Zeyun Jiang

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.

Multiscale pore-network representation of heterogeneous carbonate rocks

A multi-scale network integration approach introduced by Jiang et al. [2013] is used to generate a representative pore-network for a carbonate rock with a pore-size distribution across several orders of magnitude. We predict the macroscopic flow parameters of the rock utilising i) 3D images captured by X-ray computed micro-tomography and ii) pore-network flow simulations. To capture the multi-scale pore-size distribution of the rock we imaged four different rock samples at different resolutions and integrated the data to produce a pore-network model that combines information at several length-scales that cannot be recovered from a single tomographic image. A workflow for selection of the number and length-scale of the required input networks for the network integration process, as well as fine tuning the model parameters is presented. Mercury injection capillary-pressure data were used to evaluate independently the multi-scale networks. We explore single-scale, two-scale, and three-scale network models and discuss their representativeness by comparing simulated capillary-pressure versus saturation curves with laboratory measurements. We demonstrate that for carbonate rocks with wide pore-size distributions, it may be required to integrate networks extracted from two or three discrete tomographic data sets in order to simulate macroscopic flow parameters. This article is protected by copyright. All rights reserved.

Microbial carbonates: a sampling and measurement challenge for petrophysics addressed by capturing the bioarchitectural components

Abstract Ancient and modern stromatolites are potentially a challenge for petrophysicists when characterizing biosediments of microbial origin. Because of the heterogeneity, sometimes very cemented and lacking porosity, sometimes highly porous, these widely differing states can be used to develop techniques that can have wider application to addressing the representative elementary volume (REV – single or multiple REVs) challenge in microbial carbonates. Effective media properties – like porosity – need to be defined on REV scales and the challenge is that this scale is often close to or significantly larger than the traditional core plugs on which properties are traditionally measured. A combination of outcrop images, image analysis techniques, micro-computed tomography (CT) and modelling have been used to capture the porosity (or in some cases, precursor porosity) architecture and provide a framework for estimating petrophysical property sensitivities in a range of situations that can be subjected to further calibration by measurements in relevant microbial reservoir rocks. This work will help guide the sampling approach along with the interpretation and use of petrophysical measurements from microbial carbonates. The bioarchitectural component, when controlling porosity in microbial carbonates, presents a significant challenge as the REV scale is often much larger than core plugs, requiring careful screening of existing data and measurement and additional geostatistical model-based approaches (with further calibration).

A new method of fast distance transform 3D image based on “neighborhood between voxels in space” theory

Digital core technology is a new type of tool for analyzing and explaining the flow characteristics and fluid distribution of reservoir. Digital core technology has been widely used in recent years to describe features of pose space and simulate the process of fluid flow. As the basement of segmentation of pore space and reconstruction of pore network, the improvement of distance transform method has great impact on the development of digital core analysis technology. The accuracy and computation speed of distance transform method can directly affect the size of digital data and the detailedness of pore network model. Euclidean distance transform is the most precise one among all the distance transform methods, which means it is suitable for processing digital core data and calculating distance map. For traditional Euclidean distance transform method application in three-dimensional space data, there exist problems, such as too many search directions, large amount of data, and time-consuming. Large-scale data of digital core is hard to be transformed by this method. Therefore, a new theory of space based geometric topology neighbor relationship distance search algorithm was proposed in this paper. By introducing theory of neighborhood in 3D space, the relationship between 3 ´ 3 ´ 3 neighborhood with whole core data can be constructed, the computational area is greatly narrowed so that computation speed can be improved markedly. Then, instead of calculating every distance between pore voxels and skeleton voxels, the Euclidean distance of a pore voxel can be obtained by scanning the distance value of its 3 ´ 3 ´ 3 neighborhood. Exact Euclidean distance map of digital core data includes large-scale data showed after only two-scans. Noteworthily, due to the disturbing of boundary points which out the range of data size, special treatment is needed to process the pore voxels which near boundary of digital core data. Compared to existing methods, according to the interior of the rock pore structure characteristics, we simplified the comparison rules of the neighbor domain Euclidean distance value so that we can significantly improve computing capacity and computation speed of the Euclidean distance transform method. By this way, a large number of operations by the complex Euclidean distance structure can be avoided. And the complexity of the algorithm is better understood and applied. This article describes the process of the algorithm in detail and the method is extended to characterize pore space segmentation work of digital cores. Fractured-cave digital core, fractured digital core and kinds of digital core data were transformed by the new method, the results show that the method is more accurate and efficient for the segmentation and reconstruction of pore space model. On this basis, pore structure characteristics of pore space can be extracted and analyzed by digital core technology, and more parameters like permeability, formation factor, and so on, can be simulated. This paper created a new distance transform method on digital rock identification and extraction, which laid a theoretical foundation for the efficient development of microscopic description of oil and gas reservoirs, especially for fractured and vuggy reservoirs.

Multi-scale Pore-network Modelling of WAG in Carbonates

Carbonate reservoirs have textural heterogeneities at all length-scales (triple porosity: pore-vug-fracture) and tend to be mixed- to oil-wet. The choice of an enhanced oil recovery process and the prediction of oil recovery require a sound understanding of the fundamental controls on fluid flow in mixed- to oil-wet carbonate rocks, as well as physically robust flow functions, i.e. relative permeability and capillary pressure functions. Obtaining these flow functions is a challenging task, especially when three fluid phases coexist, such as during water-alternating-gas injection (WAG). We have recently developed a method for integration of pore-networks derived from micro CT images at different length-scales, thus capturing pore structures from different types of porosity. The network integration method honours the connectivity between different pore types, including micro-fractures, and their spatial distribution. In this work, we use these multi-scale networks as input for our three-phase flow pore-network model, which comprises a novel thermodynamic criterion for formation and collapse of oil layers that strongly depends on the fluid spreading behaviour and the rock wettability. The criterion affects in particular the oil relative permeability at low oil saturations and the accurate prediction of residual oil saturations. We generate three-phase flow functions for gas injection and WAG from networks with carbonate pore geometries and connectivities and we demonstrate the impact on residual saturations of the different types of porosity and the interaction with different realistic wettability scenarios. We also show that the network generated three-phase flow relative permeabilities are distinctly different from traditional models, such as Stone’s. The flow functions will be used in a heterogeneous carbonate reservoir model and to demonstrate their impact on the sweep efficiency.

A Pore-Skeleton-Based Method for Calculating Permeability and Capillary Pressure

AbstractWe have developed a new method for calculating permeability and capillary pressure from the pore skeleton that is extracted from a fractured rock model, which might comprises medial axes of matrix pores and/or medial surfaces of fracture voids. Such a skeleton, therefore, is able to encapsulate the total connected fluid flow paths in the pore-void space. To do pore-network flow simulations, the pore skeleton needs to be further “discretised” into a network of interconnected nodes and bonds to capture local pore morphology. Jiang et al. (Adv Water Resour 107:280–289, 2017) developed a method to extract pore skeletons of this type and a discretisation to construct a pore-network model that is optimal in many aspects. In this work, we develop a new in-place discretisation method, by simply inserting a virtual link, a bond, between every pair of skeleton voxels, nodes, which are either face or only edge adjacent under certain conditions. This new method results in a simpler pore-network model, i.e. a virtual network, in which each node or bond is assumed as either a cylinder or a tiny fracture, as well as prescribed with length and inscribed radius/aperture only. As a result, a simpler pore-network simulator is also developed using improved formulae of conductance and capillary pressure according to where each virtual link falls, appropriately distinguishing every local configuration within matrixes or fractures. We verify our methods by comparing the simulation results against with those of lattice Boltzmann methods and a laboratory flooding experiment and demonstrate the accuracy and efficiency of our methods with sensitivity analysis.

An investigation into preserving spatially-distinct pore systems in multi-component rocks using a fossiliferous limestone example

Abstract Limestones containing abundant disc-shaped fossil Nummulites can form significant hydrocarbon reservoirs but they have a distinctly heterogeneous distribution of pore shapes, sizes and connectivities, which make it particularly difficult to calculate petrophysical properties and consequent flow outcomes. The severity of the problem rests on the wide length-scale range from the millimetre scale of the fossils pore space to the micron scale of rock matrix pores. This work develops a technique to incorporate multi-scale void systems into a pore network, which is used to calculate the petrophysical properties for subsequent flow simulations at different stages in the limestones petrophysical evolution. While rock pore size, shape and connectivity can be determined, with varying levels of fidelity, using techniques such as X-ray computed tomography (CT) or scanning electron microscopy (SEM), this work represents a more challenging class where the rock of interest is insufficiently sampled or, as here, has been overprinted by extensive chemical diagenesis. The main challenge is integrating multi-scale void structures derived from both SEM and CT images, into a single model or a pore-scale network while still honouring the nature of the connections across these length scales. Pore network flow simulations are used to illustrate the technique but of equal importance, to demonstrate how supportable earlier-stage petrophysical property distributions can be used to assess the viability of several potential geological event sequences. The results of our flow simulations on generated models highlight the requirement for correct determination of the dominant pore scales (one plus of nm, μm, mm, cm), the spatial correlation and the cross-scale connections.

Micropore network modelling from 2D confocal imagery: impact on reservoir quality and hydrocarbon recovery

Microporosity in carbonate reservoirs is globally pervasive and commonly used to explain high-porosity, low-permeability reservoirs, higher than expected water saturations, low resistivity pay zones and poor sweep efficiency. The potential for micropores to store and produce hydrocarbons has long been recognized, yet limitations on tools to evaluate microporosity has prevented rigorous evaluation. Here we demonstrate a workflow for evaluating microporosity through a combination of laser scanning confocal microscopy (LSCM) and pore network modelling. Specific values for microporosity and permeability calculated in our study should not be applied explicitly, as these are simulated values, but they demonstrate the viability of micropore networks to store and flow hydrocarbons. Carbonate reservoir assessment is critical not only in the petroleum industry, but also for applications in hydrothermal and mineral resources, carbon capture and storage, and groundwater supply. This approach can be applied to understand the potential for any reservoir to hold and transmit fluids.

Visualisation of multiphase fluid displacement at pore scale in carbonate media using x-ray microtomography

The application of high resolution x-ray computed tomography in conjunction with x-ray transparent core holders and representative volumes of porous carbonate media enable exceptionally high quality imaging of fluid displacement processes. The experiments show that the rock is water wet. This in turn is in agreement with what was expected prior to running the experiments since the rock was a clean outcrop and has never been exposed to hydrocarbon phases. Moreover, major fluid displacement mechanisms being used in pore scale simulations including piston like displacement and snap off have been captured clearly. The experiments have characterized the transport features and the structure of residual oil present in a water wet system. Subsequent experiments will focus on wettability alteration to develop oil wet surfaces for direct comparison. The on-going phase of this study will focus on building multiple scale network models and using them to run fluid displacement simulations. These models will be validated against experimental data such as MICP and relative permeability measurements.

Compaction and Diagenesis during Burial - Predicting Permeability Trends with Depth

Porosity and permeability of carbonate sediments evolve markedly with burial depth, reflecting the combined effects of mechanical compaction, chemical compaction, dissolution and cementation. While trends in porosity change with depth can be qualified, the evolution of permeability remains problematic. Here, we create a theoretical series of 2D images of major pore-occluding and pore enhancing diagenetic processes linked to their depth of occurrence. These images were then used to create 3D pore architecture models using Markov Chain Monte Carlo simulation, from which pore network were extracted to obtain multiphase fluid flow properties. The modelled porosity and permeability evolution from three different diagenetic pathways display several tipping points where the decrease in permeability is significantly larger than the associated drop in porosity. Such diagenetic pathway models can provide constraints on the predicted behaviour of carbonates during burial and/or uplift scenarios.