Use of orthogonal arrays, quasi-Monte Carlo sampling and kriging response models for reservoir simulation with many varying factors

Asset development teams may adjust simulation model parameters using experimental design to reveal which factors have the greatest impact on the reservoir performance. Response surfaces and experimental design make sensitivity analysis less expensive and more accurate, helping to optimize recovery under geological and economical uncertainties. In this thesis, experimental designs including orthogonal arrays, factorial designs, Latin hypercubes and Hammersley sequences are compared and analyzed. These methods are demonstrated for a gas well with water coning problem to illustrate the efficiency of orthogonal arrays. Eleven geologic factors are varied while optimizing three engineering factors (total of fourteen factors). The objective is to optimize completion length, tubing head pressure, and tubing diameter for a partially penetrating well with uncertain reservoir properties. A nearly orthogonal array was specified with three levels for eight factors and four levels for the remaining six geologic and engineering factors. This design requires only 36 simulations compared to (26,873,856) runs for a full factorial design. Hyperkriging surfaces are an alternative model form for large numbers. Hyperkriging uses the maximum likelihood variogram model parameters to minimize prediction errors. Kriging is compared to conventional polynomial response models. The robustness of the response surfaces generated by kriging and polynomial regression are compared using jackknifing and bootstrapping. Sensitivity analysis and uncertainty analysis can be performed inexpensively and efficiently using response surfaces. The proposed design approach requires fewer simulations and provides accurate response models, efficient optimization, and flexible sensitivity and uncertainty assessment

The Material Theory of Induction

The fundamental burden of a theory of inductive inference is to determine which are the good inductive inferences or relations of inductive support and why it is that they are so. The traditional approach is modeled on that taken in accounts of deductive inference. It seeks universally applicable schemas or rules or a single formal device, such as the probability calculus. After millennia of halting efforts, none of these approaches has been unequivocally successful and debates between approaches persist. The Material Theory of Induction identifies the source of these enduring problems in the assumption taken at the outset: that inductive inference can be accommodated by a single formal account with universal applicability. Instead, it argues that that there is no single, universally applicable formal account. Rather, each domain has an inductive logic native to it.The content of that logic and where it can be applied are determined by the facts prevailing in that domain. Paying close attention to how inductive inference is conducted in science and copiously illustrated with real-world examples, The Material Theory of Induction will initiate a new tradition in the analysis of inductive inference

The Material Theory of Induction

The fundamental burden of a theory of inductive inference is to determine which are the good inductive inferences or relations of inductive support and why it is that they are so. The traditional approach is modeled on that taken in accounts of deductive inference. It seeks universally applicable schemas or rules or a single formal device, such as the probability calculus. After millennia of halting efforts, none of these approaches has been unequivocally successful and debates between approaches persist. The Material Theory of Induction identifies the source of these enduring problems in the assumption taken at the outset: that inductive inference can be accommodated by a single formal account with universal applicability. Instead, it argues that that there is no single, universally applicable formal account. Rather, each domain has an inductive logic native to it. The content of that logic and where it can be applied are determined by the facts prevailing in that domain

LIPIcs, Volume 251, ITCS 2023, Complete Volume

LIPIcs, Volume 251, ITCS 2023, Complete Volum

Efficient physics signal selectors for the first trigger level of the Belle II experiment based on machine learning

A neural network based z-vertex trigger is developed for the first level trigger of the upgraded flavor physics experiment Belle II at the high luminosity B factory SuperKEKB in Tsukuba, Japan. Using the hit and drift time information from the central drift chamber, a pool of expert neural networks estimates the 3D track parameters of the single tracks found by a 2D Hough finder. The neural networks are already implemented on parallel FPGA hardware for real time data processing and running pipelined in the online first level trigger of Belle II. Due to the anticipated high luminosity of up to 8 × 10³⁵ cm⁻²s⁻¹, Belle II will have to face severe levels of background tracks with vertices displaced along the beamline. The neural z-vertex algorithm presented in this thesis allows to reject displaced background tracks such that the requirements of the standard track trigger can be strongly relaxed. Especially for physics decay channels with a low track multiplicity in the final states, like τ pair production, or initial state radiation events with reduced center of mass energies, the trigger efficiencies can be significantly increased. As an upgrade of the present 2D Hough finder in the neural network preprocessing, a model independent 3D track finder is developed that uses the additional stereo hit information of the drift chamber. Thus, the trigger efficiencies improve for tracks in the phase space of low transverse momenta and shallow polar angles. Since the cross sections of the physics signal events typically increase towards shallow polar angles, this enlarged acceptance of the track trigger provides a substantial gain in the signal efficiencies. By using an adapted pool of expert networks, the enlarged phase space provided by the 3D finder can be efficiently covered. Studies on simulated MC background, on simulated initial state radiation events, and on recorded data from early Belle II runs demonstrate the high performance of the novel trigger algorithms. With the 3D finder an increase of the track finding rate of about 50 % is confirmed for signal tracks, while displaced background tracks are actively suppressed prior to the neural network. Based on z-vertex cuts on the tracks processed by the neural networks, a two track event efficiency of more than 99 % can be achieved with a purity of around 80 %

An Empirical Investigation Of Information Technology Mediated Customer Services In China

Information technology mediated customer service is a reality of the 21st century. More and more companies have moved their customer services from in store and in person to online through computer or mobile devices. Using 208 respondents collected from two Chinese universities, this paper investigates customer preference over two service delivery model (either in store or online) on five type of purchasing (retail, eating-out, banking, travel and entertainment) and their perception difference in customer service quality between those two delivery model. Results show that a majority of Chinese students prefer in store and in person for eating out. For ordering tickets for travel and entertainment, they prefer computer/mobile device. For retail purchasing and banking, less than half of the students prefer in person services. In general, the results show that ordering through computer/mobile devices has become more popular in China and has received higher rating for most of customer service quality except security compared to ordering in store. In addition, it is found that there exist a gender difference in purchasing preference and perception in service delivery quality in China