Maxim Khisamutdinov

Neuronetwork Decision Support System for Oilfield Equipment Condition Online Monitoring

In this paper we offer the solution of the problem of oil-extracting production efficiency and safety raise by decision support system (DSS) application developed on the artificial neuronetwork technology basis. The description of new database knowledge discovery neuronetwork methods, applied for diagnostics and forecasting is provided. The architecture and the functions of DSS applied for oilfield objects state online monitoring, developed on proposed methods and algorithms basis, is described.

Hybrid Method of Dynamograms Wavelet Analysis for Oil-Production Equipment State Identification

In the paper we considered a hybrid method of dynamograms wavelet analysis, applied for oil-production equipment work mode identification. Neural network architecture for sucker-rod deep-well pump units malfunctions detection is proposed. The architecture of intellectual data recognition system applied for pump installation control is presented.

Application of hybrid data mining methods to increase profitability of heavy oil production

In this paper, we consider the problem of increasing the efficiency of heavy oil production by a qualitative enhanced oil recovery (EOR) application. A novel approach, based on the analysis of data related to successful events, is proposed. In particular, a synthesised model, targeted to automated search of wells for EOR application, is developed. For data processing, we present a novel approach based on a hybrid implementation of neural network analysis techniques and evolutionary algorithms. The presented approach enables the selection of EOR in fuzzy, difficult to formalise, oilfield conditions and consequently reduces the dependency on human factors.

Real-time diagnostics of oil production equipment using data mining

In this paper, we consider the problem of increasing the efficiency of heavy oil production by a qualitative enhanced oil recovery (EOR) application. A novel approach, based on the analysis of data related to successful events is proposed. In particular, a synthesised model, targeted to automated search of wells for EOR application, is developed. For data processing, we present a novel approach based on a hybrid implementation of neural network analysis techniques and evolutionary algorithms. The presented approach enables the selection of EOR in fuzzy, difficult to formalise, oilfield conditions and consequently reduces the dependency on human factors.

A novel method of organisation of a software defined network control system

In this paper, we offer a new method for solving control tasks in software-defined networks for use in corporate networks. Its main feature is the ability to increase failure free operation due to employing distributed computing resources available in the corporate network. To achieve the ability of effective usage of personal computers, our proposed method uses a multi-agent approach, where a proactive agent controls every personal computer and the process of task solution is dispatched in a decentralised way through interactions of agents. To solve every control task, the agents of the system collaborate and thus facilitate dispatch and computation.

An efficient evolutionary approach to placing VLSI elements based on multithread CUDA programming

In this paper, we present an evolutionary algorithm to place VLSI elements based on evolutionary computing with multi-chromosomes. In addition, we present possible software implementation of the algorithm based on multithread CUDA programming, and report some experimental results of the suggested algorithm implementation.

An effective algorithm for multiagent dispatching of resources in heterogeneous cloud environments

In this paper, we deal with the problem of adaptive dispatching (distribution) of resources in a cloud computer environment (CCE). In particular, the CCE comprises heterogeneous computational resources and we consider the implementation of a flow of large-scale scientific tasks consisting of a set of information-dependent subtasks that enter the CCE at random times. We present an effective method for multiagent dispatching of CCE resources that can be employed when CCE resources have different performances for different tasks and different channel bandwidths for connecting with cloud infrastructure. We give a formal definition of the problem of CCE dispatching for these conditions and suggest principles of its solution using a set of program agents that are physically implemented on separate CCE resources and that represent their “interests” during dispatch. We introduce the developed algorithm of a program agent as a multiagent dispatcher providing adaptive distribution of all currently free CCE computational resources among the tasks of the flow, taking into consideration their real performances and channel capacities. We further present experimental results obtained using a program model of a heterogeneous CCE.

A hardware approach for organisation of software defined network switches based on FPGA

In this paper, we present a novel method for a hardware-based search in the rule tables of software defined network (SDN) switches. We describe the design features of modern SDNs based on the open standard OpenFlow, and highlight the main problems of modern OpenFlow switches. We then propose a new approach for a flow table search based on field-programmable gate arrays (FPGAs). The proposed method allows searching by means of a mask in all bits of the header of incoming packages and therefore to expand the usability of SDNs.

Development of Methods and Algorithms to Identify Real-Time Features of the Time-Frequency Characteristics of Electromagnetic Radiation in the Digital Data Radio Receiver

The main objective of the work is the development of methods, algorithms and software to identify real-time features of the time-frequency characteristics of electromagnetic radiation in the digital data radio receiver different wavelength ranges.