Pore-by-Pore Modelling,Validation and Prediction of Waterflooding in Oil-Wet Rocks Using Dynamic Synchrotron Data

We predict waterflood displacement on a pore-by-pore basis using pore network modelling. The pore structure is captured by a high-resolution image. We then use an energy balance applied to images of the displacement to assign an average contact angle, and then modify the local pore-scale contact angles in the model about this mean to match the observed displacement sequence. Two waterflooding experiments on oil-wet rocks are analysed where the displacement sequence was imaged using time-resolved synchrotron imaging. In both cases the capillary pressure in the model matches the experimentally obtained values derived from the measured interfacial curvature. We then predict relative permeability for the full saturation range. Using the optimised contact angles distributed randomly in space has little effect on the predicted capillary pressures and relative permeabilities, indicating that spatial correlation in wettability is not significant in these oil-wet samples. The calibrated model can be used to predict properties outside the range of conditions considered in the experiment.

     

Validity of the bead-spring model for describing the linear viscoelastic properties of single-strand DNA under strongly denaturing conditions

Using a normal mode analysis, we predict the infinite dilution linear viscoelastic properties of single-strand (ss) DNA molecules and compare the results to the linear viscoelastic data of Shusterman et al. (Phys Rev Let 92(4):048303, 2004) obtained by monitoring the diffusion of a fluorescently labeled terminus of the molecule. To compute the overall best global fit, we constrain the hydrodynamic interaction parameter, h*, equilibrium root mean square spring extension, b, and the number of Kuhn steps per spring, N _K,S, to be equal for the strands compared. The fits using the bead-spring model for all but 23,100 base ss-DNA strands match the experimental data at long times with significant deviations at intermediate and short times. However, parameters fitted separately to all individual strand lengths predict results well. The best fits to data for 2,400 and 6,700 base pairs yield N _K,S ∼12 and h* = 0.12. These values are similar to those found for conventional polymers such as polystyrene which have been successfully modeled with N _K,S ∼7 and h* = 0.15, indicating ss-DNA and polystyrene exhibit analogous hydrodynamic behavior.     

A Geometrical Pore Model for Estimating the Microscopical Pore Geometry of Soil with Infiltration Measurements

This paper presents a simple geometrical pore model designed to relate characteristic pore radii of the porous network of soils with macroscopic infiltration parameters. The model composed of a stack of spherical hollow elements is described with two radii values: the pore access radius and the actual pore radius. The model was compared to cylindrical pore models and its mathematical consistency was assessed. Soil sorptivity S and the second parameter A of the Philip infiltration equation (1957), have been determined by numerically simulated infiltration. A diagram and an empirical relation have been set in order to relate the pore access and pore radii to the infiltration parameters S and A. The consistency of the model was validated by comparing the predicted sorptivity and hydraulic conductivity values, with the widely used unsaturated soil hydraulic functions (van Genuchten, 1980). The model showed good agreement with experimental infiltration data, and it is therefore concluded that the use of a model with two radii improves the relation between microscopic pore size and macroscopic infiltration parameters.     

Two-Phase Flow Properties Prediction from Small-Scale Data Using Pore-Network Modeling

The objective of this work is to evaluate the prediction accuracy of network modeling to calculate transport properties of porous media based on the interpretation of mercury invasion capillary pressure curves only. A pore-scale modeling approach is used to model the multi-phase flow and calculate gas/oil relative permeability curves. The characteristics of the 3-D pore-network are defined with the requirement that the network model satisfactorily reproduces the capillary pressure curve (P_c curve), the porosity and the permeability. A sensitivity study on the effect of the input parameters on the prediction of capillary pressure and gas/oil relative permeability curves is presented. The simulations show that different input parameters can lead to similarly good reproductions of the experimental P_c, although the predicted relative permeabilities K_r are somewhat widespread. This means that the information derived from a mercury invasion P_c curve is not sufficient to characterize transport properties of a porous medium. The simulations indicate that more quantitative information on the wall roughness and the node/bond aspect ratio would be necessary to better constrain the problem. There is also evidence that in narrow pore size distributions pore body volume and pore throat radius are correlated while in broad pore size distributions they would be uncorrelated.     

Achieving Rapid Absorption and Extensive Liquid Uptake Capacity in Porous Structures by Decoupling Capillarity and Permeability: Nanoporous Modified Calcium Carbonate

Porous media with rapid absorption properties are greatly sought after in the fields of super absorbers and catalysts. Natural materials, such as diatomite, or synthetic zeolite feature strongly in industrial reaction processes. Most, or all, of such materials, however, are surface acidic. A novel rapidly absorbing alkaline porous structure, with a high absorption capacity, is presented here. As in the case of diatomite or zeolite, the pigment design incorporates strong capillarity within a highly permeable packed medium. A model is proposed for general use with highly absorbing media that can be proven microscopically to have separate domains of micro- or nano-capillarity embedded within a permeable matrix. The new pigment morphology, based on natural ground calcium carbonate (gcc), exhibits this property using special surface structure modifications. It is contrasted with standard gcc by using consolidated tablet blocks made from a suspension of the pigment and chosen mixtures thereof. The blocks are characterised after drying by mercury porosimetry, and the absorption dynamic of a selected liquid is studied. It is shown that using a self-assembly method of discrete pore structures provides a much faster absorption rate and a liquid capacity for up to 10 times more fluid than a conventional homogeneously distributed pore concept. In such unique discrete network systems, the mercury intrusion curve provides a separable analysis of permeability and capillarity in respect to the inflection point of the cumulative intrusion curve. The discrete decoupled properties each follow the absorption behaviour predicted by previous modelling (Ridgway and Gane, Colloids and Surfaces A: Physicochemical and Engineering Aspects, 206(1–3), 2002). The absorption driving force is shown to be determined by the proportion of fine pores present up to a size equal to a Bosanquet inertially-defined optimum within the timescale of absorption. Combining the wetting force, from the capillarity-controlled fine pore structure, with the experimental flow resistance of the sample, consisting of the assembly of particles, it is possible to predict the trends in absorption dynamic using the pore and throat model Pore-Cor.* Use of this model allows existing materials as well as new synthetic designs to be modelled prior to manufacture. The novel alkaline material is compared with independent absorption data for diatomite and shown to be comparable. *Pore-Cor is a software product name of the Environmental and Fluid Modelling Group, University of Plymouth, Devon PL4 8AA, U.K.     

Stochastic Pore Network Generation from 3D Rock Images

Pore networks can be extracted from 3D rock images to accurately predict multi-phase flow properties of rocks by network flow simulation. However, the predicted flow properties may be sensitive to the extracted pore network if it is small, even though its underlying characteristics are representative. Therefore, it is a challenge to investigate the effects on flow properties of microscopic rock features individually and collectively based on small samples. In this article, a new approach is introduced to generate from an initial network a stochastic network of arbitrary size that has the same flow properties as the parent network. Firstly, we characterise the realistic parent network in terms of distributions of the geometrical pore properties and correlations between these properties, as well as the connectivity function describing the detailed network topology. Secondly, to create a stochastic network of arbitrary size, we generate the required number of nodes and bonds with the correlated properties of the original network. The nodes are randomly located in the given network domain and connected by bonds according to the strongest correlation between node and bond properties, while honouring the connectivity function. Thirdly, using a state-of-the-art two-phase flow network model, we demonstrate for two samples that the rock flow properties (capillary pressure, absolute and relative permeability) are preserved in the stochastic networks, in particular, if the latter are larger than the original, or the method reveals that the size of the original sample is not representative. We also show the information that is necessary to reproduce the realistic networks correctly, in particular the connectivity function. This approach forms the basis for the stochastic generation of networks from multiple rock images at different resolutions by combining the relevant statistics from the corresponding networks, which will be presented in a future publication.     

Equivalent Fracture Network Permeability of Multilayer-Complex Naturally Fractured Reservoirs

Modeling naturally fractured reservoirs (NFRs) requires an accurate representation of fracture network permeability (FNP). Conventionally, logs, cores, seismic, and pressure transient tests are used as a data base for this. Our previous attempts showed that a strong correlation exists between the fractal parameters of 2-D fracture networks and their permeability (Jafari and Babadagli, SPE 113618, Western Regional and Pacific Section AAPG joint meeting, 2008; Jafari and Babadagli, SPE Reserv Eval Eng 12(3):455–469, 2009a). We also showed that 1-D well (cores-logs) and 3-D reservoir data (well test) may not be sufficient in FNP mapping and that 2-D (outcrop) characteristics are needed (Jafari and Babadagli, SPE 124077, SPE/EAGE reservoir characterization and simulation conference, 2009b). This paper is an extension of those studies, where only 2-D (single-layer, uniform fracture characteristics in z direction) representations were used. In this paper, we considered a more complex and realistic 3-D network system. Two-dimensional random fractures with known fractal and statistical characteristics were distributed in the x- and y directions. A variation of fracture network characteristics in the z direction was presented by a multilayer system representing three different facieses with different fracture properties. Wells were placed in different locations of the model to collect 1-D fracture density and pressure transient data. In addition, five different fractal and statistical properties of the network of each layer were measured. The equivalent FNP was calculated using a commercial software package as the base case. Using available 1-D, 2-D, and 3-D data, multivariable regression analyses were performed to obtain equivalent FNP correlations for many different fracture network realizations. The derived equations were validated against a new set of synthetic fracture networks, and the conditions at which 1-D, 2-D and 3-D data are sufficient to map FNP were determined. The importance of the inclusion of each data type, i.e., 1-D, 2-D and 3-D, in the correlations was discussed. It was shown that using only 3-D data are insufficient to predict the FNP due to wide spatial heterogeneity of the fracture properties in the reservoir, which cannot be captured from single-well tests. Incorporating all types of data (1-D, 2-D, and 3-D) would result in better prediction. Also, it is recommended that the 2-D data of the most conductive layer in reservoir, which has longer fractures with a higher density, should be incorporated in the correlations.     

Insights into the Relationships Among Capillary Pressure,Saturation, Interfacial Area and Relative Permeability Using Pore-Network Modeling

To gain insight in relationships among capillary pressure, interfacial area, saturation, and relative permeability in two-phase flow in porous media, we have developed two types of pore-network models. The first one, called tube model, has only one element type, namely pore throats. The second one is a sphere-and-tube model with both pore bodies and pore throats. We have shown that the two models produce distinctly different curves for capillary pressure and relative permeability. In particular, we find that the tube model cannot reproduce hysteresis. We have investigated some basic issues such as effect of network size, network dimension, and different trapping assumptions in the two networks. We have also obtained curves of fluid–fluid interfacial area versus saturation. We show that the trend of relationship between interfacial area and saturation is largely influenced by trapping assumptions. Through simulating primary and scanning drainage and imbibition cycles, we have generated two surfaces fitted to capillary pressure, saturation, and interfacial area (P _c –S _w –a _nw ) points as well as to relative permeability, saturation, and interfacial area (k _r –S _w –a _nw ) points. The two fitted three-dimensional surfaces show very good correlation with the data points. We have fitted two different surfaces to P _c –S _w –a _nw points for drainage and imbibition separately. The two surfaces do not completely coincide. But, their mean absolute difference decreases with increasing overlap in the statistical distributions of pore bodies and pore throats. We have shown that interfacial area can be considered as an essential variable for diminishing or eliminating the hysteresis observed in capillary pressure–saturation (P _c –S _w ) and the relative permeability–saturation (k _r –S _w ) curves.     

An Ising-Based Simulator for Capillary Action in Porous Media

Multiphase flows in porous media are encountered in several contexts—e.g., hydrocarbon recovery operations, battery electrodes, microfluidic devices, etc. Capillary-dominated flows are interesting due to the complex interplay of interfacial properties and pore geometries. Conventional hydrodynamic flow solvers are computationally inefficient in the capillary-dominated regime, particularly in complex pore structures. The algorithm developed here specifically targets this regime to reduce simulation times. We minimise the fluid–fluid and fluid–solid interaction energies through an approach inspired by the ferromagnetic Ising model. We validate the algorithm on (1) model pore geometries with analytical solutions for capillary action, and (2) rocks with available mercury porosimetry data. We validate its predictions for model geometries and sandstones using (1) curvatures calculated from theories developed by Mayer–Stowe–Princen, Ma and Morrow, and Mason and Morrow; (2) predictions from GeoDict, a commercial software package, which also includes a state-of-the-art drainage simulator; (3) mercury porosimetry data. Drainage capillary pressure curves predicted for Bentheimer and Fontainebleau rocks reasonably match porosimetry data.     

Effect of Network Topology on Relative Permeability

We consider the role of topology on drainage relative permeabilities derived from network models. We describe the topological properties of rock networks derived from a suite of tomographic images of Fontainbleau sandstone (Lindquist et al., 2000, J. Geophys. Res. 105B, 21508). All rock networks display a broad distribution of coordination number and the presence of long-range topological bonds. We show the importance of accurately reproducing sample topology when deriving relative permeability curves from the model networks. Comparisons between the relative permeability curves for the rock networks and those computed on a regular cubic lattice with identical geometric characteristics (pore and throat size distributions) show poor agreement. Relative permeabilities computed on regular lattices and on diluted lattices with a similar average coordination number to the rock networks also display poor agreement. We find that relative permeability curves computed on stochastic networks which honour the full coordination number distribution of the rock networks produce reasonable agreement with the rock networks. We show that random and regular lattices with the same coordination number distribution produce similar relative permeabilities and that the introduction of longer-range topological bonds has only a small effect. We show that relative permeabilities for networks exhibiting pore–throat size correlations and sizes up to the core-scale still exhibit a significant dependence on network topology. The results show the importance of incorporating realistic 3D topologies in network models for predicting multiphase flow properties.     

A New Method for Generating Pore-Network Models of Porous Media

In this study, we have developed a new method to generate a multi-directional pore network for representing a porous medium. The method is based on a regular cubic lattice network, which has two elements: pore bodies located at the regular lattice points and pore throats connecting the pore bodies. One of the main features of our network is that pore throats can be oriented in 13 different directions, allowing a maximum coordination number of 26 that is possible in a regular lattice in 3D space. The coordination number of pore bodies ranges from 0 to 26, with a pre-specified average value for the whole network. We have applied this method to reconstruct real sandstone and granular sand samples through utilizing information on their coordination number distributions. Good agreement was found between simulation results and observation data on coordination number distribution and other network properties, such as number of pore bodies and pore throats and average coordination number. Our method can be especially useful in studying the effect of structure and coordination number distribution of pore networks on transport and multiphase flow in porous media systems.     

Simulation of capillary pressure curves using bond correlated site percolation on a simple cubic network

A stochastic approach to network modelling has been used to simulate quasi-static immiscible displacement in porous media. Both number-based and volume-based network saturation results were obtained. Number-based results include: number-based saturation curves for primary drainage, secondary imbibition and secondary drainage, fluid distribution data, and cluster trapping history. Using pore structure data of porous media, it is possible to convert the number-based curves to capillary pressure — saturation relationships. Pore size distribution functions and pore shapes which are thought to closely represent Berea sandstone samples were used to predict the capillary curves. The physical basis of these calculations is a one-to-one correspondence between the cumulative node and bond index fractions in the network analysis, and the cumulative number-based distributions of pore body and pore throat diameters, respectively. The oil-water capillary pressure curve simulated for primary drainage closely resembles those measured experimentally. The agreement between the simulated and the measured secondary imbition and secondary drainage curves is less satisfactory.     

Pore-scale Network Modeling of Three-Phase Flow Based on Thermodynamically Consistent Threshold Capillary Pressures. II. Results

We use the model described in Zolfaghari and Piri (Transp Porous Media, 2016) to predict two- and three-phase relative permeabilities and residual saturations for different saturation histories. The results are rigorously validated against their experimentally measured counterparts available in the literature. We show the relevance of thermodynamically consistent threshold capillary pressures and presence of oil cusps for significantly improving the predictive capabilities of the model at low oil saturations. We study systems with wetting and spreading oil layers and cusps. Three independent experimental data sets representing different rock samples and fluid systems are investigated in this work. Different disordered networks are used to represent the pore spaces in which different sets of experiments were performed, i.e., Berea, Bentheimer, and reservoir sandstones. All three-phase equilibrium interfacial tensions used for the simulation of three-phase experimental data are measured and used in the model’s validation. We use a fixed set of parameters, i.e., the input network (to represent the pore space) and contact angles (to represent the wettability state), for all experiments belonging to a data set. Incorporation of the MSP method for capillary pressure calculations and cusp analysis significantly improves the agreement between the model’s predictions of relative permeabilities and residual oil saturations with experimental data.     

Two-Phase Pore-Network Modelling: Existence of Oil Layers During Water Invasion

Pore-network modelling is commonly used to predict capillary pressure and relative permeability functions for multi-phase flow simulations. These functions strongly depend on the presence of fluid films and layers in pore corners. Recently, van Dijke and Sorbie (J. Coll. Int. Sci. 293:455–463, 2006) obtained the new thermodynamically derived criterion for oil layers existence in the pore corners with non-uniform wettability caused by ageing. This criterion is consistent with the thermodynamically derived capillary entry pressures for other water invasion displacements and it is more restrictive than the previously used geometrical layer collapse criterion. The thermodynamic criterion has been included in a newly developed two-phase flow pore network model, as well as two versions of the geometrical criterion. The network model takes as input networks extracted from pore space reconstruction methods or CT images. Furthermore, a new n-cornered star shape characterization technique has been implemented, based on shape factor and dimensionless hydraulic radius as input parameters. For two unstructured networks, derived from a Berea sandstone sample, oil residuals have been estimated for different wettability scenarios, by varying the contact angles in oil-filled pores after ageing from weakly to strongly oil-wet. Simulation of primary drainage, ageing and water invasion show that the thermodynamical oil layer existence criterion gives more realistic oil residual saturations compared to the geometrical criteria. Additionally, a sensitivity analysis has been carried out of oil residuals with respect to end-point capillary pressures. For strongly oil-wet cases residuals increase strongly with increasing end-point capillary pressures, contrary to intermediate oil-wet cases.     

Using a Lumped Parameter Dynamic Model of a Rock Bolt to Produce Training Data for a Neural Network for Diagnosis of Real Data

Ground anchorage systems are used extensively throughout the world as supporting devices for civil engineering structures such as bridges and tunnels. The condition monitoring of ground anchorages is a new area of research, with the long term objective being a wholly automated or semi-automated condition monitoring system capable of repeatable and accurate diagnosis of faults and anchorage post-tension levels. The ground anchorage integrity testing (GRANIT) system operates by applying an impulse of known force by means of an impact device that is attached to the tendon of the anchorage. The vibration signals that arise from this impulse are complex in nature and require analysis to be undertaken in order to extract information from the vibrational response signatures that is relevant to the condition of the anchorage. Novel artificial intelligence techniques are used in order to learn the complicated relationship that exists between an anchorage and its response to an impulse. The system has a worldwide patent and is currently licensed commercially.A lumped parameter dynamic model has been developed which is capable of describing the general frequency relationship with increasing post-tension level as exhibited by the signals captured from real anchorages. The normal procedure with the system is to train a neural network on data that has been taken from an anchorage over a range of post-tension levels. Further data is needed in order to test the neural network. This process can be time consuming, and the lumped parameter dynamic model has the potential of producing data that could be used for training purposes, thereby reducing the amount of time needed on site, and reducing the overall cost of the system's operation.This paper presents data that has been produced by the lumped parameter dynamic model and compares it with data from a real anchorage. Noise is added to the results produced by the lumped parameter dynamic model in order to match more closely the experimental data. A neural network is trained on the data produced by the model, and the results of diagnosis of real data are presented. Problems are encountered with the diagnosis of the neural network with experimental data, and a new method for the training of the neural network is explored. The improved results of the neural network trained on data produced by the lumped parameter dynamic model to experimental data are shown. It is shown how the results from the lumped parameter dynamic model correspond well to the experimental results.     

Positive Effect of Flow Velocity on Gas–Condensate Relative Permeability: Network Modelling and Comparison with Experimental Results

Positive velocity dependency of relative permeability of gas–condensate systems, which has been observed in many different core experiments, is now well acknowledged. The above behaviour, which is due to two-phase flow coupling in condensing systems at low interfacial tension (IFT) conditions, was simulated using a 3D pore network model. The steady-dynamic bond network model developed for this purpose was also equipped with a novel anchoring technique, which was based on the equivalent hydraulic length concept adopted from fluid flow through pipes. The available rock data on the co-ordination number, capillary pressure, absolute permeability, porosity and one set of measured relative permeability curves were utilised to anchor the capillary, volumetric and flow characteristics of the constructed network model to those properties of the real core sample. Then the model was used to predict the effective permeability values at other IFT and velocity levels. There is a reasonable quantitative agreement between the predicted and measured relative permeability values affected by the coupling rate effect.     

Decomposing <Emphasis Type="Italic">J</Emphasis>-function to Account for the Pore Structure Effect in Tight Gas Sandstones

The J-function predicts the capillary pressure of a formation by accounting for its transport properties such as permeability and porosity. The dependency of this dimensionless function on the pore structure is usually neglected because it is difficult to implement such dependency, and also because most clastic formations contain mainly one type of pore structure. In this paper, we decompose the J-function to account for the presence of two pore structures in tight gas sandstones that are interpreted from capillary pressure measurements. We determine the effective porosity, permeability, and wetting phase saturation of each pore structure for this purpose. The throats, and not the pores, are the most important parameter for this determination. We have tested our approach for three tight gas sandstones formations. Our study reveals that decomposing the J-function allows us to capture drainage data more accurately, so that there is a minimum scatter in the scaled results, unlike the traditional approach. This study can have major implications for understanding the transport properties of a formation in which different pore structures are interconnected.     

Characterization of Single-Phase Flow Through Carbonate Rocks: Quantitative Comparison of NMR Flow Propagator Measurements with a Realistic Pore Network Model

We report here the quantitative comparisons between the measured NMR flow propagator of a carbonate rock and the flow propagator calculated with a porous network extracted from the micro-CT image of the twin plug. We developed a numerical model based on a particle tracking algorithm in pore space. The particle tracking in throats is described using the first arrival time distribution. As pores have an important volume fraction in the sample considered, we implemented a time-delay mechanism for particle transport in the pores. We consider that the nodes have volume and there is a transport of the tracking particles inside the nodes, which leads to an “apparent” time-delay. Simulations of flow propagator show good agreement with low field NMR experiments performed on the twin plug of the sample used for pore network extraction with a single adjustable parameter (that describes the dynamics in the pores). These results lead us to a better understanding of the connection between pore structure and the behavior of NMR flow propagator in fluid-saturated rocks and are essential in interpreting the experimental data and correlating NMR parameters to petrophysical properties.