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Advancements in Drilling and Formation Evaluation using Machine Learning and Data Science

Wednesday, 25 September
228 - 230
Technical Session
With the advancement of machine learning (ML) and data science (DS), Oil and Gas industry is embracing the new technologies to enhance the decision making in drilling operations and formation evaluation. Interactions of ML/DS with the drilling operations and formation data analysis is the key focus of this session. It aims to shed light on innovative approaches to improve formation data interpretability and predictability, reduce modeling uncertainty, and drive high drilling operation efficiency.
Session Chairpersons
Catalin Teodoriu - University of Oklahoma
Wei Chen - SLB
  • 1400-1425 220708
    Improving Reliability Of Seismic Stratigraphy Prediction By Integrating Uncertainty Quantification Into Attention Mechanism Neural Network
    C. Ang, PETRONAS Research Sdn Bhd; A. Elsheikh, Heriot-Watt University
  • 1425-1450 221058
    Chasing Remaining Potential In Dana Gas East Nile Delta Development Blocks Using Machine Learning
    M.M. Abdelrahman, I. Yahia, S. Mohammed, Dana Gas; M. Abd El-Gwad, Wasco
  • 1450-1515 220703
    A Better Mousetrap? The Applicability Of Explainable Boosting Machines (EBM) As A “Glassbox” Modeling Paradigm For Improved Model Interpretability During Supervised Learning
    S. Das, S. Mishra, A. Datta-gupta, Texas A&M University
  • 1515-1540 220980
    Predicting Permeability In Real-time From LWD Resistivity And Gamma Ray Logs
    J.H. Norbisrath, Equinor US; V. Sangolt, A.K. Russell, Equinor ASA
  • 1540-1605 221000
    Refined Diffusion Posterior Sampling For Seismic Denoising And Interpolation
    T. Nguyen, H. Pham, M. Nguyen, T. Nguyen, Viettel Solutions
  • 1605-1630 220946
    Use Of Machine Learning In Microseismic Monitoring For Thermal Operations In Cold Lake, Ab, Canada
    S. Costin, Imperial Oil Resources Ltd.; S. Scaini, Imperial Oil & Gas; H. Zhao, Imperial Oil Ltd.; C. Brisco, ExxonMobil Canada; T. Fink, Imperial Oil Co.; J. Feng, Imperial Oil Resources Ltd.; D. Yadav, Exxon Mobil Corporation
  • Alternate 220950
    Reference Synthetic Dataset For Drilling Inventory Optimization
    O.E. Abdelaziem, AMAL Petroleum Company (AMAPETCO); R.M. Khafagy, BP Egypt
  • Alternate 220691
    Novel Method For Automated Advanced Mud Gas Extraction Efficiency Correction (eec) Characterization In Near Real Time
    N. Ritzmann, B. Baecker, Baker Hughes
  • Alternate 220863
    Deploy Distributed Real-time Data Pipelines Across Cloud And Edge To Process Rig Data For Easy Data Fusion And Processing
    A. Wang, Prescient Devices, Inc.; R. Whitney, Precision Drilling

Prepare for an Unforgettable Opening Session!

Through an insightful discussion, we aim to provide a comprehensive understanding of the past, present, and future of innovation within the Oil & Gas industry, inspiring a new era of energy professionals committed to shaping a resilient and sustainable energy landscape.

Moderator: Elena Melchert | Podcast Host and Consultant | Oil and Gas Global Network