A Novel Approach to Reservoir Simulation of Hydraulic Fractures: Performance Improvement Using Pseudo Well Connections

Reservoir simulation is used in most modern reservoir studies to predict future production of oil and gas, and to plan the development of the reservoir. The number of hydraulically fractured wells has risen drastically in recent years due to the increase in production in unconventional reservoirs. Gone are the days of using simple analytic techniques to forecast the production of a hydraulic fracture in a vertical well, and the need to be able to model multiple hydraulic fractures in many stages over long horizontals is now a common practice. The type of simulation approach chosen depends on many factors and is study specific. Pseudo well connection approach was preferred in the current case. Due to the nature of the reservoir simulation problem, a decision needs to be made to determine which hydraulic fracture modeling method might be most suitable for any given study. To do this, a selection of methods is chosen based on what is available at hand, and what is commonly used in various reservoir simulation software packages. The pseudo well connection method, which models hydraulic fractures as uniform conductivity rectangular fractures was utilized for a field of interest referred to as Field A in this paper. Such an assumption of the nature of the hydraulic fracture is common in most modern tools. Field A is a low permeability (0.01md-0.1md), tight (8% to 12% porosity) gas-condensate (API ~51deg and CGR~65 stb/mmscf) reservoir at ~3000m depth. Being structurally complex, it has a large number of erosional features and pinch-outs. The pseudo well connection approach was found to be efficient both terms of replicating data of Field A for a 10 year period while drastically reducing simulation runtime for the subsequent 10 year-period too. It helped the subsurface team to test multiple scenarios in a limited time-frame leading to improved project management.

Research on the weapon target assignment problem in the combined air defense missile system for training simulation

In modern warfare, when the weapon system and the targets are constantly being improved and upgraded, ensuring the distribution of firepower to optimally destroy the target will help the commander to make quick and accurate decisions, thereby improving combat effectiveness. This paper proposes a method to build a command-control automatic system based on solving the weapon target assignment (WTA) problem in a combination of short and medium-range air defense missile systems so that the total damage of targets is maximum and the damage of protected area is minimum. Based on combinatorial optimization algorithms, the probability of kill, linear programming method using Hungarian algorithm, the paper presents a mathematical model of WTA and its optimal solution for short- and medium-range air defense missile systems serving the training simulation problem, thereby giving the results of evaluating the effectiveness of the algorithm.

PENERAPAN MODEL DISCOVERY LEARNING UNTUK MENINGKATKAN MOTIVASI BELAJAR PESERTA DIDIK PADA MATERI SEJARAH KEBUDAYAAN ISLAM

The research aims to improve students learning motivaton and achievement VIII B SMPN 1 Tasikmalaya in PAI Subject through allpaying discovery learning with topic “sejarah dakwah islam”. The research is an action research conducted with two cycles. Each cycle consist of flanning, acting, observing and reflecting. The subject of this research is students of VIII B SMP N 1 Tasikmalaya periode 2018-2019 amount 31 students. In this research, the data collection is conducted with observation to find the quality of he learning process, quesionair to mesure the level of students learnin motivation and acchievement. The data was got through obervation and quesionair is analysed with qualitative-descriptive technique, mean while the data was got through learning achievement test is analysed with quantitative-descriptive technique. The research result show the improvement of students learning motivation from cycle I to cycle II in the amount of 7 %. Then, the students learning achievement show the improvement from cycle I to cycle II in the amount 4 %. The improvement of classical completeness from cycle I to cycle II in the amount of 35 %. The constraint in applying discovery learning is the students are unusual with applaying discovery learning involving simulation, problem statement, data collection, data processing, verification and generaltion, so that it will be chellenge for the teacher to simplify the implementation, so that the student can follow the learnin process well. ABSTRAKPenelitian ini bertujuan untuk meningkatkan motivasi dan hasil belajar peserta didik mata pelajaran PAI dengan menggunakan model discovery learning pada pokok bahasan sejarah kebudayaan Islam. Penelitian ini adalah penelitian tindakan kelas yang dilakukan dengan dua siklus. Setiap siklus terdiri dari tahap perencanaan, pelaksanaan, observasi atau pengamatan dan refleksi. Subjek penelitian ini adalah siswa kelas VIII B SMP N 1 Kota Tasikmalaya tahun pelajaran 2018-2019 yang berjumlah 31 orang. Pengumpulan data dalam penelitian ini dilakukan dengan metode observasi untuk melihat kualitas proses pembelajaran, angket/kuesioner untuk mengukur tingkat motivasi belajar siswa. Data yang diperoleh melalui metode observasi dan angket/kuesioner dianalisis dengan teknik deskriptif-kualitatif sedangkan data yang diperoleh melalui tes hasil belajar dianalisis dengan teknik deskriptif-kuantitatif. Hasil penelitian ini menunjukkan peningkatan rata-rata motivasi belajar siswa siklus I ke siklus II sebesar 7%. Kendala yang dihadapi dalam penerapan model discovery learning yaitu siswa belum terbiasa dengan penerapan model discovery learning yang meliputi Stimulation (stimulasi/pemberian rangsangan), problem statement (pernyataan/identifikasi masalah), data collection (pengumpulan data), data processing ( pengolahan data), verification (pentahkikan/ pembuktian), dan generalization (menarik kesimpulan/ generalisasi) sehingga menjadi tantangan tersendiri bagi guru untuk menyederhanakan pelaksanaannya sehingga siswa dapat mengikuti proses pembelajaran dengan baik.

Dual form of the phase-space classical simulation problem in quantum optics

In quantum optics, nonclassicality of quantum states is commonly associated with negativities of phase-space quasiprobability distributions.We argue that the impossibility of any classical simulations with phase-space functions is a necessary and sufficient condition of nonclassicality. The problem of such phase-space classical simulations for particular measurement schemes is analysed in the framework of Einstein-Podolsky-Rosen-Bell's principles of physical reality. The dual form of this problem results in an analogue of Bell inequalities. Their violations imply the impossibility of phase-space classical simulations and, as a consequence, nonclassicality of quantum states. We apply this technique to emblematic optical measurements such as photocounting, including the cases of realistic photon-number resolution and homodyne detection in unbalanced, balanced, and eight-port configurations.

Accounting for Interelement Interferences in Atomic Emission Spectroscopy: A Nonlinear Theory

The article describes a nonlinear theory of how the presence of third elements affects the results of analyzing the elemental composition of substances by means of atomic emission spectroscopy. The theory is based on the assumption that there is an arbitrary relationship between the intensity of the analytical line of the analyte and the concentration of impurities and alloying elements. The theory has been tested on a simulation problem using commercially available equipment (the SPAS-05 spark spectrometer). By comparing the proposed algorithm with the traditional one, which assumes that there is a linear relationship between the intensity of the analytical line of the analyte and the intensities of the spectral lines (or concentrations) in the substance, it was revealed that there is a severalfold decrease in the deviations of nominal impurity concentrations from the measured ones. The results of this study allow for reducing the number of analytical procedures used in analyzing materials that have different compositions and the same matrix element. For instance, it becomes possible to determine the composition of iron-based alloys (low-alloy and carbon steels; high-speed steels; high-alloy, and heat-resistant steels) using one calibration curve within the framework of a universal analytical method.

SOME ASPECTS OF IMPORT SUBSTITUTION IN THE DOMESTIC SOFTWARE DEVELOPMENT FOR THE DESIGN OF ANTENNA-FEEDER DEVICES

Рассматриваются вопросы импортозамещения при создании отечественного программного обеспечения для проектирования антенно-фидерных устройств. Обозначены основные аспекты проблемы: импортозамещение при создании вычислительных ядер (решение электродинамических задач,задач анализа цепей СВЧ,расчет антенных характеристик и т.д.)и импортозамещение при создании пользовательских интерфейсов и средств электродинамического моделирования. Дается оценка состояния вопроса в отечественной теории и практике в указанных областях. С общих позиций анализируется круг задач, требующих решения для обеспечения процессов импортозамещения. Оцениваются возможности и перспективы реализации данных процессов на практике. The problems of import substitution in the domestic software development for the design of antenna-feeder devices are considered. The main aspects of the import substitution problem are outlined: calculation kernel design (electrodynamic simulation problem, RF-circuit analysis problem, antenna performance definition problem, etc.), user interface design, and electrodynamic modeling tools. The assessment of the state in the domestic theory and practice in these areas is given. From a general point of view, the authors analyze the range of tasks that need to be solved to ensure import substitution processes. The possibilities and prospects for the implementation of these processes in practice are evaluated.

The Effects of Simulation Problem-Based Learning on the Empathy, Attitudes toward Caring for the Elderly, and Team Efficacy of Undergraduate Health Profession Students

Undergraduate students studying health professions receive a uniprofessional education in an isolated educational environment within the university curriculum, and they have limited opportunities to experience collaborative learning through interactions with other professions. This study adopted a one-group, pretest–posttest, quasi-experimental design to investigate the effect of an undergraduate course that applied simulation problem-based learning (S-PBL) on nursing and dental hygiene students’ empathy, attitudes toward caring for the elderly, and team efficacy. The S-PBL was designed based on the ARCS model of motivation proposed by Keller, and the subjects (n = 24) participated in a small group activity of identifying and checking for medical errors that may pose a threat to patients’ safety. The results showed that there was a statistically significant increase in the subjects’ attitudes toward caring for the elderly (t = 3.11, p = 0.01) and team efficacy (t = 2.84, p = 0.01) after participating in the S-PBL. The teaching method developed by this study aims to counteract the problems of the limited experience available to undergraduate health profession students during clinical practicum in the context of the COVID-19 pandemic and the limitations of interprofessional education, and it has established the groundwork for further exploration of the learning transfer effect of S-PBL.

Improving Barnes-Hut t-SNE Algorithm in Modern GPU Architectures with Random Forest KNN and Simulated Wide-Warp

The t-Distributed Stochastic Neighbor Embedding (t-SNE) is a widely used technique for dimensionality reduction but is limited by its scalability when applied to large datasets. Recently, BH-tSNE was proposed; this is a successful approximation that transforms a step of the original algorithm into an N-Body simulation problem that can be solved by a modified Barnes-Hut algorithm. However, this improvement still has limitations to process large data volumes (millions of records). Late studies, such as t-SNE-CUDA, have used GPUs to implement highly parallel BH-tSNE. In this research we have developed a new GPU BH-tSNE implementation that produces the embedding of multidimensional data points into three-dimensional space. We examine scalability issues in two of the most expensive steps of GPU BH-tSNE by using efficient memory access strategies , recent acceleration techniques , and a new approach to compute the KNN graph structure used in BH-tSNE with GPU. Our design allows up to 460% faster execution when compared to the t-SNE-CUDA implementation. Although our SIMD acceleration techniques were used in a modern GPU setup, we have also verified a potential for applications in the context of multi-core processors.

Impact of Temperature on the Moisture Buffering Performance of Palm and Sunflower Concretes

The use of bio-based materials (BBM) in buildings is an interesting solution as they are eco-friendly materials and have low embodied energy. This article aims to investigate the hygric performance of two bio-based materials: palm and sunflower concretes. The moisture buffering value (MBV) characterizes the ability of a material or multilayer component to moderate the variation in the indoor relative humidity (RH). In the literature, the moisture buffer values of bio-based concretes were measured at a constant temperature of 23 °C. However, in reality, the indoor temperature of the buildings is variable. The originality of this article is found in studying the influence of the temperature on the moisture buffer performance of BBM. A study at wall scale on its impact on the indoor RH at room level will be carried out. First, the physical models are presented. Second, the numerical models are implemented in the Simulation Problem Analysis and Research Kernel (SPARK) suited to complex problems. Then, the numerical model validated with the experimental results found in the literature is used to investigate the moisture buffering capacity of BBM as a function of the temperature and its application in buildings. The results show that the temperature has a significant impact on the moisture buffering capacity of bio-based building materials and its capacity to dampen indoor RH variation. Using the numerical model presented in this paper can predict and optimize the hygric performance of BBM designed for building application.

Data-Driven Particle-Based Liquid Simulation with Deep Learning Utilizing Sub-Pixel Convolution

In recent years, the performance of neural network inference has been drastically improved. This rapid change has paved the way for research projects focusing on accelerating physics-based simulations by replacing solver with its approximation. In this paper, we propose several efficient architectures of neural networks, which can be used to exploit this idea. The purpose of our research was to specifically target a liquid simulation problem. The central challenge for us was to create an efficient solution capable of approximating Position Based Fluid [Macklin and Müller 2013] solver. It requires the network to produce an accurate output at particles located in a continuous space and be significantly faster than the GPU based simulation. We achieved this by using modern sub-pixel convolution techniques originally used for image super-resolution. In our experiments, our method runs up to 200 times faster than the reference GPU simulation.