ABSTRACT
The use of digital manufacturing for the construction of orthosis and prostheses has become common since the popularization of 3D printers and the advent of Industry 4.0. Furthermore, due to the fact that the manufacture of orthosis is interactive and for personal use, generic production is difficult. In this sense, the large-scale production of these products lacks of improvements, standardization of processes and production optimization. An aggravation of this is the recent social distance due to the COVID-19 pandemic, which makes the use of temporary orthosis made in 3D printers to have a recent growth. Parallel to this, the use of multi-lattice inner structures for internal structuring of prints has also been increasing and taking on a more consolidated form. This article aims to present the multi-lattice optimization as a solution to this problem, in order to reduce material waste while maintaining the mechanical behavior of printed parts. © 2022, The Author(s), under exclusive licence to Springer-Verlag France SAS, part of Springer Nature.
ABSTRACT
The COVID-19 pandemic wreaks havoc in supply chains by reducing the production capacity of some essential suppliers, closure of production facilities or the absence of infected workers. In this paper, we present three decision support models for a plant manager to help in deciding on (a) the level of protection of the workforce against the spread of the virus in the absence of regional protection measures, (b) on the duration of the protection, and (c) the level of protection of the workforce with regional protection measures enforced by health authorities. These decision models are based on a SIS epidemiological model which takes into account the possibility that a worker can infect others but also that even when recovered can be infected again. The first and third models prescribe how, in time, the protection effort in terms of prophylactic measures must be deployed. The second model extends the first one as it also determines the length the protection effort must be deployed. The proposed models have been applied to the case of a meat processing plant that must satisfy the demand of a large-scale retailer. Clearly, to achieve production targets and satisfy customers' demand, plants in this labor-intensive industry rely on the number of healthy workers and the service level of suppliers. Our results indicate that these models provide managers with the tools to understand and measure the impact of an infection on production and the corresponding cost. Along the way, this work illustrates the ripple effect as suppliers affected by the pandemic are unable to fulfill the processing plant requirements and so the retailer's orders. Our findings provide normative guidance for supply chain decision support systems under risk of pandemic induced disruptions using a quantitative model-based approach.
ABSTRACT
Reservoir surveillance and production optimization will remain at the forefront of company strategies in the new post-COVID19 environment. We anticipate that companies will focus more on producing assets and go the route of production enhancement rather than exploration. Accordingly, production logging will remain an important surveillance method in evaluating and strategizing production-optimization schemes pertaining to flow-characterization from reservoir-to-wellbore. This work is culmination of operational and technical excellence that enabled the revival of a loaded-up well through simultaneous lifting-and-logging technique. Conventionally, wireline is the preferred mode of conveyance for production-logging;however, well must be continuously flowing throughout acquisition timeframe. Kicking-off the well using nitrogen-lift and then bringing in wireline-unit for production-logging in Well A-4 was not feasible as previous attempts confirmed well to load-up in few hours post-offloading. Therefore, success of this project was heavily dependent on initial planning stage, which accounted for all available data including production-history, well-events, intervention-details, fluid analysis and well load-up behavior. Next, a multi-domain approach was adopted while bringing-out each domain from its silos and strategize collectively to simultaneously kickoff the well with nitrogen and acquire real-time downhole production-logging data through smart-coiled-tubing (CT). This was first implementation of concurrent lifting and logging operation in Pakistan. By deploying the approach mentioned above through smart CT (using optical-telemetry-link inside the CT-string coupled with downhole-assembly), synchronized lifting-and-logging operation was carried-out successfully. Well was observed to swiftly go back to load-up conditions post-kickoff;however, continuous well dynamics monitoring downhole enabled us to log perforated interval across multiple time domains. Well was activated through CT nitrogen-injection but depicted continuous loading tendency, which was captured downhole in form of flow-transients. Real-time job optimization ensured vigilant monitoring and selection of right-time to acquire meaningful zonal-contribution data for evaluation and diagnostic solutions. Finally, operational excellence was complemented through technical data analysis and interpretation, integrating passes data with transients and stationary measurements. Ultimately, acquired data analyzed using an integrated lens involving fluid velocities, downhole density, temperature, and water hold up data. Consequently, enabling us to decipher gas and water-entries on a zonal-basis across perforated sandstone reservoir. Copyright © 2022, Society of Petroleum Engineers.
ABSTRACT
PETRONAS completed well H-X on B field in Malaysia with a digital intelligent artificial lift (DIAL) gas lift production optimization system. This DIAL installation represents the first ever successful installation of the technology in an Offshore well for Dual String production. This paper provides complete details of the installation planning and operational process undertaken to achieve this milestone. DIAL is a unique technology that enhances the efficiency of gas lift production. Downhole monitoring of production parameters informs remote surface-controlled adjustment of gas lift valves. This enables automation of production optimization removing the need for well intervention. This paper focusses on a well completed in November 2020, the fourth well to be installed with the DIAL technology across PETRONAS Assets. The authors will provide details of the well and the installation phases: system design, pre-job preparations, improvements implementation, run in hole and surface hookup. For each phase, challenges encountered, and lessons learned will be listed together with observed benefits. DIAL introduces a paradigm shift in design, installation and operation of gas lifted wells. This paper will briefly highlight the justifications of this digital technology in comparison with conventional gas lift techniques. It will consider value added from the design stage, through installation operations, to production optimization. This DIAL system installation confirms the ability to be implement the technology in a challenging dual string completion design to enable deeper injection while avoiding interventions on a well with a greater than 60-degree deviation. With remotely operated, non-pressure dependent multi-valve in-well gas lift units, the technology removes the challenges normally associated with gas-injected production operation in a dual completion well - gas robbing and multi-pointing. Despite the additional operational & planning complications due to COVID-19 restrictions, the well was completed with zero NPT and LTI. Once brought online, this DIAL-assisted production well will be remotely monitored and controlled ensuring continuous production optimization, part of PETRONAS' upstream digitization strategic vision. © Copyright 2021, Society of Petroleum Engineers