通过使用 AI 优化 ESP 的运行参数来提高石油产量
技术
- 分析与建模 - 机器学习
- 传感器 - 电表
适用行业
- 石油和天然气
适用功能
- 维护
用例
- 资产健康管理 (AHM)
- 泄漏与洪水监测
服务
- 数据科学服务
- 测试与认证
挑战
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。
客户
拉雷多石油
关于客户
拉雷多石油公司是一家领先的能源公司，专注于石油和天然气资产的收购、勘探和开发。运营效率和生产优化是公司的首要任务。目前，拉雷多石油公司正在投资数字技术，以确保资产完整性、降低运营成本并提高生产率。
解决方案
解决方案结果：
- 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
- 模拟场景的交互式可视化
- Streamlight仪表板作为参数推荐测试的原型平台
- ML 和基于物理的模型的组合
数量效益
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