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SPE Orange and Blue Logo ATCE
21–23 October 2026 | George R. Brown Convention Center | Houston, TX, USA

21–23 October 2026
George R. Brown Convention Center
Houston, TX, USA

SPE white Logo ATCE

21–23 October 2026
George R. Brown Convention Center
Houston, TX, USA

2026 Technical Program

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SPE ATCE

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Production Optimization

Thursday, 22 October
Room 372 AD
Technical Session
This session focuses on production optimization through the application of advanced automation, machine learning, and digital twin technologies. Presentations highlight field proven, AI driven workflows for gas lift, plunger lift, ESPs, and produced water management, spanning applications from operational decision support to closed loop and fully autonomous control. Case studies demonstrate how physics informed models, real time diagnostics, and self-calibrating workflows improve day to day operating performance, reliability, and scalable optimization across a range of artificial lift and production systems.
Session Chairpersons
Geir Elseth - EQUINOR ASA
Rajan Chokshi - Accutant Solutions
  • 0830-0855 233953
    From Manual To Autonomous: Field Experience And Lesson Learned From Implementing The First AI And APC-based Autonomous Gas Lift Optimization System
    E. Rubio, ADNOC Onshore
  • 0855-0920 234201
    Restoring Gas Lift Injectivity Using A Retrofit Straddle Solution: A Multi‑Year North Sea Field Case History
    A. Bouhouche, E. Wiktorski, P. Mortensen, K. Stenvold, Aker BP ASA
  • 0920-0945 233800
    Gas Lift Evaluation And Production Forecasting Through A Transfer Learning-inspired Dual-track Machine Learning Framework
    A.F. Al Raisi, H. Karami, D. Devegowda, Mewbourne School of Petroleum and Geological Engineering, University of Oklahoma, Norman
  • 1015-1040 233827
    A Physics-informed Machine Learning Framework For Real-time Predictive Diagnostics And Conformance Control In Produced Water Management
    M. Omer, S. Sheikh, M. Duarte Ballesteros, Weatherford International
  • 1040-1105 233942
    Agile Ai Agentic Neural-based Framework For Complex Workflow Forming, With Multiple Implementations Across Upstream And Downstream Scenarios
    H. Chen, China Natl. Petroleum Corp.
  • 1105-1130 234113
    Ai Enabled Digital Twins Enabling Integrated Production Optimization And Autonomous Operations
    R. Mohan, ADNOC Upstream; A. Alhebsi, A. Alsereidi, ADNOC Onshore; M. Almenhali, ADNOC Offshore; M. Alhammadi, ADNOC Onshore; S. Chennallour, ADNOC Offshore; D. Tambunan, ADNOC Onshore; M. Al Hashemi, ADNOC Upstream; S. Alshehhi, D. Kherroubi, Z. Cristea, ADNOC Onshore; A. ALZAABI, ADNOC OFFSHORE; S. ALHASHMI, ADNOC Onshore; R. Alhammadi, ADCO Producing Co. Inc.
  • Alternate 233878
    AI-driven Plunger Lift Optimization: An Eagle Ford Case Study
    E. Onegova, J. Harmouche, A. Segura-Tinoco, M. Khan, Baker Hughes; R. Demian, M. Ramos, G. Dhillon, Repsol; F. Ruiz Garcia, Repsol Exploracion S.A.; V. De Dios, Repsol; O. Iskandar, S. Barbarino, Baker Hughes
  • Alternate 233842
    Closed-loop Automated Gas Lift Injection Optimization Through Daily Self-calibrating Hydraulic Modeling And Field Validation In HPSPGL Wells
    D. Ruiz, Enerview; D. Larson, J. Joiner, CPX Energy; B. Pronk, Dwiggins Consulting LLC; E. Littuma, S. Suheil, Enerview
  • Alternate 234162
    A Hybrid Machine Learning Framework For Virtual Flow Metering Of Esp Wells
    K. Raslan, H. Elnaggar, Badr Petroleum Company; I. Owusu, Ghana National Gas Company
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