Lu, Xiao (The University of New South Wales) | Armstrong, Ryan (The University of New South Wales) | Yuan, Meng (The University of New South Wales) | Zhang, Yulai (The University of New South Wales) | Mostaghimi, Peyman (The University of New South Wales)
Coalbed methane (CBM), also known as coal seam gas, is becoming an increasingly important energy resource in the global natural gas market. Gas transport in CBM reservoirs remains a crucial research topic that has not been fully understood. Two scales of gas flow are identified in coal cores: flow in fractures and diffusion within matrix. The diffusion process is quantified by the gas diffusion coefficient while flow in fractures is governed by fracture apertures. This paper aims to explore the diffusion process in coal using X-ray microcomputed tomography (micro-CT) imaging. The experiments are conducted at 100 psi effective stress to eliminate the impact of pressure. The images obtained are registered for visualisation and analysis of the diffusion process and comparisons of fracture. In the paper, the impact of increasing effective stress on fracture aperture is demonstrated. Also, the diffusion coefficient of Krypton in coal matrix is estimated and discussed.
We present the DigiCoal toolbox that is an integrated set of numerical functions written in Matlab, dedicated to analyse 3D computed tomography (CT) images of coal and reconstructing representative digital models. The design is based on a comprehensive framework: CT image pre-processing, statistics extraction, digital coal modelling and structural analysis. This paper offers an overview of the structure and techniques used in the creation of the toolbox, together with code snippets and examples.
Fogden, Andrew (FEI Oil &Gas) | Arena, Alessio (FEI Oil & Gas) | Zhang, Christopher (FEI Oil &Gas) | Carnerup, Anna (FEI Oil & Gas) | Goergen, Eric (FEI Oil& Gas) | Olson, Terri (FEI Oil & Gas) | Cheng, Qianhao (Australian National University) | Middleton, Jill (Australian National University) | Kingston, Andrew (Australian National University) | Zhang, Yulai (University of New South Wales) | Armstrong, Ryan (University of New South Wales)
Optimization of production from shale reservoirs requires understanding of rock properties over a range of scales. Multiple imaging techniques can be combined to determine the nature, connectivity, and wettability of nano-scale pore systems as well as the underlying mineralogy and organic textures that control reservoir behavior and the propensity of the matrix to fail and to contain expulsion cracks. The current study demonstrates new capabilities in integrated multiscale and time-resolved imaging and analysis workflows for three organic-rich shale samples from two formations.
The spatial distributions of connected porosity, organic matter, and microfractures within vertical sub-plugs were quantified from micro-CT imaging, using X-ray contrast enhancement strategies to detect their volume contributions from sub-resolution features, together with tomogram alignment and segmentation. These registered 3D volume distributions comprising billions of voxels showed that most of the porosity in these three samples was hosted by organic matter and most of the coring-induced fractures ran through laminations of locally higher organic content. Dynamic micro-CT imaging was also performed to directly visualize the progress of liquid-liquid diffusion through the pore space. The imaged concentration profiles were fitted to models to estimate the average in-plane diffusivity coefficient.
This tomographic analysis was validated and complemented by automated high-resolution 2D back-scattered SEM (BSEM) and SEM-EDS imaging and mapping of pores, organic matter and mineralogy over ion-milled sub-plug sections, and registration of these image mosaics into the corresponding tomogram cross-section. In this way, information on the fine scale of individual features could be combined with statistics over the more representative tomogram volumes. The distribution of organic matter was characterized from this 2D BSEM together with 3D FIBSEM imaging. The majority of organic-hosted connected pores detected by contrast-enhanced micro-CT lay below BSEM and FIBSEM resolution. Secondary electron SEM images (using FESEM) of raw broken surfaces revealed the relatively homogeneous texture of the sub-10 nm pore network permeating the fused aggregates of bitumen nano-granules. Further, the same contrast technique used to highlight bitumen in the tomograms was also applied to ion-milled sections to extend the automated BSEM imaging coupled with SEM-EDS mapping to distinguish bitumen from kerogen at high resolution.
Fogden, Andrew (Department of Applied Mathematics, Australian National University) | Olson, Terri (FEI, Denver) | Cheng, Qianhao (Department of Applied Mathematics, Australian National University) | Middleton, Jill (Department of Applied Mathematics, Australian National University) | Kingston, Andrew (Department of Applied Mathematics, Australian National University) | Turner, Michael (Department of Applied Mathematics, Australian National University) | Sheppard, Adrian (Department of Applied Mathematics, Australian National University) | Armstrong, Ryan (School of Petroleum Engineering, University of New South Wales)
Diffusion is a key mechanism of gas recovery from unconventional reservoirs. Spatial and temporal monitoring of the progress of diffusion would also aid in characterizing the local variations in pore size and tortuosity, of broad relevance to all unconventionals and transport processes. In this study the technique recently developed for contrast- enhanced, micro-CT static imaging of the pore volume distribution in shale plugs, after saturation of their accessible pore space with the X-ray opaque liquid diiodomethane (CH2I2), was extended to dynamic imaging of diffusion over these micron-centimeter scales. The CH2I2-saturated plug was immersed in the miscible, X-ray transparent liquid toluene, for diffusion to be monitored by continuous micro-CT scanning via the progressive advance in attenuation loss. This sequence of dynamic tomograms was then spatially registered to the static tomograms of the dry state and the initial CH2I2-saturated state to visualize and quantify the CH2I2 attenuation decrease at each voxel over time. Results are presented for vertically cored, cleaned sub-plugs of 3-12 mm diameter of two shale gas and one shale oil samples.
Koroteev, Dmitry Anatolyevich (Schlumberger) | Dinariev, Oleg (Schlumberger) | Evseev, Nikolay (Schlumberger) | Klemin, Denis Vladimirovich (Schlumberger R&D Inc.) | Safonov, Sergey (Schlumberger) | Gurpinar, Omer M. (Schlumberger) | Berg, Steffen (Shell Global Solutions International BV) | vanKruijsdijk, Cor (Shell) | Myers, Michael (Shell) | Hathon, Lori Andrea (Shell International E&P Co.) | de Jong, Hilko (Shell Oil Co.) | Armstrong, Ryan (Shell)
Fast and reliable EOR process selection is a critical step in any EOR project. The digital rock (DR) approach jointly developed by Shell and SLB is aimed to be the smallest scale yet advanced EOR Pilot technology. In this document, we describe the application of DR technology for screening of different EOR mechanisms at pore-scale focused to enhance recovery from a particular reservoir formation. For EOR applications DR brings unique capabilities as it can fully describe different multiphase flow properties at different regimes.
The vital part of the proposed approach is the high-efficient pore-scale simulation technology called Direct Hydrodynamics (DHD) Simulator. DHD is based on a density functional approach applied for hydrodynamics of complex systems. Currently, DHD is benchmarked against multiple analytical solutions and experimental tests and optimized for high performance (HPC) computing. It can handle many physical phenomena: multiphase compositional flows with phase transitions, different types of fluid-rock and fluid-fluid interactions with different types of fluid rheology. As an input data DHD uses 3D pore texture and composition of rocks with distributed micro-scale wetting properties and pore fluid model (PVT, rheology, diffusion coefficients, and adsorption model). In a particular case, the pore geometry comes from 3D X-ray microtomographic images of a rock sample. The fluid model is created from lab data on fluid characterization. The output contains the distribution of components, velocity and pressure fields at different stages of displacement process. Several case studies are demonstrated in this work and include comparative analysis of effectiveness of applications of different chemical EOR agents performed on digitized core samples.