- 分析与建模 - 机器学习
- 应用基础设施与中间件 - 数据可视化
SoftServe 帮助 Laredo 使用深度神经网络 (DNN) 算法和优化技术在 Amazon Web Services (AWS) 上构建了一个全面的解决方案，包括针对最终用户的广泛可视化和解释。它通过模拟具有不同场景的遥测和生产来对整个系统进行建模。此外，它还允许拉雷多石油公司找到最佳的控制措施，以满足电机温度、电压、进气压力以及气体或水量等限制条件。该算法可以适应各种 ESP 系统，并为操作员提供最佳的日常控制。
- 根据从 50 个 ESP 中收集的数月数据确定的具有高预测精度的 ML 模型
- 将自动重新训练的管道部署到 AWS 环境
- ML 和基于物理的模型的组合
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Taking Oil and Gas Exploration to the Next Level
DownUnder GeoSolutions (DUG) wanted to increase computing performance by 5 to 10 times to improve seismic processing. The solution must build on current architecture software investments without sacrificing existing software and scale computing without scaling IT infrastructure costs.
Remote Wellhead Monitoring
Each wellhead was equipped with various sensors and meters that needed to be monitored and controlled from a central HMI, often miles away from the assets in the field. Redundant solar and wind generators were installed at each wellhead to support the electrical needs of the pumpstations, temperature meters, cameras, and cellular modules. In addition to asset management and remote control capabilities, data logging for remote surveillance and alarm notifications was a key demand from the customer. Terra Ferma’s solution needed to be power efficient, reliable, and capable of supporting high-bandwidth data-feeds. They needed a multi-link cellular connection to a central server that sustained reliable and redundant monitoring and control of flow meters, temperature sensors, power supply, and event-logging; including video and image files. This open-standard network needed to interface with the existing SCADA and proprietary network management software.
Refinery Saves Over $700,000 with Smart Wireless
One of the largest petroleum refineries in the world is equipped to refine various types of crude oil and manufacture various grades of fuel from motor gasoline to Aviation Turbine Fuel. Due to wear and tear, eight hydrogen valves in each refinery were leaking, and each cost $1800 per ton of hydrogen vented. The plant also had leakage on nearly 30 flare control hydrocarbon valves. The refinery wanted a continuous, online monitoring system that could catch leaks early, minimize hydrogen and hydrocarbon production losses, and improve safety for maintenance.