Browsing by Author "Fan, Ming"

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    Micrometer-scale Experimental Characterization of the Lower Huron Shale in the Central Appalachian Basin
    Tan, Xinyu; Gilliland, Ellen; Tang, Xu; Fan, Ming; Ripepi, Nino (American Geophysical Union, 2020)
    The mechanical properties of shale play an important role in hydraulic fracturing design. Although the popular nanoindentation method can be performed to evaluate some mechanical characteristics of organic matter, it is still difficult to fully characterize mechanical properties of organic components of shale due to their small scale which is usually on the order of micrometers or even nanometers. As a novel material characterization tool, Atomic Force Microscopy (AFM) has shown great potential to characterize surface properties and pore structures at micrometer- and nanometer-scale and has been applied to investigate the elastic properties of organic components in shale by multiple researchers. Raman and FTIR can detect chemical bands by utilizing molecular vibration information. Because Raman and FTIR measurements are non-destructive, high sensitivity, and short in duration, they have been used extensively to study maturation processes of organic components in coal and shale samples. To some extent, these two methods can be considered as complementary to each other, and more comprehensive understanding about maturation processes of organic components can be achieved by combining these two methods. In this work, mechanical properties and chemical characteristics of four shale samples with different thermal maturities were investigated. Generally, this study had two objectives: (1) Characterize the mechanical properties of shale samples with different maturity levels through the novel AFM method, and (2) Explore the underlying cause for the change in elastic properties of shale samples from a chemical perspective through the complementary Raman and FTIR methods.
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    Pore-scale Study of Flow and Transport in Energy Georeservoirs
    Fan, Ming (Virginia Tech, 2019-07-22)
    Optimizing proppant pack conductivity and proppant-transport and -deposition patterns in a hydraulic fracture is of critical importance to sustain effective and economical production of petroleum hydrocarbons. In this research, a numerical modeling approach, combining the discrete element method (DEM) with the lattice Boltzmann (LB) simulation, was developed to provide fundamental insights into the factors regulating the interactions between reservoir depletion, proppant-particle compaction and movement, single-/multiphase flows and non-Darcy flows in a hydraulic fracture, and fracture conductivity evolution from a partial-monolayer proppant concentration to a multilayer proppant concentration. The potential effects of mixed proppants of different sizes and types on the fracture conductivity were also investigated. The simulation results demonstrate that a proppant pack with a smaller diameter coefficient of variation (COV), defined as the ratio of standard deviation of diameter to mean diameter, provides better support to the fracture; the relative permeability of oil was more sensitive to changes in geometry and stress; when effective stress increased continuously, oil relative permeability increased nonmonotonically; the combination of high diameter COV and high effective stress leads to a larger pressure drop and consequently a stronger non-Darcy flow effect. The study of proppant mixtures shows that mixing of similar proppant sizes (mesh-size-20/40) has less influence on the overall fracture conductivity than mixing a very fine mesh size (mesh-size-100); selection of proppant type is more important than proppant size selection when a proppant mixture is used. Increasing larger-size proppant composition in the proppant mixture helps maintain fracture conductivity when the mixture contains lower-strength proppants. These findings have important implications to the optimization of proppant placement, completion design, and well production. In the hydraulic-mechanical rock-proppant system, a fundamental understanding of multiphase flow in the formation rock is critical in achieving sustainable long-term productivity within a reservoir. Specifically, the interactions between the critical dimensionless numbers associated with multiphase flow, including contact angle, viscosity ratio, and capillary number (Ca), were investigated using X-ray micro computed tomography (micro-CT) scanning and LB modeling. The primary novel finding of this study is that the viscosity ratio affects the rate of change of the relative permeability curves for both phases when the contact angle changes continuously. Simulation results also indicate that the change in non-wetting fluid relative permeability was larger when the flow direction was switched from vertical to horizontal, which indicated that there was stronger anisotropy in larger pore networks that were primarily occupied by the non-wetting fluid. This study advances the fundamental understanding of the multiphysics processes associated with multiphase flow in geologic materials and provides insight into upscaling methodologies that account for the influence of pore-scale processes in core- and larger-scale modeling frameworks. During reservoir depletion processes, reservoir formation damage is an issue that will affect the reservoir productivity and various phases in fluid recovery. Invasion of formation fine particles into the proppant pack can affect the proppant pack permeability, leading to potential conductivity loss. The combined DEM-LB numerical framework was used to evaluate the role of proppant particle size heterogeneity (variation in proppant particle diameter) and effective stress on the migration of detached fine particles in a proppant supported fracture. Simulation results demonstrate that a critical fine particle size exists: when a particle diameter is larger or smaller than this size, the deposition rate increases; the transport of smaller fines is dominated by Brownian motion, whereas the migration of larger fines is dominated by interception and gravitational settling; this study also indicates that proppant packs with a more heterogeneous particle-diameter distribution provide better fines control. The findings of this study shed lights on the relationship between changing pore geometries, fluid flow, and fine particle migration through a propped hydraulic fracture during the reservoir depletion process.
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    Radiogenomic signatures reveal multiscale intratumour heterogeneity associated with biological functions and survival in breast cancer
    Fan, Ming; Xia, Pingping; Clarke, Robert; Wang, Yue; Li, Lihua (2020-09-25)
    Advanced tumours are often heterogeneous, consisting of subclones with various genetic alterations and functional roles. The precise molecular features that characterize the contributions of multiscale intratumour heterogeneity to malignant progression, metastasis, and poor survival are largely unknown. Here, we address these challenges in breast cancer by defining the landscape of heterogeneous tumour subclones and their biological functions using radiogenomic signatures. Molecular heterogeneity is identified by a fully unsupervised deconvolution of gene expression data. Relative prevalence of two subclones associated with cell cycle and primary immunodeficiency pathways identifies patients with significantly different survival outcomes. Radiogenomic signatures of imaging scale heterogeneity are extracted and used to classify patients into groups with distinct subclone compositions. Prognostic value is confirmed by survival analysis accounting for clinical variables. These findings provide insight into how a radiogenomic analysis can identify the biological activities of specific subclones that predict prognosis in a noninvasive and clinically relevant manner. Tumours are made up of heterogeneous subclones. Here, the authors show using breast cancer imaging and gene expression datasets that these subclones can be inferred by the deconvolution of gene expression data, mapped to MRI derived radiogenomic signatures and used to estimate prognosis.
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    Tumour heterogeneity revealed by unsupervised decomposition of dynamic contrast-enhanced magnetic resonance imaging is associated with underlying gene expression patterns and poor survival in breast cancer patients
    Fan, Ming; Xia, Pingping; Liu, Bin; Zhang, Lin; Wang, Yue; Gao, Xin; Li, Lihua (2019-10-17)
    Background Heterogeneity is a common finding within tumours. We evaluated the imaging features of tumours based on the decomposition of tumoural dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data to identify their prognostic value for breast cancer survival and to explore their biological importance. Methods Imaging features (n = 14), such as texture, histogram distribution and morphological features, were extracted to determine their associations with recurrence-free survival (RFS) in patients in the training cohort (n = 61) from The Cancer Imaging Archive (TCIA). The prognostic value of the features was evaluated in an independent dataset of 173 patients (i.e. the reproducibility cohort) from the TCIA I-SPY 1 TRIAL dataset. Radiogenomic analysis was performed in an additional cohort, the radiogenomic cohort (n = 87), using DCE-MRI from TCGA-BRCA and corresponding gene expression data from The Cancer Genome Atlas (TCGA). The MRI tumour area was decomposed by convex analysis of mixtures (CAM), resulting in 3 components that represent plasma input, fast-flow kinetics and slow-flow kinetics. The prognostic MRI features were associated with the gene expression module in which the pathway was analysed. Furthermore, a multigene signature for each prognostic imaging feature was built, and the prognostic value for RFS and overall survival (OS) was confirmed in an additional cohort from TCGA. Results Three image features (i.e. the maximum probability from the precontrast MR series, the median value from the second postcontrast series and the overall tumour volume) were independently correlated with RFS (p values of 0.0018, 0.0036 and 0.0032, respectively). The maximum probability feature from the fast-flow kinetics subregion was also significantly associated with RFS and OS in the reproducibility cohort. Additionally, this feature had a high correlation with the gene expression module (r = 0.59), and the pathway analysis showed that Ras signalling, a breast cancer-related pathway, was significantly enriched (corrected p value = 0.0044). Gene signatures (n = 43) associated with the maximum probability feature were assessed for associations with RFS (p = 0.035) and OS (p = 0.027) in an independent dataset containing 1010 gene expression samples. Among the 43 gene signatures, Ras signalling was also significantly enriched. Conclusions Dynamic pattern deconvolution revealed that tumour heterogeneity was associated with poor survival and cancer-related pathways in breast cancer.