Jamming of fingers: an experimental study to determine force and deflection in participants and human cadaver specimens for development of a new bionic test device for validation of power-operated motor vehicle side door windows

The deformability of human fingers is central to addressing the real-life hazard of finger jamming between the window and seal entry of a power-operated motor vehicle side door window. The index and little fingers of the left hand of 109 participants and of 20 cadaver specimens were placed in a measurement setup. Participants progressively jammed their fingers at five different dorsal-palmar jam positions up to the maximum tolerable pain threshold, whereas the cadaver specimens were jammed up to the maximum possible deflection. Force-deflection curves were calculated corresponding to increasing deflection of the compressed tissue layers of the fingers. The average maximum force applied by the participants was 42 N to the index finger and 35 N to the little finger. In the cadaver fingers, the average of the maximum force applied was 1886 N for the index finger and 1833 N for the little finger. In 200 jam positions, 25 fractures were observed on radiographs; fractures occurred at an average force of 1485 N. These data assisted the development of a prototype of a bionic test device for more realistic validation of power-operated motor vehicle windows

Face Injury from Jamming in a Power-Operated Window of a Motor Vehicle: A Case Report

Introduction: Automatic power-operated windows of modern motor vehicles present a risk of injury due to jam events. Case Report: A 41-year-old man suffered an injury of his gingiva upon jamming of his face between the window and seal entry. The motor vehicle window that led to the injury had no closing force restriction. Discussion: Equipping power-operated windows with closing force restriction should be required

Effect of reservoir and production system integration on field production strategy selection

In petroleum engineering studies, the integration of reservoir and production system models can improve production forecasts. As the integration increases computation time, it is important to assess when this integration is necessary and how to choose a suitable coupling methodology. This work analyzes the influence of this integration, for a petroleum field in the development phase, evaluating the effects on the production strategy parameters. We tested a benchmark model based on an offshore field in Brazil so we could validate the solution in a reference known model. This work continues the research of Von Hohendorff Filho and Schiozer (2014, 2017) and aims to improve step 11 of the 12-step reservoir development and management methodology by Schiozer et al. (2015). The solution is tested in a reference model. Using the integrated production system and reservoir models from step 11 of the methodology, we re-optimize the production strategy of a standalone production development, while evaluating net present value as the objective function. We adapted an assisted workflow to include the optimization of new variables, such as pipe diameters of the well systems and gathering systems, platform positions, and artificial lift application, and compared these with the production strategy obtained from the same benchmark in a standalone approach. Comparing the integrated standalone and integrated production strategies, we observed important changes that indicate the need to integrate reservoir and production models. The optimized integrated systems resulted in significantly increased net present values, maintaining the same oil recovery factor while requiring lower initial investment. We implemented the best integrated production strategy and the integrated production strategy derived from the standalone case in the reference model which, in this case, represents a real field (emulating a real situation). Integration in the implementation step impacted the production forecast more than the optimization step, demonstrating the benefits of integrating reservoir and production systems to increase project robustness

Effect of reservoir and production system integration on field production strategy selection

International audienceIn petroleum engineering studies, the integration of reservoir and production system models can improve production forecasts. As the integration increases computation time, it is important to assess when this integration is necessary and how to choose a suitable coupling methodology. This work analyzes the influence of this integration, for a petroleum field in the development phase, evaluating the effects on the production strategy parameters. We tested a benchmark model based on an offshore field in Brazil so we could validate the solution in a reference known model. This work continues the research of Von Hohendorff Filho and Schiozer (2014, 2017) and aims to improve step 11 of the 12-step reservoir development and management methodology by Schiozer et al. (2015). The solution is tested in a reference model. Using the integrated production system and reservoir models from step 11 of the methodology, we re-optimize the production strategy of a standalone production development, while evaluating net present value as the objective function. We adapted an assisted workflow to include the optimization of new variables, such as pipe diameters of the well systems and gathering systems, platform positions, and artificial lift application, and compared these with the production strategy obtained from the same benchmark in a standalone approach. Comparing the integrated standalone and integrated production strategies, we observed important changes that indicate the need to integrate reservoir and production models. The optimized integrated systems resulted in significantly increased net present values, maintaining the same oil recovery factor while requiring lower initial investment. We implemented the best integrated production strategy and the integrated production strategy derived from the standalone case in the reference model which, in this case, represents a real field (emulating a real situation). Integration in the implementation step impacted the production forecast more than the optimization step, demonstrating the benefits of integrating reservoir and production systems to increase project robustness

Oscillation mitigation in subsurface and surface couplings using PID controllers

Simulation coupling subsurface (reservoir) and surface (network) systems is a challenging problem, especially for computationally intensive multi-reservoir models and complex surface network facilities. Integration of petroleum systems can be done using different simulators (explicit coupling) or considering all individual components of the system in one simulator (implicit coupling). The explicit method is more flexible, allowing the integration of commercial-off-the-shelf simulators. However, as a drawback, it can yield oscillatory solutions. In this work, a new framework for mitigating explicit coupling numerical instabilities (oscillations) is developed by recasting the problem in a control setting. Results from this work show that explicit coupling without a mechanism to avoid numerical instabilities presents oscillations that can grow throughout the simulation. In order to mitigate the numerical oscillations we develop a framework based on a feedback control system, namely a PID (Proportional, Integral and Derivative) controller. The PID controller, with parameters (KC, τI, τD) tuned manually for a group of well settings, adjusts the traditional IPR curve generated by the reservoir simulator so that the error between the bottom-hole pressure calculated by the reservoir simulator (BHPRS) and the bottom-hole pressure defined in the operating point (BHPOP) is minimal. In this case, a qOP value representative for the entire time step (next time step) is obtained. The new methodology was tested in a synthetic numerical model (UNISIM-I-D) based on Namorado field (Campos Basin – Brazil), comprised by 20 satellite wells (7 injectors and 13 producers). The PID control reduces the rate and pressure oscillations in the case study, and results converge with base case scenario, which represents the network system of producer wells by proper pressure drop tables18