Multi-Well Pressure History Matching in Delaware Play Helps Optimizing Fracturing for Subsequent Pads

Predicting fracture behavior is important for well placement design and for optimizing multi-well development production. This requires the use of fracturing models that are calibrated to represent field measurements. However, because hydraulic fracture models include complex physics and uncertainties and have many variables defining these, the problem of calibrating modeling results with field responses is ill-posed. There are more model variables than can be changed than field observations to constrain these. It is always possible to find a calibrated model that reproduces the field data. However, the model is not unique and multiple matching solutions exist. The objective and scope of this work is to define a workflow for constraining these solutions and obtaining a more representative model for forecasting and optimization. We used field data from a multi-pad project in the Delaware play, with actual pump schedules, frac sequence, and time delays as used in the field, for all stages and all wells. We constructed a hydraulic fracturing model using high-confidence rock properties data and calibrated the model to field stimulation treatment data varying the two model variables with highest uncertainty: tectonic strain and average leak-off coefficient, while keeping all other model variables fixed. By reducing the number of adjusting model variables for calibration, we significantly lower the potential for over-fitting. Using an ultra-fast hydraulic fracturing simulator, we solved a global optimization problem to minimize the mismatch between the ISIPs and treatment pressures measured in the field and simulated by the model, for all the stages and all wells. This workflow helps us match the dominant ISIP trends in the field data and delivers higher confidence predictions in the regional stress. However, the uncertainty in the fracture geometry is still large. We also compared these results with traditional workflows that rely on selecting representative stages for calibration to field data. Results show that our workflow defines a better global optimum that best represents the behavior of all stages on all wells, and allows us to provide higher-confidence predictions of fracturing results for subsequent pads. We then used this higher confidence model to conduct sensitivity analysis for improving the well placement in subsequent pads and compared the results of the model predictions with the actual pad results.

Dynamic Stochastic Optimization of Emergent Blood Collection and Distribution from Supply Chain Perspective

In response to emergencies, it is critical to investigate how to deliver emergency supplies efficiently and securely to disaster-affected areas and people. There is no doubt that blood is deemed one of the vital relief supplies, and ensuring smooth blood delivery may substantially alleviate subsequent impacts caused by the disaster. Taking red blood cell products as the research object, this work proposes a four-echelon blood supply chain model. Specifically, it includes blood donors, blood donation houses, blood centres, and hospitals. Furthermore, numerical analysis is provided to test the feasibility of blood collection and distribution schemes and conduct sensitivity analysis to test the impacts of the relevant parameters (e.g., apheresis donation proportion of red blood cells (RBCs), distance between blood donors and blood facilities, and times of blood donation) on the scheme. This research provides some scientific and reasonable support for decision makers and managerial implications for emergency departments and contributes to the study of emergent blood supply chain.

Transmission dynamics and quarantine control of COVID-19 in cluster community: A new transmission-quarantine model with case study for diamond princess

Coronavirus Disease 2019 (COVID-19) is a zoonotic illness which has spread rapidly and widely since December, 2019, and is identified as a global pandemic by the World Health Organization. The pandemic to date has been characterized by ongoing cluster community transmission. Quarantine intervention to prevent and control the transmission are expected to have a substantial impact on delaying the growth and mitigating the size of the epidemic. To our best knowledge, our study is among the initial efforts to analyze the interplay between transmission dynamics and quarantine intervention of the COVID-19 outbreak in a cluster community. In the paper, we propose a novel Transmission-Quarantine epidemiological model by nonlinear ordinary differential equations system. With the use of detailed epidemiologic data from the Cruise ship “Diamond Princess”, we design a Transmission-Quarantine work-flow to determine the optimal case-specific parameters, and validate the proposed model by comparing the simulated curve with the real data. First, we apply a general SEIR-type epidemic model to study the transmission dynamics of COVID-19 without quarantine intervention, and present the analytic and simulation results for the epidemiological parameters such as the basic reproduction number, the maximal scale of infectious cases, the instant number of recovered cases, the popularity level and the final scope of the epidemic of COVID-19. Second, we adopt the proposed Transmission-Quarantine interplay model to predict the varying trend of COVID-19 with quarantine intervention, and compare the transmission dynamics with and without quarantine to illustrate the effectiveness of the quarantine measure, which indicates that with quarantine intervention, the number of infectious cases in 7 days decrease by about 60%, compared with the scenario of no intervention. Finally, we conduct sensitivity analysis to simulate the impacts of different parameters and different quarantine measures, and identify the optimal quarantine strategy that will be used by the decision makers to achieve the maximal protection of population with the minimal interruption of economic and social development.

Targeted Undersmoothing: Sensitivity Analysis for Sparse Estimators

This paper proposes a procedure for assessing sensitivity of inferential conclusions for functionals of sparse high-dimensional models following model selection. The proposed procedure is called targeted undersmoothing. Functionals considered include dense functionals that may depend on many or all elements of the highdimensional parameter vector. The sensitivity analysis is based on systematic enlargements of an initially selected model. By varying the enlargements, one can conduct sensitivity analysis about the strength of empirical conclusions to model selection mistakes. We illustrate the procedure's performance through simulation experiments and two empirical examples.

A Bayesian Approach for Large Asset Allocation

The Black-Litterman model combines investor’s personal views with historical data and gives optimal portfolio weights. In (Andrei & Hsu, 2020), they reviewed the original Black-Litterman model and modified it in order to fit it into a Bayesian framework, when a certain number of assets is considered. They used the idea by (Leonard & Hsu, 1992) for a multivariate normal prior on the logarithm of the covariance matrix. When implemented and applied to a large number of assets such as all the S&P500 companies, they ran into memory allocation and running time issues. In this paper, we reduce the dimensions by considering Bayesian factor models, which solve the asset allocation problems for a large number of assets. In addition, we will conduct sensitivity analysis for the confidence levels that the investors have to input.

Experimental and Numerical Analysis on the Performance of Spiral Two-Fluid Atomizer Using DPM Method

A two-fluid atomizer has been frequently used in a wide range of industries for various purposes such as painting, cleaning particles and snow making. In particular, the manufacturing of advance semiconductors using sensitive devices such as organic light emitting diodes (OLED) and dynamic random access memory (DRAM), require high performance nozzle. The droplets sprayed with a high relative gas velocity are widely used for cleaning particles. In this paper, two-fluid atomizer is numerically studied according to four variables to confirm the effect on the atomizer performance. The numerical results using the discrete phase model (DPM) with several break-up models are compared with the experimental data measured by the phase doppler particle analyzer (PDPA). Design of experiment (DOE) and genetic algorithm (GA) were used to obtain design points, and conduct sensitivity analysis, respectively. The results showed that the WAVE model has a good agreement compared to the other models, and the orifice diameter is a crucial factor for this model to determine the performance of Weber number and pressure.

Quantifying unmeasured confounding in three epidemiological studies associating myelodysplastic syndromes (MDS) with cardiovascular disease (CVD).

e19532 Background: Confounding occurs when an investigator studies the effect of an exposure on the occurrence of an outcome, but in the process measures the effect of another factor (a confounding variable). Knowledge of possible causal pathways assists an investigator to minimize confounding, but despite utmost attempts, errors are frequent. To assess role of a possible confounder (smoking), data from 3 studies (1. Herrera at al. 2019 2. Goldberg et al. 2010 and 3. Brunner et al. 2017) associating MDS (exposure) with CVD (outcome) was used to conduct sensitivity analysis. Methods: 2 x 2 tables were constructed with data from 3 studies (part 2 of table below). A vector defining the proportion of confounder (smoking) among exposed [prop (Smoking+|MDS+)], unexposed [prop (Smoking+|MDS-)] and risk ratio associating smoking status with CVD (part 1) using historical data was deduced. Extracted data was added to online bias analysis tool ( https://sites.google.com/site/biasanalysis/ ) to calculate crude as well as mantel-haenszel (MH) relative risk (RR) adjusted for smoking. Results: RRs unadjusted for smoking status for studies 1, 2 and 3 were 1.31, 1.34 and 2.21, respectively, signifying increased risk of CVD in MDS patients (part 3). Upon adjustment for smoking status, MH-RR for MDS-CVD relationship was 0.95, 0.97 for studies 1 and 2 respectively, signifying no increased CVD risk and 1.59 for study 3 signifying increased CVD risk but lower upon adjustment. Conclusions: Making inference of MDS-CVD association without accounting for smoking status can blur effects and at times result in false associations. Given ambiguity in results from these 3 studies, further evidence is needed to better assess MDS-CVD relationship. A simple online tool allows estimating nonrandom errors, assess magnitude and direction of biases, and help clinicians/researchers quantify these biases for better interpretation of observational data in a time efficient manner. Other bias analyses for these studies will be presented. [Table: see text]

Coordination of a Green Supply Chain with One Manufacturer and Two Competing Retailers under Different Power Structures

The main objective of this research is to examine the role of power relationship in a two-level green supply chain which is made up of one shared manufacturer and two competitive retailers. We develop six game theory-based models to explore the members’ operational decisions in a supply chain taking into account three vertical power structures (Manufacturer Stackelberg, Retailer Stackelberg, and Vertical Nash) as well as two retailers’ horizontal power structure (Bertrand or Stackelberg competition). Then, we design a two-part tariff contract which can encourage the supply chain members to promote cooperation and eventually coordinate the decentralized green supply chain under each power structure. Lastly, to further discuss the impact of the green awareness of consumers and the greening cost on supply chain players’ operational decisions and profits, we employ some numerical examples to conduct sensitivity analysis. The main conclusions are as follows. Firstly, the impact of power structure on the supply chain players’ operational decisions and profits mainly depends on the substitutability of the green products, the green awareness of consumers, and the greening cost for the manufacturer. Secondly, the more power the manufacturer has, the lower product greenness will be set. Thirdly, the consumer’s environmental awareness (the greening cost) positively (negatively) influences the manufacturer’s product greenness and wholesale price, the retailers’ sales prices, and the player’s profits under each power structure. Finally, the developed two-part tariff contract is practicable and beneficial for both the manufacturer and the two retailers.

Model and analysis of the effect of China’s potential domestic emission control area with 0.1% sulphur limit

Purpose The purpose of this study is to analyze the effect of China’s potential domestic emission control area (DECA) with 0.1 per cent sulphur limit on sulphur emission reduction. Design/methodology/approach The authors calculate the fuel cost of a direct path within the DECA and a path that bypasses the DECA for ships that sail between two Chinese ports in view of the DECA. Ships adopt the path with the lower cost and the resulting sulphur dioxide (SO2) emissions can be calculated. They then conduct sensitivity analysis of the SO2 emissions with different values of the parameters related to sailing distance, fuel price and ships. Findings The results show that ships tend to detour to bypass the DECA when the distance between the two ports is long, the ratio of the price of low sulphur fuel and that of high sulphur fuel is high and the required time for fuel switching is long. If the time required for fuel switching is less than 12 h or even 24 h, it can be anticipated that a large number of ships will bypass the DECA, undermining the SO2 reduction effect of the DECA. Originality/value This study points out the size and shape difference between the emission control areas in Europe and North America and China’s DECA affects ships’ path choice and SO2 emissions.

Impact of Gliricidia Fertilizer Tree Technology on Smallholder Farmers Economic Livelihood in Malawi: Case of Kasungu District

Agriculture plays a huge role in farmer’s livelihoods in Africa. With the adverse effect of climate change on agricultural productivity, developing agricultural technologies that are adaptive to climate change is one of the perquisites for agricultural development. Gliricidia intercropping is one of the climate smart agricultural innovations; that is being promoted by most researchers. Gliricidia intercropping has many benefits. Despite evidence of such benefits, there exists some missing literature on the impact of Gliricidia intercropping on farmer’s economic livelihoods. The study used cross sectional data collected by ICRAF in Kasungu district which sampled 406 households and employed a Propensity Score Matching method to analyze the effect of Gliricidia intercropping on smallholder farmer’s incomes. Results showed that among the observable factors used to match participants and non-participants, hired labour, age, education level, soil type, perception of soil fertility and access to extension services significantly affected participation in Gliricidia intercropping. The Average Treatment Effect on the Treated showed that Gliricidia intercropping improves the economic livelihoods of farmers by increasing household monthly income by MWK 38,565.83 ($54) at 1 percent significant level. The study went further to conduct sensitivity analysis using the Rosenbaum bounds, and found that unobserved heterogeneity has to increase the odds ratio of participating in Gliricidia intercropping by 10-60 percent before it can negate the estimated ATT. The study then recommends promoting the adoption of Gliricidia intercropping by capitalizing on the factors that influence participation or adoption of Gliricidia intercropping in order to improve smallholder farmers’ incomes and hence their livelihoods.