THE CASPIAN REGION: DEVELOPMENT RESULTS AND NEW TRENDS

The Caspian region came into the focus of attention of the Caspian and non-regional states even prior to the collapse of the U.S.S.R. The increased global attention to this region was associated with the presence of proven and potential reserves of hydrocarbon resources, which increased the region’s geopolitical significance. After the collapse of the Soviet Union, the Caspian region found itself in the center of geopolitical rivalry. From that time on, the subject of energy acquired a new meaning in the Caspian region. Western oil and gas companies and government agencies began to demonstrate an increased interest in the hydrocarbon resources of the Caspian region. Moreover, for decades the West has maintained a close focus on the Eurasian space, in particular, on the problems associated with the production and transportation of hydrocarbon resources. The most acute geopolitical standoff occurred between Russia and the United States, which supported various pipeline projects. For Russia, the key task was to preserve its regional dominance, which had been growing over the course of several centuries. The United States supported the geopolitical turn of the new Caspian states, advocating the creation of new hydrocarbon supply routes that would bypass Russian territory. The key task for the Caspian states was to increase hydrocarbon production and provide reliable routes for their export to foreign markets. Based on these goals, the Caspian states built their own foreign policy, including intraregional policy. Thirty years later, the results of geopolitical rivalry are visible. The Caspian countries, which rely on financial resources and political support from non-regional actors, have implemented large-scale hydrocarbon export projects. The new pipeline architecture has changed the balance of power in the Caspian region, increasing the involvement of the Caspian states in the energy policy of Turkey, China, and the EU. At the same time, the regional states have managed to solve the problem of the international legal status of the Caspian Sea in a five-sided format. A new trend of the last decade has involved projects related to the construction of coastal infrastructure and expansion of shipping. The Caspian countries are growing increasingly more interested in participating in international transport projects, considering them as an important component of their foreign policy. Despite the attained agreements and solutions to key problems, competition between the Caspian states, which is greatly influenced by non-regional actors, is intensifying.

Design and Implementation of AGU based FFT Pipeline Architecture

Present it is most needful task to get various applications with parallel computations by using a Fast Fourier Transform (FFT) and the derived outputs should be in regular format. This can be achieved by using an advanced technique called Multipath delay commutator (MDC) Pipelining FFT processor and this processor will be capable to perform the computation of a different data streams at a time. In this paper the design and implementation of AGU based Pipelined FFT architecture is done Caluclation of a butterfly is done within 2 cycles by the instructions proposed. A Data Processing Unit (DPU) is employed in this pipeline architecture and supports the instructions & an FFT Adress Generation Unit (FAGU) caluclates butterfly input & output data adresses automatically. The DPU proposed sysyem requires less area compared to commericial DSP chips. Futhermore, the proposed FAGU reduces the number of FFT computation cycles. The FFT design architecture will have real data paths. With various FFT sizes, different radix & various parallesim levels, the FFT can be mapped to the pipeline architecture. The most attractive feature of the pipelined FFT architecture is it consists of bit reversal operation so it requires little number of registers and better throughput.

Design and Assertion Based Verification of RISC-V Processor Subsystems

RISC-V is an open, free standard architecture. As its open-source architecture, it can be used in multiple applications like embedded processors, IoT, artificial intelligence, machine learning, military and defense applications. The parameters like throughput, performance, high speed etc., become essential in designing processor architecture. Pipelining is one such unique feature supported by RISC-V ISA, which basically involves the execution of multiple instructions in single cycle. This feature helps in improving the performance of the processor architecture. RISC-V ISA supports five stages of pipelining they are instruction fetch, instruction decode, execute, memory and write-back stage. The work covered in this paper involves the design and implementation of the subsystems of the RISC-V ISA which are present in different stages of pipeline architecture. The subsystems included in this work are Floating Point Unit (FPU) of Execute stage, Branch Prediction Unit (BPU) of instruction fetch stage, Forwarding Unit of execution stage, Operand Logic of decode stage and Floating-Point register file of Write-back stage. These subsystems are designed using Verilog Hardware Description Language in Xilinx ISE. Followed by the implementation the verification of the floating-point unit and the forwarding unit is performed using System Verilog Assertions in QuestaSim. The Assertion coverage report for the same is extracted.

A Real-Time Fiber Optical System for Wellbore Monitoring: A Johan Sverdrup Case Study

Fiber Optic (FO) sensing capabilities for downhole monitoring include, among other techniques, Distributed Temperature Sensing (DTS) and Distributed Acoustic Sensing (DAS). The appeal of DTS and DAS data is based on its high temporal and spatial sampling, allowing for very fine localization of processes in a wellbore. Furthermore, the broad frequency spectrum that especially DAS data is acquired with, enables observations, ranging from more continuous effects like oil flow, to more distinct effects like opening and closing of valves. Due to the high data volume of hundreds of Gb per well per hour, DAS data has traditionally been acquired acquisition-based, where data is recorded for a limited amount of time and processed at a later point in time. This limits the decision-making capability based on this data as reacting to events is only possible long after the event occurred. Equinor has addressed these decision-making shortcomings by building a real-time streaming solution for transferring, processing, and interpretation of its FO data at the Johan Sverdrup field in the North Sea. The streaming solution for FO data consists of offshore interrogators streaming raw DAS and DTS data via a dedicated bandwidth to an onshore processing cluster. There, DAS data is transformed into FO feature data, e.g., Frequency Band Energies, which are heavily decimated versions of the raw data; allowing insight extraction, while significantly reducing data volumes. DTS and DAS FO feature data are then streamed to a custom-made, cloud-based visualization and integration platform. This cloud-based platform allows efficient inspection of large data sets, control and evaluation of applications based on these data, and sharing of FO data within the Johan Sverdrup asset. During the last year, this FO data streaming pipeline has processed several tens of PB of FO data, monitoring a range of well operations and processes. Qualitatively, the benefits and potential of the real-time data acquisitions have been illustrated by providing a greater understanding of current well conditions and processes. Alongside the FO data pipeline, multiple prototype applications have been developed for automated monitoring of Gas Lift Valves, Safety Valve operations, Gas Lift rate estimation, and monitoring production start-up, all providing insights in real-time. For certain use cases, such as monitoring production start-up, the FO data provides a previously non-existent monitoring solution. In this paper, we will discuss in detail the FO data pipeline architecture from-platform-to-cloud, illustrate several data examples, and discuss the way-forward for "real-time" FO data analytics.