A review of stimulated reservoir volume characterization for multiple fractured horizontal well in unconventional reservoirs

Unconventional resource exploration has boosted U.S. oil and gas production, which is successfully by horizontal well drilling and hydraulic fracturing. The horizontal well with multiple transverse fractures has proven to be effective stimulation approach could increase reservoir contact significantly. Unlike the single fracture planes in typical low permeability sands, fractures in shales tends to generate more complex, branching networks. The concept of stimulated reservoir volume was developed to quantitative measure of multistage fracture interact with natural fractures in unconventional reservoir. However, the simple fracture modeling of the past do not suitable for the complex scenarios simulation. This paper reviews the mainstream characterization method of stimulated reservoir volume in shale reservoirs, including microseismic interpretation, rate transient analysis method, analytical and semi-analytical method and numerical method. Finally, the systematic evaluation of application conditions with respect to each method and further research directions for characterization method are proposed.Cited as: Wang, W., Zheng, D., Sheng, G., et al. A review of stimulated reservoir volume characterization for multiple fractured horizontal well in unconventional reservoirs. Advances in Geo-Energy Research, 2017, 1(1): 54-63, doi: 10.26804/ager.2017.01.0

Automated tracking of level of consciousness and delirium in critical illness using deep learning

Over- and under-sedation are common in the ICU, and contribute to poor ICU outcomes including delirium. Behavioral assessments, such as Richmond Agitation-Sedation Scale (RASS) for monitoring levels of sedation and Confusion Assessment Method for the ICU (CAM-ICU) for detecting signs of delirium, are often used. As an alternative, brain monitoring with electroencephalography (EEG) has been proposed in the operating room, but is challenging to implement in ICU due to the differences between critical illness and elective surgery, as well as the duration of sedation. Here we present a deep learning model based on a combination of convolutional and recurrent neural networks that automatically tracks both the level of consciousness and delirium using frontal EEG signals in the ICU. For level of consciousness, the system achieves a median accuracy of 70% when allowing prediction to be within one RASS level difference across all patients, which is comparable or higher than the median technician-nurse agreement at 59%. For delirium, the system achieves an AUC of 0.80 with 69% sensitivity and 83% specificity at the optimal operating point. The results show it is feasible to continuously track level of consciousness and delirium in the ICU

Regional determinants of China’s consumption-based emissions in the economic transition

China has entered the economic transition in the post-financial crisis era, with unprecedented new features that significantly lead to a decline in its carbon emissions. However, regional disparity implies different trajectories in regional decarbonisation. Here, we construct multi-regional input-output tables (MRIO) for 2012 and 2015 and quantitatively evaluate the regional disparity in decarbonisation and the driving forces during 2012-2015. We found China's consumption-based emissions peaked in 2013, largely driven by a peak in consumption-based emissions from developing regions. Declined intensity and industrial structures are determinants due to the economic transition. The rise of the Southwest and Central regions of China have become a new feature, driving up emissions embodied in trade and have reinforced the pattern of carbon flows in the post-financial crisis period. Export-related emissions have bounced up after years of decline, attributed to soaring export volume and export structure in the Southeast and North of the country. The disparity in developing regions has become the new feature in shaping China's economy and decarbonisation

Detection of Activities by Wireless Sensors for Daily Life Surveillance: Eating and Drinking

This paper introduces a two-stage approach to the detection of people eating and/or drinking for the purposes of surveillance of daily life. With the sole use of wearable accelerometer sensor attached to somebody’s (man or a woman) wrists, this two-stage approach consists of feature extraction followed by classification. At the first stage, based on the limb’s three dimensional kinematics movement model and the Extended Kalman Filter (EKF), the realtime arm movement features described by Euler angles are extracted from the raw accelerometer measurement data. In the latter stage, the Hierarchical Temporal Memory (HTM) network is adopted to classify the extracted features of the eating/drinking activities based on the space and time varying property of the features, by making use of the powerful modelling capability of HTM network on dynamic signals which is varying with both space and time. The proposed approach is tested through the real eating and drinking activities using the three dimensional accelerometers. Experimental results show that the EKF and HTM based two-stage approach can perform the activity detection successfully with very high accuracy

Genomic Profiling of Childhood Tumor Patient-Derived Xenograft Models to Enable Rational Clinical Trial Design.

Accelerating cures for children with cancer remains an immediate challenge as a result of extensive oncogenic heterogeneity between and within histologies, distinct molecular mechanisms evolving between diagnosis and relapsed disease, and limited therapeutic options. To systematically prioritize and rationally test novel agents in preclinical murine models, researchers within the Pediatric Preclinical Testing Consortium are continuously developing patient-derived xenografts (PDXs)-many of which are refractory to current standard-of-care treatments-from high-risk childhood cancers. Here, we genomically characterize 261 PDX models from 37 unique pediatric cancers; demonstrate faithful recapitulation of histologies and subtypes; and refine our understanding of relapsed disease. In addition, we use expression signatures to classify tumors for TP53 and NF1 pathway inactivation. We anticipate that these data will serve as a resource for pediatric oncology drug development and will guide rational clinical trial design for children with cancer

Analytic methods and numerical algorithms for pricing and hedging discretely sampled variance products and volatility derivatives

Volatility derivatives are a class of derivative products whose payoffs are closely associated with the volatility of some underlying asset. They have gained more and more popularity among investors due to the widespread awareness of the volatility risk. Therefore, accurate pricing of volatility derivatives and a good understanding of their risk exposure are essential. In this thesis, various derivatives on the discretely monitored realized variance are investigated under a variety of asset price dynamics. Closed form pricing formulas for generalized variance swaps which are known as the third generation volatility products are derived, as a result of the linearity of the payoff structure and the affine structure of the stochastic volatility model with simultaneous jumps. The linkage between the discrete pricing formulas and their continuous counterparts is analyzed and the convergence is established. For other types of volatility derivatives with nonlinear payoff structures, such as volatility swaps and options on realized variance, semi-analytical pricing formulas via the saddlepoint approximation method have been proposed. Finally, numerical algorithms based on Fourier transform are designed to price general contingent claims on discretely sampled (generalized) realized variance