Capacity Assessment Of The System Of Gas Pipelines, Receiving And Transporting Gas Of Inland Production

Today, the majority of gas fields in Ukraine are in the final stages of development, which is characterized by a significant decrease in wellhead pressure, as well as an increased gas-water factor. As is well known, when lowering wellhead pressure arises the problem of ensuring the design capacity of the gas production system as a whole.The main function of the gas pipeline system of the gas producing company of Ukraine is collection of gas from deposits and transport natural gas to consumers.Taking into account the tasks of ensuring the energy independence of Ukraine, as well as the program to build up gas of its own production, the question of assessing the capacity of the gas pipeline system remains relevant, performing the function of collection and transportation.As part of the research, the current state of the gas collection and transportation system is analyzed. The workload of gas pipeline sections in the chain from the wellhead to the consumer is investigated. As a result, it is established that the initial sections of the gas production system are fully loaded. Areas that can be recharged are identified, as a result of which it will reduce the output pressure at the wellheads and stabilize hydrocarbon production.On the basis of the conducted research, it is revealed that one of the alternative methods of increasing the capacity of the gas production system at the initial sections is to increase the equivalent diameter and length of the system by building new gas pipelines. It is also found that the periodic cleaning of pipelines in existing parts of the system prevents the decrease in capacity.It has been established that reducing the backpressure of the system is possible only in conjunction with unloading the system by changing the flow directions, creating centralized gas collection points, as well as retrofitting existing booster compressor stations.The availability of data on the load on the gas transmission system will allow the gas producing company to plan the distribution of gas to areas with available free capacity, while ensuring an increase in the production of its own gas. As a result, when the gas is distributed to areas with partial load, it will prevent excessive pressure losses in the system, as well as provide optimal system operation conditions

Large Scale SfM with the Distributed Camera Model

We introduce the distributed camera model, a novel model for Structure-from-Motion (SfM). This model describes image observations in terms of light rays with ray origins and directions rather than pixels. As such, the proposed model is capable of describing a single camera or multiple cameras simultaneously as the collection of all light rays observed. We show how the distributed camera model is a generalization of the standard camera model and describe a general formulation and solution to the absolute camera pose problem that works for standard or distributed cameras. The proposed method computes a solution that is up to 8 times more efficient and robust to rotation singularities in comparison with gDLS. Finally, this method is used in an novel large-scale incremental SfM pipeline where distributed cameras are accurately and robustly merged together. This pipeline is a direct generalization of traditional incremental SfM; however, instead of incrementally adding one camera at a time to grow the reconstruction the reconstruction is grown by adding a distributed camera. Our pipeline produces highly accurate reconstructions efficiently by avoiding the need for many bundle adjustment iterations and is capable of computing a 3D model of Rome from over 15,000 images in just 22 minutes.Comment: Published at 2016 3DV Conferenc

Comparing MapReduce and pipeline implementations for counting triangles

A generalized method to define the Divide & Conquer paradigm in order to have processors acting on its own data and scheduled in a parallel fashion. MapReduce is a programming model that follows this paradigm, and allows for the definition of efficient solutions by both decomposing a problem into steps on subsets of the input data and combining the results of each step to produce final results. Albeit used for the implementation of a wide variety of computational problems, MapReduce performance can be negatively affected whenever the replication factor grows or the size of the input is larger than the resources available at each processor. In this paper we show an alternative approach to implement the Divide & Conquer paradigm, named pipeline. The main features of pipeline are illustrated on a parallel implementation of the well-known problem of counting triangles in a graph. This problem is especially interesting either when the input graph does not fit in memory or is dynamically generated. To evaluate the properties of pipeline, a dynamic pipeline of processes and an ad-hoc version of MapReduce are implemented in the language Go, exploiting its ability to deal with channels and spawned processes. An empirical evaluation is conducted on graphs of different sizes and densities. Observed results suggest that pipeline allows for the implementation of an efficient solution of the problem of counting triangles in a graph, particularly, in dense and large graphs, drastically reducing the execution time with respect to the MapReduce implementation.Peer ReviewedPostprint (published version