Intellectual System For Analyzing Thermal Compositional Modeling With Chemical Reactions

The aim of any oil producing company is daily effective and safe oil production. To maximize the extraction of oil from oil reservoirs, it is necessary to continuously improve the work of the oil industry, carry out various measures to optimize the work of producing and injection wells, maintain optimal reservoir pressure, and use modern approved and tested methods of increasing oil recovery. Also, it is necessary to improve the technology of oil production by automating the management of the production process on the basis of “i-fields” concept. When considering large and complex oil and gas fields, the implementation of such technologies requires their qualitative research. Operative decision-making and optimal exploitation of fields imply the need for modeling and monitoring of these fields in real time with the involvement of modern software and hardware. When managing a field, real-time collection and processing of information is required. Whereas, not all fields are provided with advanced infrastructure for wireline data collection. For them, it is suggested to collect and pre-process data in the fields in an automatic mode with the help of sensors of the embedded system (FPGA-based system). Given work devoted to development of the intellectual distributed high-performance information system of analysis of different scenarios of the oil production to determine optimal development parameters of oil fields. Proposed system uses thermal compositional model taking into account chemical reactions and supports high-performance computing based on CUDA technology for mobile platforms and MPI for supercomputers in real-time. System allows rapid sequence reading from wells using sensors and controllers (FPGA) and if necessary preprocess data for usage in further calculations. The principles of Closed-Loop Reservoir Management (CLRM) methodology will be used as a basis, which is a combination of optimization of the life cycle and comparison of the development history of a field. The implementation of this requires large computational resources with the use of procedures for assessing the Value of Information (VOI). Different methods of data clustering (K-averages, multidimensional scaling and tensor decomposition) comparing to select a limited number of representative members from the ensemble of field models with the choice of the optimal set of controls for multiple modeling scenarios.