Discrete element modelling of hydraulic fracture interaction with natural fractures in shale formations

Research presented in this paper aimed at establishing a better understanding of natural fracture (NF)/hydraulic fracture (HF) interaction mechanisms and fracture network development in naturally fractured and nonhomogeneous shale formations through numerical modelling using the two-dimensional particle flow code (PFC2D). Hydraulic fracture propagation was firstly modelled in a 30 m x 30 m model representing intact rock by bonded particle method (BPM), which served as a base case in the research. Then a single, deterministic natural fracture was embedded into the same model by a smooth joint contact model (SJM) to investigate different NF/HF interaction mechanisms under a range of different conditions by varying the angle of approach, differential horizontal stress, and the mechanical properties of a fracture within the model. Based on the parametric research findings, number and diversity of natural fractures in the model were increased both deterministically and stochastically, and the results are compared and discussed

Gas-driven rapid fracture propagation and gas outbursts under unloading conditions in coal seams

Copyright © 2018 ARMA, American Rock Mechanics Association. Coal and gas outbursts have long posed a serious risk to safe and efficient production in coal mines. It is recognised that the coal and gas outbursts are triggered by excavation unloading followed by gas-driven rapid propagation of a system of preexisting or mining-induced fractures. Gas-filled fractures parallel to a working face are likely to experience opening first, then expansion and rapid propagation stages under unloading conditions. This research aimed to identify the key factors affecting outburst initiation and its temporal evolution during roadway developments. Specifically, the response of pre-set fractures in a coal seam sandwiched between rock layers to roadway development is simulated using a geomechanical model coupled with fracture mechanics for fracture opening and propagation. In addition, kinetic gas desorption and its migration into open fractures is considered. During simulations outburst is deemed to occur when the fracture length exceeds the dimension of a host element. The findings of this research suggests that the simulated coal and gas outburst may be considered as a dynamic gas desorption-driven fracture propagation process. The occurrence of coal and gas outbursts is found to be influenced mainly by the coal properties, fracture attributes, and initial gas pressure and the in situ stress conditions

Multiple-panel longwall top coal caving induced microseismicity: Monitoring and development of a statistical forecasting model for hazardous microseismicity

Continuous microseismic monitoring was carried out around 9 producing longwall top coal caving (LTCC) panels with concurrently recorded daily face advance rates at Coal Mine Velenje in Slovenia over a 27-month monitoring period. The monitoring results suggested that spatial and magnitude characteristics of microseismicity are dominated by those of underlying fractures, while microseismic event rate is under the combined effects of local natural fracture abundance and mining intensity. On this basis, a data-driven yet physics-based forecasting methodology was established for LTCC induced hazardous microseismicity, which is above a given threshold of energy magnitude and within a certain distance to the longwall face. Statistical analyses were first conducted to characterise temporal, magnitude and spatial characteristics of long-term recorded microseismicity, based on which a short-term forecasting model was developed to calculate the probability of hazardous microseismicity considering the three characteristics. The model developed was employed to forecast the likelihood of hazardous microseismicity at one of these LTCC panels, and the forecasted results were supported by the monitoring. This statistical model has important implications in the evaluation of mining-induced hazards, and it can be used to optimise longwall face advance rates to minimise the risk of hazardous microseismicity in burst-prone deep-level mining sites

Parametric analysis of slotting operation induced failure zones to stimulate low permeability coal seams

The main constrain for effective gas drainage in coal mines is the low permeability nature of coal reservoirs. As coal mining activities are extending to deeper subsurface, the ever-increasing in situ stress conditions is anticipated to result in much lower permeability and more challenges for gas emission control in coal mines. In recent years, hydraulic slotting using high-pressure waterjet along underground gas drainage boreholes, as a general solution to stimulate low permeability coal seams, has become increasingly favourable. This paper presents a systematic investigation into the sensitivity of borehole slotting performance to a number of field and operational parameters. A wide range of geomechanical properties, in situ stress conditions, slot geometry and spacing of multiple slots were considered in a series of numerical simulations. The relations between these key parameters and the failure zone size/volume induced by slotting were quantified. The effect of different parameters in improving slotting performance has also been ranked, which provides theoretical base for mine operators to optimise slotting operations

Development of fractal-fuzzy evaluation methodology and its application for seismic hazards assessment using microseismic monitoring in coal mining

Seismic hazards have become one of the common risks in underground coal mining and their assessment is an important component of the safety management. In this study, a methodology, involving nine fractal dimension-based indices and a fuzzy comprehensive evaluation model, has been developed based on the processed real time microseismic data from an underground coal mine, which allows for a better and quantitative evaluation of the likelihood for the seismic hazards. In the fuzzy model, the membership function was built using a Gaussian shape and the weight of each index was determined using the performance metric F score derived from the confusion matrix. The assessment results were initially characterised as a probability belonging to each of four risk levels (none, weak, moderate and strong). The comprehensive result was then evaluated by integrating the maximum membership degree principle (MMDP) and the variable fuzzy pattern recognition (VFPR). The model parameters of this methodology were first calibrated using historical microseismic data over a period of seven months at Coal Mine Velenje in Slovenia, and then applied to analyse more recent microseismic monitoring data. The results indicate that the calibrated model was able to assess seismic hazards in the mine

Depression and anxiety in relation to cancer incidence and mortality: a systematic review and meta-analysis of cohort studies

The link between depression and anxiety status and cancer outcomes has been well-documented but remains unclear. We comprehensively quantified the association between depression and anxiety defined by symptom scales or clinical diagnosis and the risk of cancer incidence, cancer-specific mortality, and all-cause mortality in cancer patients. Pooled estimates of the relative risks (RRs) for cancer incidence and mortality were performed in a meta-analysis by random effects or fixed effects models as appropriate. Associations were tested in subgroups stratified by different study and participant characteristics. Fifty-one eligible cohort studies involving 2,611,907 participants with a mean follow-up period of 10.3 years were identified. Overall, depression and anxiety were associated with a significantly increased risk of cancer incidence (adjusted RR: 1.13, 95% CI: 1.06–1.19), cancer-specific mortality (1.21, 1.16–1.26), and all-cause mortality in cancer patients (1.24, 1.13–1.35). The estimated absolute risk increases (ARIs) associated with depression and anxiety were 34.3 events/100,000 person years (15.8–50.2) for cancer incidence and 28.2 events/100,000 person years (21.5–34.9) for cancer-specific mortality. Subgroup analyses demonstrated that clinically diagnosed depression and anxiety were related to higher cancer incidence, poorer cancer survival, and higher cancer-specific mortality. Psychological distress (symptoms of depression and anxiety) was related to higher cancer-specific mortality and poorer cancer survival but not to increased cancer incidence. Site-specific analyses indicated that overall, depression and anxiety were associated with an increased incidence risks for cancers of the lung, oral cavity, prostate and skin, a higher cancer-specific mortality risk for cancers of the lung, bladder, breast, colorectum, hematopoietic system, kidney and prostate, and an increased all-cause mortality risk in lung cancer patients. These analyses suggest that depression and anxiety may have an etiologic role and prognostic impact on cancer, although there is potential reverse causality; Furthermore, there was substantial heterogeneity among the included studies, and the results should be interpreted with caution. Early detection and effective intervention of depression and anxiety in cancer patients and the general population have public health and clinical importance

Over-Fitting in Model Selection with Gaussian Process Regression

Model selection in Gaussian Process Regression (GPR) seeks to determine the optimal values of the hyper-parameters governing the covariance function, which allows flexible customization of the GP to the problem at hand. An oft-overlooked issue that is often encountered in the model process is over-fitting the model selection criterion, typically the marginal likelihood. The over-fitting in machine learning refers to the fitting of random noise present in the model selection criterion in addition to features improving the generalisation performance of the statistical model. In this paper, we construct several Gaussian process regression models for a range of high-dimensional datasets from the UCI machine learning repository. Afterwards, we compare both MSE on the test dataset and the negative log marginal likelihood (nlZ), used as the model selection criteria, to find whether the problem of overfitting in model selection also affects GPR. We found that the squared exponential covariance function with Automatic Relevance Determination (SEard) is better than other kernels including squared exponential covariance function with isotropic distance measure (SEiso) according to the nLZ, but it is clearly not the best according to MSE on the test data, and this is an indication of over-fitting problem in model selection

Controlled Growth of Carbon Spheres Through the Mg-Reduction Route

Hollow spheres, hollow capsules and solid spheres of carbon were selectively synthesized by Mg-reduction of hexachlorobutadiene at appropriate reaction conditions. X-ray powder diffraction and Raman spectra reveal that the as-prepared materials have a well-ordered structure. A possible formation mechanism has been proposed

Dynamics of Kv1 Channel Transport in Axons

Concerted actions of various ion channels that are precisely targeted along axons are crucial for action potential initiation and propagation, and neurotransmitter release. However, the dynamics of channel protein transport in axons remain unknown. Here, using time-lapse imaging, we found fluorescently tagged Kv1.2 voltage-gated K+ channels (YFP-Kv1.2) moved bi-directionally in discrete puncta along hippocampal axons. Expressing Kvβ2, a Kv1 accessory subunit, markedly increased the velocity, the travel distance, and the percentage of moving time of these puncta in both anterograde and retrograde directions. Suppressing the Kvβ2-associated protein, plus-end binding protein EB1 or kinesin II/KIF3A, by siRNA, significantly decreased the velocity of YFP-Kv1.2 moving puncta in both directions. Kvβ2 mutants with disrupted either Kv1.2-Kvβ2 binding or Kvβ2-EB1 binding failed to increase the velocity of YFP-Kv1.2 puncta, confirming a central role of Kvβ2. Furthermore, fluorescently tagged Kv1.2 and Kvβ2 co-moved along axons. Surprisingly, when co-moving with Kv1.2 and Kvβ2, EB1 appeared to travel markedly faster than its plus-end tracking. Finally, using fission yeast S. pombe expressing YFP-fusion proteins as reference standards to calibrate our microscope, we estimated the numbers of YFP-Kv1.2 tetramers in axonal puncta. Taken together, our results suggest that proper amounts of Kv1 channels and their associated proteins are required for efficient transport of Kv1 channel proteins along axons

Effects of Thyroxine Exposure on Osteogenesis in Mouse Calvarial Pre-Osteoblasts

The incidence of craniosynostosis is one in every 1,800-2500 births. The gene-environment model proposes that if a genetic predisposition is coupled with environmental exposures, the effects can be multiplicative resulting in severely abnormal phenotypes. At present, very little is known about the role of gene-environment interactions in modulating craniosynostosis phenotypes, but prior evidence suggests a role for endocrine factors. Here we provide a report of the effects of thyroid hormone exposure on murine calvaria cells. Murine derived calvaria cells were exposed to critical doses of pharmaceutical thyroxine and analyzed after 3 and 7 days of treatment. Endpoint assays were designed to determine the effects of the hormone exposure on markers of osteogenesis and included, proliferation assay, quantitative ALP activity assay, targeted qPCR for mRNA expression of Runx2, Alp, Ocn, and Twist1, genechip array for 28,853 targets, and targeted osteogenic microarray with qPCR confirmations. Exposure to thyroxine stimulated the cells to express ALP in a dose dependent manner. There were no patterns of difference observed for proliferation. Targeted RNA expression data confirmed expression increases for Alp and Ocn at 7 days in culture. The genechip array suggests substantive expression differences for 46 gene targets and the targeted osteogenesis microarray indicated 23 targets with substantive differences. 11 gene targets were chosen for qPCR confirmation because of their known association with bone or craniosynostosis (Col2a1, Dmp1, Fgf1, 2, Igf1, Mmp9, Phex, Tnf, Htra1, Por, and Dcn). We confirmed substantive increases in mRNA for Phex, FGF1, 2, Tnf, Dmp1, Htra1, Por, Igf1 and Mmp9, and substantive decreases for Dcn. It appears thyroid hormone may exert its effects through increasing osteogenesis. Targets isolated suggest a possible interaction for those gene products associated with calvarial suture growth and homeostasis as well as craniosynostosis. © 2013 Cray et al