In flip-chip packaging an underfill mixture is placed into the chip-to-substrate standoff created by the array of solder bumps, using a capillary flow process. The flow behavior is a complex function of the mixture properties, the wetting properties, and the flow geometry. This paper reports on the use of a plane channel capillary flow to characterize underfill materials. The measured flow behavior provides evidence that both the contact angle (θ) and the suspension viscosity (μapp) vary with time under the Influence of changing flow conditions. This nonlinear fluid behavior is modeled for the flow of both model suspensions and commercial underfill materials using an extended Washburn model.
Objectives/Scope The benefits of real-time estimation of the cool down time of Subsea Production System (SPS) to prevent formation of hydrates are shown on a real oil and gas facility. The innovative tool developed is based on an integrated approach, which embeds a proxy model of SPS and hydrate curves, exploiting real-time field data from the Eni Digital Oil Field (eDOF, an OSIsoft PI based application developed and managed by Eni) to continuously estimate the cool down time before hydrates are formed during the shutdown. Methods, Procedures, Process The Asset value optimization and the Asset integrity of hydrocarbon production systems are complex and multi-disciplinary tasks in the oil and gas industry, due to the high number of variables and their synergy. An accurate physical model of SPS is built and, then, used to develop a proxy model, which integrates hydrate curves at different MeOH concentration, being able to estimate in real time the cool down time of SPS during the shutdown exploiting data from subsea transmitters made available by eDOF in order to prevent formation of hydrates. The tool is also integrated with a user-friendly interface, making all relevant information readily available to the operators on field. Results, Observations, Conclusions The integrated approach provides a continues estimation of cool down time based on real time field data (eDOF) in order to prevent formation of hydrates and activate preservation actions. An accurate physical model of SPS is built on a real business case using Olga software and cool down curves simulated considering different operating shutdown scenarios. Hydrate curves of the considered production fluid are also simulated at different MeOH concentration using PVTsim NOVA software. Off-line simulated curves are then implemented as numerical tables combined with eDOF data by an Eni developed fast executing proxy model to produce estimated cool down time before hydrates are formed. A graphic representation of SPS behavior and its cool down time estimation during shutdown are displayed and ready to use by the operators on field in support of the operations, saving cost and time. Novel/Additive Information The benefits of real time estimation of the cool down time of SPS to prevent hydrates formation are shown in terms of saving of time and cost during the shutdown operations on a real case application. This integrated approach allows to rely on a continue, automatic and acceptably accurate estimate of the available time before hydrates are formed in SPS, including the possibility to be further developed for cases where subsea transmitters are not available or extended to other flow assurance issues.
An Oil&Gas production system is based on the pressure balance between the reservoir and the delivery point. Since a deviation of the flowing conditions in a single point of the production system has consequences in the entire asset, a big challenge for flow assurance engineers is to preserve a stable pressure in the system, with a particular attention at the separator level. Given that the description of pressure oscillations in the systems provided by the available simulators, based on first principle, is not always enough accurate and reliable as needed for practical applications, it is fundamental to develop models and tools to help plant operators in understanding the phenomenon and properly managing it. In this context, the objective of the present work is to use large datasets, containing historical measurements of hundreds of plant signals, for identifying the most critical components of the production system with respect to the pressure oscillation phenomenon and extracting knowledge from it. To this aim, a novel indicator of the intensity of the pressure oscillation phenomenon in the separators has been firstly defined combining Discrete Short Time Fourier Transform and Principal Component Analysis. Then, a method to extract information on the causes of the oscillation phenomenon has been developed. It is based on (i) the prioritization of the plant signals importance with respect to the pressure oscillation by using the Maximum Information Coefficient and the moment-independent Kolmogorov-Smirnov distance; (ii) the aggregation of the signal for the identification of the most critical plant components; (iii) the extraction of rules describing the phenomenon by developing a Classification And Regression Tree model whose inputs are the most important signals in (i). This method has been verified considering a production plant operated by Eni where the oscillations in the separator's pressure are frequent. The results show the effectiveness of the developed indicator of the intensity of oscillation and provide hints about the physical causes.
The benefits of a multi-objective optimization approach embedding an accurate exergy model of a hydrocarbon production system are shown on a real oil and gas facility. The innovative tool developed is based on a biogenetical differential evolution algorithm, which exploits a self-adaptive iterative procedure to maximize the value of the Asset. The optimization integrates the production system considering the trade-off between hydrocarbon production, energy consumption and efficiency. The Asset value optimization is one of the most complex and multi-disciplinary task in the oil and gas industry, due to the high number of objectives and their synergy. An integrated physical model of wells, gathering network and process plant is built and, then, used for exergy efficiency and gas production optimization. Conflicts and interactions among process variables and operational constraints are treated and solved holistically by the tailored evolutionary algorithm. This has also been integrated with a quick and efficient exergetic and thermoeconomic analysis in order to grant the achievement of the maximum asset value. The multi-objective optimization provides a trade-off between the optimal values of exergy efficiency and gas production that would have been obtained independently with single-objective optimizations. In fact, the exergy efficiency optimization is driven towards a configuration having lower irreversibilities: by apportioning the entire system exergy into the exergy destroyed by each piece of equipment and by calculating its local efficiency, the most contributing to irreversibility generation is identified. Moreover, the exergy analysis is complemented with an exergetic and a thermoeconomic cost analysis: the exergy analysis, apart from describing the quality of any thermodynamic process, does not give any information about the costs of each system stream, which can be even more interesting and of practical application. Indeed, the exergy analysis is proven to be an accurate way of assessing which equipment is performing worse. Therefore, it leads to preventive corrective actions to ensure good thermodynamic performance of the system and supports the decision process in the choice of maintenance operations. The thermoeconomic analysis, based on the theory of exergetic costs, confirms the improved management of the process plant and its efficiency enhancement, without neglecting the main target of the oil and gas industries that is production maximization. The benefits of a novel multi-objective biogenetical approach to solve and optimize a hydrocarbon production system in terms of exergy efficiency and gas production are shown, with respect not only to other methodologies of literature but also in terms of exergetic and thermoeconomic costs of the solutions provided, allowing to achieve the highest value of the operated asset.
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