Enhancing Unconventional Well Management through Continuous Performance Tracking with Hybrid Models

Recorded On: 10/09/2023

Unconventional oil and gas production has made a significant contribution in the past decade, yet many of these wells are not managed to their fullest potential. There is a significant opportunity to optimize well performance through continuous estimation and tracking of well performance for large-scale operations.

However, understanding and predicting well performance in unconventional reservoirs poses a significant challenge due to the complexity of capturing the relevant physics of multi-stage fractured horizontal wells (MFHWs). Traditional mechanistic or numerical models are not suitable for field-scale applications, as they may require information that is not easily available, are interpretive, need arduous manual efforts, have long runtimes, or produce results with high uncertainty.

In recent years, hybrid models have gained popularity as a solution to these challenges. These models combine physics-informed data-driven methods to accurately model transient well performance with low input requirements, fast convergence, and high accuracy. They enable fast decision making compared to pure numerical simulation, while reducing overfitting compared to pure data driven solution.

In this talk, we discuss the application of hybrid models in addressing major challenges in unconventional reservoirs, including well performance evaluation, artificial lift life cycle management, performance insights, production optimization, well interference, and forecasting.

This webinar is categorized under the Data Science and Engineering Analytics and Reservoir technical disciplines.

All content contained within this webinar is copyrighted by Utkarsh Sinha and its use and/or reproduction outside the portal requires express permission from Utkarsh Sinha.

Utkarsh Sinha

Utkarsh Sinha is an Associate Research Engineer at Xecta Digital Labs. He’s responsible for the R&D activities in unconventional reservoirs, serving digital solutions for the energy industry by fusing physics and data-driven methods for applications in solving reservoir and production engineering problems. Utkarsh is a member of the Society of Petroleum Engineers and has served in several roles including advisory positions,chairperson, and technical committee member in industry initiatives, and authored published manuscripts on applications of machine learning in reservoir engineering in leading journals and conference proceedings. He has a B.Tech. degree in Chemical Engineering from VIT University, India, and M.Eng degree in Petroleum Engineering from the University of Houston.

Yuxing Ben (Moderator)

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 won the best paper award from URTeC 2019 and was selected as a SPE distinguished lecturer for 2021 on “Machine Learning Applications for Optimizing Real-Time Drilling and Hydraulic Fracturing”. Prior to Oxy, Dr. Ben developed hydraulic fracturing models for Baker Hughes and Halliburton and was a postdoc at MIT. She has authored more than 30 papers and holds three US patents. She earned a BS in theoretical mechanics at Peking University, and a PhD in chemical engineering from the University of Notre Dame.

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