SPE Live: Distinguished Lecturers Series

Join SPE Live for a special series of Distinguished Lecturers—bringing select presentations from the 2020-21 season to members and nonmembers.

  • Well Placement: Where We’re Headed and Why Non-Drillers Should Care

    Contains 3 Component(s), Includes Credits Includes a Live Web Event on 06/24/2021 at 10:00 AM (EDT)

    This special SPE Live Distinguished Lecturer Series reviews directional drilling practice, how the wellbore quality that results from the directional drilling process impacts the overall value of the well and how tortuosity impacts drilling, completions, production equipment and production rates.

    Join SPE Live for a special series of Distinguished Lecturers—bringing select presentations from the 2020-21 season to members and nonmembers.

    In a hundred years, directional drilling has come a long wayHorizontal drilling is now common and increasingly longer laterals present new challenges for well placement and wellbore quality as expectations of accuracy are increasing and more measurements are available for geosteering. There is a growing qualitative understanding of the impact of well placement and wellbore quality on the overall value of the well and on the economics of completions and production.

    There is an undercurrent of opinion that completions and production are compromised to maximize rate of penetration but with some controversy about the precise value and how easy it is to attribute cause. The presentation will review directional drilling practicehow the wellbore quality that results from the directional drilling process impacts the overall value of the well and how tortuosity impacts drilling, completions, production equipment and production rates. It will predict automation of geometric and geologic aspects of directional drilling along while optimization of drilling and pressure management.

    The key take-away will be the likely technical and value impacts of well placement on completions and production, why the non-drillers in the room need to understand what their drilling colleagues are doing, and how they can go back and influence those colleagues to the benefit of the overall value of the well. 


    This SPE Live is categorized under the Drilling technical discipline

    John (J.M.) Clegg

    Director, HM Clegg Ltd

    SPE Distinguished Lecturer, 2020-21 Lecture Season

    Over 33 years John Clegg has worked in multiple countries in engineering, manufacturing, sales and operations with upstream technologies including drill bits, drilling motors, rotary steerable tools, measurement-while-drilling, logging-while-drilling and managed pressure drilling. 

    He holds a Master’s degree in Engineering Science and a Diploma in Global Business, both from Oxford University, England. He has 14 patents, has authored multiple papers and sits on the Boards of the Drilling Systems Automation and Research and Development Technical Sections of SPE, the Program Committee for ADIPEC and the SPE Drilling Advisory Committee. John is the principal of J M Clegg Ltd, a consultancy specializing both in drilling and in innovation as a strategic discipline. 

    Learn more about the SPE Distinguished Lecturer Program: https://www.spe.org/en/dl/

    Each year, SPE selects a group of professionals, nominated by their peers, to share their knowledge and expertise with SPE members around the globe.


    0.1 CEUs offered

  • Subsurface Analytics: Digital Transformation of Reservoir Management with Artificial Intelligence and Machine Learning

    Contains 3 Component(s), Includes Credits Includes a Live Web Event on 08/03/2021 at 11:00 AM (EDT)

    This special SPE Live Distinguished Lecturer Series discusses how subsurface Analytics is an alternative to traditional reservoir modeling and res. management. Positively influencing subsurface-related decision-making is one of the most important contributions of any new technology.

    Join SPE Live for a special series of Distinguished Lecturers—bringing select presentations from the 2020-21 season to members and nonmembers.

    Subsurface Analytics is an alternative to traditional reservoir modeling and res. management.

    Positively influencing subsurface-related decision-making is the most important contribution of any new technology. Subsurface Analytics is the application of Artificial Intelligence and Machine Learning (AI&ML) in Reservoir Engineering, Characterization, Modeling, and Management. Applicable to both conventional and unconventional plays, Subsurface Analytics goes far beyond the traditional statistical algorithms that use only production data and fail to take into consideration the important field measurements such as well trajectories, well logs, seismic, core data, PVT, well test, completion, and operational constraints. Subsurface Analytics is the manifestation of Digital Transformation in Reservoir Engineering, Modeling, and Management.

    Subsurface Analytics is a new and innovative technology that has been tested and validated in a large number of real-life cases in North and Central America, the North Sea, the Middle East, and Southeast Asia. It has been successfully applied in several highly complex mature fields where conventional commercial reservoir simulators were unable to simultaneously history match multiple dynamic variables for a large number of wells. Results and field validations from multiple case studies are included in the presentation.

    Subsurface Analytics addresses realistic and useful applications of AI&ML in the upstream Exploration and Production Industry. The technology has been validated and confirmed for (a) prediction of well behavior under different operational conditions, (b) modeling and forecasting pressure and saturation distribution throughout the reservoir, (c) infill well location optimization for both producers and injectors, (d) choke optimization for production improvement, and (e) completion optimization for production enhancement.

    This SPE Live is categorized under the Reservoir technical discipline

    Shahab Mohaghegh

    Professor, West Virginia University

    SPE Distinguished Lecturer, 2020-21 Season

    Shahab D. Mohaghegh, a pioneer in the application of Artificial Intelligence and Machine Learning in the Exploration and Production industry, is Professor of Petroleum Engineering at West Virginia University and founder of Intelligent Solutions, Inc. He has authored three books (Shale Analytics – Data Driven Reservoir Modeling – Application of Data-Driven Analytics for the Geological Storage of CO2), more than 200 technical papers and carried out more than 60 projects for independents, NOCs and IOCs.

    He has been featured as the Distinguished Author in SPE’s Journal of Petroleum Technology (JPT 2000 and 2004). He is the founder of SPE’s Petroleum Data-Driven Analytics Technical Section. He has been honored by the U.S. Secretary of Energy for his AI-based technical contribution in the aftermath of the Deepwater Horizon and was a member of U.S. Secretary of Energy’s Technical Advisory Committee (2008-2014). He represented the United States at ISO on Carbon Capture and Storage (2014-2016).

    Learn more about the SPE Distinguished Lecturer Program: https://www.spe.org/en/dl/

    Each year, SPE selects a group of professionals, nominated by their peers, to share their knowledge and expertise with SPE members around the globe.


    0.1 CEUs offered

  • Big Data and Machine Learning in Reservoir Analysis

    Contains 3 Component(s), Includes Credits Includes a Live Web Event on 07/15/2021 at 11:00 AM (EDT)

    This special SPE Live Distinguished Lecturer Series discusses how well monitoring can provide a continuous record of flow rate and pressure, which gives us rich information about the reservoir and makes well data a valuable source for reservoir analysis. Recently, it has been shown that machine learning is a promising tool to interpret well transient data.

    Join SPE Live for a special series of Distinguished Lecturers—bringing select presentations from the 2020-21 season to members and nonmembers.

    Well monitoring can provide a continuous record of flow rate and pressure, which gives us rich information about the reservoir and makes well data a valuable source for reservoir analysis. Recently, it has been shown that machine learning is a promising tool to interpret well transient data. Such methods can be used to denoise and deconvolve the pressure signal efficiently and recover the full reservoir behavior. The machine learning framework has also been extended to multiwell testing and flow rate reconstruction.

    Multiwell data can be formulated into machine learning algorithms using a feature-coefficient-target model. The reservoir model can then be revealed by predicting the pressure corresponding to a simple rate history with the trained model.

    Flow rate reconstruction aims at estimating any missing flow rate history by using available pressure history. This is a very useful capability in practical applications in which individual well rates are not recorded continuously. The success of rate reconstruction modeling also illustrates the adaptability of machine learning to different kinds of reservoir modeling, by adjusting features and targets.

    Machine learning is also a particularly promising technique for analysis of data from permanent downhole gauges (PDG), given that the massive volumes of data are otherwise hard to interpret using conventional interpretation methodologies.

    This SPE Live is categorized under the Reservoir technical discipline

    Roland N. Horne

    Professor, Standford University

    SPE Distinguished Lecturer, 2020-21 Season

    Roland N. Horne is the Thomas Davies Barrow Professor of Earth Sciences at Stanford University, and Professor of Energy Resources Engineering. He was Chairman of the Department of Petroleum Engineering at Stanford University from 1995 to 2006.

    He is an Honorary Member of SPE, and a member of the US National Academy of Engineering.

    Horne has been awarded the SPE Distinguished Achievement Award for Petroleum Engineering Faculty, the Lester C. Uren Award, and the John Franklin Carl Award. He is a Fellow of the School of Engineering, University of Tokyo (2016) and also an Honorary Professor of China University of Petroleum – East China (2016).

    Learn more about the SPE Distinguished Lecturer Program: https://www.spe.org/en/dl/

    Each year, SPE selects a group of professionals, nominated by their peers, to share their knowledge and expertise with SPE members around the globe.


    0.1 CEUs offered