This paper focuses on the use of gas turbines (GTs) equipped with Dry Low Emissions (DLE) combustion technology in offshore oil and gas (O&G) applications. The paper presents information from offshore GT units and discusses improvements in operational reliability and fuel flexibility that have been achieved as a result of extensive testing on combustion systems. These improvements, coupled with significant NOx emissions reductions have made DLE gas turbines a viable alternative to non-DLE gas turbines, the latter of which have traditionally dominated the offshore market. Field operating data will be presented on three lightweight industrial gas turbine models that have been offered with DLE as a standard configuration for over 20 years. Currently, 394 units operate in offshore applications. The DLE configuration has accumulated more than 22 million hours of service. The paper outlines the performance, reliability, and fuel profiles of the current offshore fleet, confirming that DLE combustion systems have now achieved reliability on par with non-DLE combustion systems. A brief review of the testing experience proving the reliability and the range of fuels that can be burned within the DLE system will also be discussed in the paper.
This paper focuses on overcoming the challenges of developing integrated gas-to-power infrastructure. It discusses strategies for driving the viability of LNG-to-power megaprojects– providing considerations for financing and risk mitigation. Additionally, it examines the merits of adopting an integrated approach to project development, with a close look at the Gas Natural Acu (GNA-1) project in Rio de Janeiro, Brazil, which is the largest LNG-to-power development in Latin America. For the project, the power plant's original equipment manufacturer (OEM) became a 33% equity investor. This, along with commitments from other strategic equity investors, significantly de-risked GNA-1 and played an important role in reaching final investment decision (FID).
The paper offers detailed insight into the inherent risks and challenges LNG-to-power project stakeholders encounter during development. It also discusses how these risks can be addressed and how projects can be made more attractive to institutional investors through innovative project financing structures. The ability to secure financing from a variety of equity sources with strategic interests in the project is critical, as it allows the sponsors to raise a greater amount of debt than would otherwise be possible. In doing so, sponsors are able to assume limited recourse after project start-up and in many cases, enjoy customized loan repayment profiles, which contributes to better overall project economics. Co-financing also reduces investment risk and allows for capital to be raised at a relatively low cost, which benefits all stakeholders.
This paper will focus on solutions and strategies for conserving weight and space, reducing emissions, and leveraging data to optimize the performance of rotating equipment on floating, production, storage, and offloading (FPSO) vessels. It will discuss design considerations for gas turbines in offshore applications (i.e., dry-low emissions technology, use of lightweight components, etc.) The paper will also outline a holistic digital lifecycle approach to FPSO topsides, which can help reduce capital and operating expenses, shorten project development cycles, and decrease offshore manpower requirements. For illustrative purposes, the paper will discuss specific power and compression solutions that were implemented on various offshore projects in 2017 - 2018, ranging from Offshore Brazil to the Bering Sea. The paper will outline how the equipment configurations helped operators meet horsepower requirements and emissions targets, as well as CAPEX and OPEX objectives. Additionally, it will discuss how digital transformation can be leveraged to optimize FPSO lifecycle performance, delivering benefits such as 4-12 week reduction in project cycle times, $7 million reduction in CAPEX, and $60 - $100 million reduction in OPEX over a 10-year period.
This paper will focus on the application of lithium-ion energy storage solutions (ESS) for offshore oil and gas (O&G) installations. It will discuss the benefits that can be achieved by integrating energy storage in hybrid power plants, using the West Mira semisubmersible installation in the North Sea as a representative case study. West Mira will be the world's first modern drilling rig to operate a low-emission hybrid (dieselelectric) power plant using lithium-ion batteries. The integration of energy storage with the power supply and distribution system of a drilling rig represents an important step towards improving the environmental sustainability of the offshore oil and gas industry by reducing emissions and paving the way to harnessing clean but intermittent renewables, such as offshore wind. Offshore rigs have highly variable power consumption for drilling and dynamic positioning. By incorporating energy storage, it is possible to reduce the runtime of combustion engines and also keep them operating on an optimized combustion level. The installation of an ESS on West Mira will result in an estimated 42% reduction in the runtime of on-platform diesel engines, reducing CO2 emissions by 15 percent and NOx emissions by 12 percent, which is equivalent to annual emissions from approximately 10,000 automobiles. The batteries on West Mira will be charged from the rig's diesel-electric generators and used for supplying power during peak load times. In addition, they will serve as backup to prevent blackout situations and provide power to the thrusters in the unlikely event of loss of all running machinery.
This paper will discuss when it is advantageous (in the context of an offshore oil and gas environment) to process data at the network edge (in close proximity to equipment assets) or to stream data to a cloud-based Internet of Things (IoT) platform for analysis. It will offer an objective assessment of both approaches and provide recommendations for securing data in both cases, as part of an overarching cybersecurity strategy.
IoT has opened the door to significant efficiency gains in the oil and gas industry. This is particularly the case in the offshore sector, where there is a pressing need to reduce costs and maximize equipment availability. In some cases, it is advantageous to process data in close proximity to equipment assets (i.e., at the edge). In others, it makes more sense to securely stream data to a cloud- based IoT platform and harness artificial intelligence (AI) to aid in decision making. In certain cases, both architectures can be utilized in compliment to one another.
Many factors need to be taken into consideration when evaluating an edge or cloud-based approach. Some of these include data volume, transmission and processing speed, control of data, cost, etc. Edge computing can be used to streamline and enhance the efficiency of data analytics. In certain applications, this can mean the difference between analyzing a performance failure after the fact, and pre-empting it in the first place, which in the offshore environment could potentially translate into millions of dollars per day.
On the other hand, there are situations where it is beneficial to store large volumes of data on a cloud-based platform. For example, if the goal is to leverage advanced IoT-based industrial analytics to optimize an entire fleet of a certain type of equipment, the cloud may be the best solution. Cybersecurity is another consideration. Attacks on critical infrastructure have risen significantly over the course of the past year. As more Intelligent Electronic Devices (IEDs) are deployed in the oil and gas industry to optimize efficiency, Industrial Control Systems (ICSs) are increasingly vulnerable. As a result, the threat extends beyond proprietary data to mission-critical operational technology (OT) assets and equipment.
Cybersecurity standards and layered, defense-in-depth models have grown in response to the frequency and sophistication of cyber attacks. Additionally, recent advances in cyber defense technology incorporate small, kilobit-sized embedded software agents to monitor networks for anomalies that could signal an intrusion. This paper will explore new cybersecurity threats to oil and gas assets, as well as strategies operators can employ to defend against them, whether using an edge or cloud-based platform, or both.
The Internet of Things (loT) has paved the way for significant efficiency gains in the oil and gas industry. One concept that has garnered significant attention is the "digital twin". However, there remains a great deal of confusion surrounding what a digital twin actually is and how it can be harnessed to add value to oil and gas operations. Some use digital twin as a synonym for their 3D plant models, others for their predictive maintenance solutions, or their simulation models. The bottom line is that the digital twin is all of these and more and unless operators look at it holistically, they are likely to miss out on some of the benefits.
Digital twins afford companies a number of advantages that would otherwise not be possible, including the ability to run risk analyses, health assessments, and what-if scenarios in real-time; the ability to train personnel in a 3D immersive, risk-free environment; and the capability to detect faults early before control limits are reached. This paper/ presentation will elaborate on how digital twins can be used to enhance efficiency and will address their use in the wider context of the oil and gas industry – with a particular focus on its impact on reducing risk and cost during both the project and operational phases of the asset lifecycle.
The objective is to demystify the digital twin, outline the advanced capabilities it enables and illustrate how oil and gas operators can use this concept to improve their competitive advantage.
In recent years, the oil and gas industry has gained greater operational efficiencies and productivity by deploying advanced technologies, such as smart sensors, data analytics, artificial intelligence and machine learning — all linked via Internet of Things connectivity. This transformation is profound, but just starting. Leading offshore E&P operators envision using such applications to help drive their production costs to as low as $7 per barrel or less. A large North Sea operator among them successfully deployed a low-manned platform in the Ivar Aasen field in December 2016, operating it via redundant control rooms — one on the platform, the other onshore 1,000 kilometers away in Trondheim, Norway. In January 2019, the offshore control room operators handed over the platform's control to the onshore operators, and it is now managed exclusively from the onshore one. One particular application — remote condition monitoring of equipment — supports a proactive, more predictive condition-based maintenance program, which is helping to ensure equipment availability, maximize utilization, and find ways to improve performance. This paper will explain the use case in greater detail, including insights into how artificial intelligence and machine learning are incorporated into this operational model. Also described will be the application of a closed-loop lifecycle platform management model, using the concepts of digital twins from pre-FEED and FEED phases through construction, commissioning, and an expected lifecycle spanning 20 years of operations. It is derived from an update to a paper presented at the 2018 SPE Offshore Technology Conference (OTC) that introduced the use case in its 2017-18 operating model, but that was before the debut of the platform's exclusive monitoring of its operations by its onshore control room.
In recent years, there has been a great deal of discussion surrounding the topic of mid-scale vs. large-scale liquefaction trains in LNG export facilities. While in some select cases, mid-scale trains (0.5 - 2 million tonnes per year (typ)) may offer commercial, executional, and financial advantages, the decision is not a simple matter of one-size-fits-all, as large trains ( 3 million tpy) offer economies of scale, among other benefits. As this paper will explain, choosing a solution that delivers the most value over the life of a facility is contingent on the operator's specific objectives and circumstances. Ultimately, train size and liquefaction licensor selection have only a marginal impact on project capital costs as long as the total facility achieves a minimum of 8 million tpy (Russell, 2018). The paper will offer objective cases for building both mid-and large-scale trains and outline key areas for project stakeholders to consider when making an informed decision regarding capacity and liquefaction licensor. It will also discuss important considerations for optimizing LNG plant design, such as mechanical driver selection, modularization, and the incorporation of digital technologies. The paper will conclude by discussing the importance of adopting a holistic strategy that integrates the entire gas value chain when attempting to drive out costs, minimize risk, and maximize overall project economics.
This paper provides the validation test results of preheat sequence applied to induction motors at two Test Facilities and offshore application for operation in the Gulf of Mexico.
Although the objective of preheating Induction Motors (IM) is to lower the viscosity of the lubricant oil by 2 orders of magnitude (from 1000 cP to 10cP) for extending Electric Sumersible Pump (ESP) run life, this paper is exclusively focused on motor preheating results.
The motor is energized with low voltage at a frequency of 120Hz maintaining the voltage low enough in order to keep the supplied shaft torque under the system's breakaway torque; thus the shaft never spins. The Medium Voltage Drive (MVD) is a Variable Frequency Drive output power determines heat rate that is adjusted to obtain temperature slope of 1°F/min specified by the project.
The motor is modeled electrically and magnetically through Finite Element Analisys (FEA) to estimate its power losses; the motor internal temperatures can be predicted by the Motor-CAD (Computer-Aided Design) thermal model which is calibrated by winding resistance change and skin tempeperature measurement.
The systems for validation were: First test facilities: 1500hp Induction Motor coupled to a pump and driven with a 2500hp MVD Second test facilities: 1500hp Induction Motor coupled to a dyno and driven with a 2500hp MVD. Offshore: Five 1500hp ESPs driven with 2500hp MVD each.
First test facilities: 1500hp Induction Motor coupled to a pump and driven with a 2500hp MVD
Second test facilities: 1500hp Induction Motor coupled to a dyno and driven with a 2500hp MVD.
Offshore: Five 1500hp ESPs driven with 2500hp MVD each.
The results at first and second test facilities and offshore in the Gulf of Mexico demonstrate the preheat sequence can be successfully implemented in the field by using existing MVD with little software changes in order to apply low voltage at 120Hz without spinning the rotor. The stator current and induced current on the rotor make motor internal temperature (including lubricant oil) to rise achieving different temperature slopes. Temperature slopes vary in function of applied motor current (there was no need of overpassing motor nominal current on any test), motor thermal capacity, initial motor temperature, and external temperature.
All tested motors are very similar and was found that Keeping heating power at around 34kW, winding temperature rise can be achieved at a rate of 1.52°F/min at an initial temperature of 38°F and 1.2°F/min at an initial temperature of 148°F. Temperature rise rate at the motor air gap (actually filled with oil) and bearings location can also be predicted by the motor thermal model.
The required preheating time is previously calculated to reach less than 10cP viscosity of lubricant oil to guarantee safe startup without the occurrence of bearing spin; otherwise bearing friction torque overcomes the T-ring retaining torque causing bearing(s) damage.
When the need of preheating the induction motor of electric submersible pumps installed in deepwater applications was identified, there was no clear means to make it possible. This was the first time that concept was applied and successfully implemented in the field.
A second milestone was to preheat the motor with the MVD without adding equipment. Among five potential methods for preheating the motor, the selected scheme worked as expected with minimum MVD software changes.
Digitalization Deployed: The Ivar Aasen Field Development Project: The Pursuit of an Ultra-low Manned Platform Pays Dividents in the North Sea