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.

Issue with Stone-II Three Phase Permeability Model, and A Novel Robust Fundamentals-Based Alternative to It

The objective of this paper is to present a fundamentals-based, consistent with observation, three-phase flow model that avoids the pitfalls of conventional models such as Stone-II or Baker's three-phase permeability models. While investigating the myth of residual oil saturation in SAGD with comparing model generated results against field data, Gupta et al. (2020) highlighted the difficulty in matching observed residual oil saturation in steamed reservoir with Stone-II and Baker's linear models. Though the use of Stone-II model is very popular for three-phase flow across the industry, one issue in the context of gravity drainage is how it appears to counter-intuitively limit the flow of oil when water is present near its irreducible saturation. The current work begins with describing the problem with existing combinatorial methods such as Stone-II, which in turn combine the water-oil, and gas-oil relative permeability curves to yield the oil relative permeability curve in presence of water and gas. Then starting with the fundamentals of laminar flow in capillaries and with successive analogical formulations, it develops expressions that directly yield the relative permeabilities for all three phases. In this it assumes a pore size distribution approximated by functions used earlier in the literature for deriving two-phase relative permeability curves. The outlined approach by-passes the need for having combinatorial functions such as prescribed by Stone or Baker. The model so developed is simple to use, and it avoids the unnatural phenomenon or discrepancy due to a mathematical artefact described in the context of Stone-II above. The model also explains why in the past some researchers have found relative permeability to be a function of temperature. The new model is also amenable to be determined experimentally, instead of being based on an assumed pore-size distribution. In that context it serves as a set of skeletal functions of known dependencies on various saturations, leaving constants to be determined experimentally. The novelty of the work is in development of a three-phase relative permeability model that is based on fundamentals of flow in fine channels and which explains the observed results in the context of flow in porous media better. The significance of the work includes, aside from predicting results more in line with expectations and an explanation of temperature dependent relative permeabilities of oil, a more reliable time dependent residual oleic-phase saturation in the context of gravity-based oil recovery methods.