|Theme||Visible||Selectable||Appearance||Zoom Range (now: 0)|
Methane (CH4), the primary constituent of natural gas and is the second-most abundant greenhouse gas after carbon dioxide (CO2), accounts for 16% of global emissions. The lifetime of methane in the atmosphere is much shorter than CO2, but CH4 is more efficient at trapping radiation than CO2. Pound for pound, the comparative effect of CH4 is more than 25 times greater than CO2 over a 100-year period. Natural-gas emissions from oil and gas facilities such as well sites, refineries, and compressor stations can have significant safety, economic, and regulatory effects. Continuous emission detection systems enable rapid identification and response to unintended emission events.
Following the trend of energy efficiency, the Oil&Gas sector is looking continuously to sustainable solutions aimed to reduce carbon footprint while maintaining competitiveness. The market shows that a clever way for O&G field is the implementation of Organic Rankine Cycle technology, which turns waste-heat into useful power, with minimum impact on the existing facilities. An ORC unit can exploit waste heat from several sources. Different ORC applications within the O&G field were studied.
The study conducted evolved in two phases. The first one aimed to identify the most suitable waste heat sources unexploited in the O&G facilities. The second one explored the technical and economic analysis of different configurations, in order to understand the best ORC solution for this industrial sector (in terms of process parameters, equipment and layout).
A proved ORC application was in the Gas-compressor-stations along the pipelines where multiple gas-turbines operating in open-cycle are used as prime-movers for compressors. Although reliable and flexible, they waste a significant amount of energy that can be converted into useful power by means of an ORC system, a clear opportunity to boost the overall efficiency of the plant.
Other applications regarded the exploitation of hot streams in associated petroleum gas (APG) process carried-out within refineries. Due to its poor chemical composition, APG are typically burned via torches, thus wasted. ORC can exploit that energy to produce electricity by means of a flare-gas-boiler which heats up a vector fluid to feed the turbogenerator.
Beside those waste-heat streams, another potential form of energy was available in gas pressure-letdown stations, where lamination valves dissipate the potential energy contained in the pressurized gas. In this scenario, the Gas-expander technology (similar to ORC) can be a valuable alternative and a more efficient solution. It consists in a turbine through which the NG at high pressure, rather than being laminated, expands to produce work, thereafter converted into electricity by a generator.
This paper will present the above-mentioned solutions, employed both individually or combined.
Considering a large-scale application, the paper will show how the implementation of the ORC recovery systems represents other than a way to meet sustainability targets also a remarkable and profitable business for O&G companies. Furthermore, the Gas Expander technology represents a solution to improve the energy efficiency of NG transmission and distribution networks, as well as upstream and downstream facilities.
Gathering system optimization is no longer an option for any operator, it is a necessity. The primary objective for an operator is to match the demand at the sales point to avoid penalties due to minimum volume commitment (MVC) agreement.
With time, operators face challenges to adhere to this demand due to inefficient production surveillance, production optimization & capital project evaluation. Also, with oil & gas price at its lowest, operators are now facing increased cost pressures leading to less personnel for field operations.
Operators are now collecting & managing a lot of data than ever from their operations as part of their digital journey, which enables them to improve the network performance using machine learning. Insights or details missed by operators or conventional first principle-based models can be derived & explained using these models.
The paper talks about this novel approach of machine learning-based collaborative platform called ‘Digital Twin’. Digital Twin is based on automated data flow from ingesting data from SCADA system, pushing required data to the steady-state simulator, receiving simulated results, & finally viewing the machine learning augmented simulation results on the platform.
In this solution, the Bayesian Changepoint model is trained on wellhead flowrate & line pressure to identify critical wellpads & pipeline sections needing intervention, in real-time.
Deep learning-based neural network algorithm is then integrated as part of the digital twin to accurately predict the scenario of field changes. This neural network algorithm is trained on the actual field data along with steady-state simulation results. The model output helps the operator to identify the deviation of the simulation results from actual field data, then improving the simulation results for certain scenarios of field changes.
The improvement in accuracy by about 15% was realised with the neural network structure of 5 layers (3 hidden layers + input layer + output layer) through its machine learning augmented simulation results. With improved accuracy, operators are accepting the solution to make real-time operational decisions through the automated architecture resulting in a 95% reduction in time for simulating scenarios.
This paper aims to provide a blueprint of the digital twin solution & explaining its potential for easy scalability across different gathering networks.
This case study describes how gas condensates within a subsea tieback system behave very differently to condensed water from a wet-gas system and therefore a pseudo dry-gas system needs to be configured differently for gas-condensate developments. The reciprocating compression division manufactures and services compression and engine systems that are used in a variety of applications, including the transmission of natural gas across domestic and international pipelines. Pioneer shut in 8,000 BOE/D production in its West Panhandle field in Texas on 6 March due to a compression station fire. Planning to use idle compressors, production is expected to restart later this month or in early April. As compressor stations are added to the natural gas gathering and transmission networks, the potential noise issues are coming under increasing public scrutiny at the same time as regulations are being rolled back.
Around $90 billion, or 40% of the revenue from the top 50 players in the global service market, could potentially be replaced by energy transition projects, such as clean energy infrastructure and renewable energy production development services. The Hydrogen Offshore Production project identifies an alternative to decommissioning by providing reuse options for offshore infrastructure. It aims to prove the feasibility of decentralized hydrogen generation, storage, and distribution to provide a bulk hydrogen solution. Five key steps for shaping renewable energy projects are discussed. As part of the contract, Wood will provide the topside modifications needed for the Snorre A and Gullfaks A platforms to integrate the Hywind floating wind park with existing systems powering the facilities.
One of North America’s biggest midstream companies is getting a new chief in the new year. He will oversee the construction of a 1,200-mile-long pipeline that has been a decade in the making. The pipeline continues to operate as courts weigh expedited appeals from the Army Corp of Engineers and Energy Transfer. The country’s once-thriving railway system has skidded to a stall, falling victim to low crude-oil prices, reduced demand, and government-imposed oil production cuts. US Court decisions put two major pipelines on hold and led to the cancellation of another pipeline project within days of each other.
We introduce a linearization procedure that transforms gas pipeline transient optimization problems to linear programs using a piece-wise local discretization scheme that enables closed-form solution of partial differential equations for gas flow. An entire pipeline system including junctions and compressors is modeled, where feasible regions for compressor operation are approximated by inscribed convex polygons. The reduction to linear programming enables acceleration of solution time for nonlinear optimization of large-scale systems by several orders of magnitude compared with previous methods, and enables the incorporation of binary decision variables in future extensions. This study compares numerical solutions with previously validated methods to assess accuracy and computation time using a case study based on a section of an actual pipeline system.
Bu, Yaran (China University of Petroleum, Beijing) | Wu, Changchun (China University of Petroleum, Beijing) | Zuo, Lili (China University of Petroleum, Beijing) | Chen, Qian (China University of Petroleum, Beijing)
A gas pipeline company as a common carrier is bound to provide transmission capacity to shippers under contracts, in which the maximum daily throughputs for each shipper are specified, and any daily nomination below that should be satisfied except for some inevitable reasons. To clarify the gas transmission responsibility of the company, service reliability is suggested to be included in the contracts, describing the probability that the nomination of the shippers can be satisfied. An analytical method combined hydraulic simulation, considering the uncertainty of both the pipeline system and gas demands, is proposed to assess the service reliability. The consequences caused by the compressor station or pipe segment failure of a pipeline are simulated by Stoner Pipeline Simulator (SPS). The probability distribution of the maximum actual flows of the pipeline system is determined based on probability theory and random process. Thus, the service reliability associated with firm contracts can be obtained, and combined with predicting the gas demands assigned in firm contracts, the service reliability associated with an interruptible contract is obtained with convolution. The study provides an early warning method for both shippers and pipeline companies to cope with a potential gas shortage. It can be further applied to the service reliability assessment of a gas pipeline network.
The pilot utilized sensor technology originally deployed by Nasa for the Mars Curiosity Rover to collect methane emissions data live-streamed from a drone. BP said it plans to deploy the technology to all of its North Sea assets, including ETAP and Glen Lyon, in 2020. Equinor Technology Ventures and OGCI Climate Investments have agreed to back the tech developer, which integrates its SeekIR miniature gas sensors onto drones to detect, localize, and quantify carbon emissions. Equinor is working on a natural language processing tool that could combine data sources and help planners anticipate the issues that affect onsite operational safety. In this case, a film guides the audience to make positive, personal choices whenever planning and operating a work at height.
As the gas industry landscape gets ever more complicated with thousands of new players entering the gas business, the challenges for efficient management of day-to-day operations are mounting. Situation on the gas market with ever changing nominations for transport pose significant problems for TSO’s to change their network mode (compressor stations, regulators, valves) to match the today and tomorrow’s demand.
There is a need to dynamically manage network switching and network elements to match the situation when nominations can dramatically change every hour. Gas companies are looking for a tool that can optimise the current situation and nearest future with as low as possible transport cost while fulfilling the contract constraints.
We have found that after talking to a number of TSO’s that the actual question posed by dynamic optimisation varies from one company to another. Therefore, it is impossible to create one universal algorithm that would fulfill the needs of everyone (apart from brute force, but that is not a task for today’s computers). We have devised a way how to supplement this lack of raw computing power with human interaction and knowledge of individual network.
We will talk about our experience of creating a tool that assists control room dispatchers in quick reaction to changing transport situation. This tool we call Net Schedule Management (or NSM in short) and it consists of two modules – one that archives a gas day simulation and cleans bad SCADA data from it and one that actually optimises the network mode.
To dynamically optimise network, operator can pull a historical day that most matches the current situation and predicted consumptions and transport, simulate different scenarios and try to improve the criteria of fuel gas while observing the pressure limits. Steady state optimisation can help him as well as a library of network switching, but ultimately it is the human experience and knowledge of the network that does the trick. We will present a case study that confirms 4% - 23% daily savings of fuel gas on a medium complexity gas network using the NSM tool.