Machine Learning Applications for Optimizing Real-Time Drilling and Hydraulic Fracturing

报告题目：Machine Learning Applications for Optimizing Real-Time Drilling and Hydraulic Fracturing

报告 人：Yuxing Ben教授

时 间：2021年12月9日（周四）上午10:50

地 点：国家重点实验室A403学术报告厅

报告人单位：SPE杰出演讲者

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报告内容

This presentation will first introduce machine learning and its applications in oil and gas industry in the past few years, then share the experiences and learnings from three examples in real-time drilling and hydraulic fracturing.

For real-time drilling, the operator developed a general machine learning model to classify rig states. Time series data was gathered from 40 wells with 30 million rows representing three US onshore basins. The model is proved to have over 99% accuracy after being deployed on all the company's unconventional drilling rigs. The model predicts real-time rig states every second with tolerant latency. The results are used to generate drilling KPIs in real time for drilling engineers in the office, aid in directional analysis, and optimize drilling operations.

Continuous learning was used to predict wellhead pressure to avoid screenout and optimize completion costs in real time. More than 100 hydraulic fracturing stages were selected from several wells completed in the Delaware Basin. The wellhead pressure can be predicted with an acceptable accuracy by a neural network model. The ML model was tested in the Cloud, where real-time streaming data such as slurry rate and proppant concentration are gathered. The computation is fast enough that real-time wellhead pressure can be predicted.

System identification was combined with model predictive control to allow the engineers to adjust the pumping schedule and optimize hydraulic fracturing costs.

The presentation will conclude with several takeaway points including future research and development directions for machine learning applications in oil and gas industry.

报告人简介

Dr. Yuxing Ben is a reservoir engineer at Occidental, where she develops hybrid physics and data-driven solutions in the subsurface engineering technology group. She was the principal developer of machine learning technology for Anadarko's real-time drilling and hydraulic fracturing platforms. She won the best paper award from URTeC 2019 and was selected as a SPE distinguished lecturer for 2021. Prior to Anadarko, Dr. Ben served as the technical expert for Baker Hughes' hydraulic fracturing software—MFrac. She has developed complex fracture model for Halliburton and was a postdoc at MIT. She earned a BS in theoretical mechanics at Peking University, and a PhD in chemical engineering from the University of Notre Dame.

油气藏地质及开发工程国家重点实验室

SPE成都分部

西南石油大学科研处

石油与天然气工程学院

西南石油大学SPE学生分会

2021年12月7日