Well On/Off Time Classification Using Recurrent Neural Networks and a Developed Transient Well Simulator

Y. K. AlHammad, H. Hoteit

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Supervised machine learning (ML) projects require data for model training, validation, and testing. However, the confidential nature of field and well production data often hinders the progress of ML projects. To address this issue, we developed a well simulator that generates realistic well production data based on physical, governing differential equations. The simulation models the reservoir, wellbore, flowline, and choke coupled using transient nodal analysis to solve for transient flow rate, pressure, and temperature as a function of variable choke opening over time in addition to a wide range of static parameters for each component. The simulator's output is then perturbed using the gauge transfer function to introduce systemic and random errors, creating a dataset for ML projects without the need for confidential production data. We then generated a simulated dataset to train a recurrent neural network (RNN) on the task of classifying well on/off times. This task typically requires a significant number of manhours to manually filter and verify data for hundreds or thousands of wells. Our RNN model achieves high accuracy in classifying the correct on/off labels, representing a promising step towards a fully-automated rate allocation process. Our simulator for well production data can be used for other ML projects, circumventing the need for confidential data, and enabling the study and development of different ML models to streamline and automate various oil and gas work processes. Overall, the success of our RNN model demonstrates the potential of ML to improve the operational efficiency of various oil and gas work processes.

Original languageEnglish (US)
Title of host publicationSociety of Petroleum Engineers - ADIPEC, ADIP 2023
PublisherSociety of Petroleum Engineers
ISBN (Electronic)9781959025078
DOIs
StatePublished - 2023
Event2023 Abu Dhabi International Petroleum Exhibition and Conference, ADIP 2023 - Abu Dhabi, United Arab Emirates
Duration: Oct 2 2023Oct 5 2023

Publication series

NameSociety of Petroleum Engineers - ADIPEC, ADIP 2023

Conference

Conference2023 Abu Dhabi International Petroleum Exhibition and Conference, ADIP 2023
Country/TerritoryUnited Arab Emirates
CityAbu Dhabi
Period10/2/2310/5/23

Bibliographical note

Publisher Copyright:
© 2023, Society of Petroleum Engineers.

ASJC Scopus subject areas

  • Geochemistry and Petrology
  • Geotechnical Engineering and Engineering Geology
  • Fuel Technology

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