Vietnam Section

What Does the Future Look Like for the Oil & Gas and Energy Sector - The Energy Transition

Distinguished Lecture by Ian Phillips, Pale Blue Dot Energy Limited Co-organised by SPE Vietnam Section & SPE Singapore Section Abstract: This presentation considers the future of the Oil & Gas industry from two perspectives. The first half paints a picture of an industry in extremely good health and with an exciting future - global population continues to grow and individuals' standard of living continues to rise - so demand for energy is growing steadily. Despite this growing demand, global proven reserves have risen as technology improves. In addition, we know where there is a lot more oil and gas in deposits that are currently uneconomic, but a small increase in the oil price would make these "proven reserves." In a nutshell, the industry might have a bright future. The second half presents the significant obstacle: summarizing the science of climate change and considering what happened when temperature rose this high in the past. The emerging alternatives, renewables, energy efficiency, and some dramatic scientific advances in "clean hydrocarbons," will also be reviewed. The presentation closes with an assessment of the impact of these competing forces on the oil and gas industry, the developed world, and the human race. Biography: Ian Phillips has over 30 years of experience in the upstream Oil & Gas industry with operators and service companies. In 2007, he became a founding Director of CO2DeepStore Limited - now Pale Blue Dot Energy Limited - the first company specifically seeking to deliver CCS as a service. He holds an M.Sc. in Petroleum Engineering from Heriot Watt University and an MBA. He is also a Fellow of the UK Energy Institute and is a Chartered Petroleum Engineer. He recently completed 4 years as Chairman of the SPE Aberdeen Section and was previously North Sea Regional Director on the SPEI Board. He is an SPE Distinguished Member.

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Oiling the Cogs: How Cognitive Science Can Improve Oil Industry Decisions

Distinguished Lecture by Matthew Welsh, University of Adelaide Abstract: The oil industry regularly makes decisions that are high-stakes, requiring long-term forecasting, accurate estimation of unknown parameters and complex modelling of scenarios. Observational and experimental evidence, however, tells us that people's natural decision-making tendencies are reliant on simple, inaccurate estimation strategies that lead to systematic biases in judgments and decisions. Preventing people from using these processes, however, is very difficult - as evidenced by the fact that the oil industry has been discussing the problem of overconfidence in forecasting for more than 40 years without great improvements. In order to work out how to improve judgments and decisions, we need a better understanding of how people's cognitive tendencies and limitations lead to observed biases and errors. By way of example, this lecture discusses a core aspect of cognition - memory - explaining how this differs both from people's intuitive assumptions about how it works and from how a purely rational process would. Key examples of biases that result from our inherent memory processes are discussed and the implications of this for decision making in oil industry contexts presented, leading into a discussion of how to design elicitation processes informed by cognitive science that can limit the impact of biases. Members should come away from this lecture understanding that we should not expect people to be able to make better decisions if we do not, first, understand how and why people think the way they do. This is true for professionals at all stages of a decision process: from SMEs generating inputs for models; to team leaders selecting which development options to consider; to senior decision makers' assessments of these options relative to other opportunities. Biography: Dr Matthew Welsh has a PhD in Psychology and has worked for 16 years in the Australian School of Petroleum, applying his understanding of human cognitive processes to improving oil industry decisions. In this role, he has supervised 20 student projects, published 50 papers in psychology and industry outlets and written a book on how cognitive processes affect the judgments and decisions that scientists make: "Bias in Science and Communication: a field guide." He has consulted and collaborated with oil companies incuding BG, BHP, ExxonMobil, Santos and Woodside, as well as with researchers in diverse fields including psychology, geophysics, medicine and defence. His key focus is on the problem of elicitation - how we elicit estimates from experts in such a way as to avoid biasing their responses and to ensure that their estimates accurately reflect what they do and do not know.

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Subsurface Analytics: Digital Transformation of Reservoir Management with AI and ML

Distinguished Lecture by Shahab D. Mohaghegh Hosted by SPE Singapore Section, in collaboration with SPE Kuala Lumpur & SPE Vietnam Section Abstract: 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, North Sea, Middle East, and Southeast Asia. It has been successfully applied in several highly complex mature fields where conventional commercial reservoir simulators were unable to simultanuously history match multiple dynamic variables for 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. Biography: 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).

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