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DTSTART;TZID=America/Denver:20251110T160000
DTEND;TZID=America/Denver:20251110T180000
DTSTAMP:20251027T165107Z
CREATED:20251027T164902Z
LAST-MODIFIED:20251027T165107Z
UID:5592-1762790400-1762797600@denverspe.org
SUMMARY:SPE Denver Technical Happy Hour: Completions
DESCRIPTION:Speaker: Madison Hollaway\, Low Emissions And Sustainability Lead\, Liberty Energy\nTitle: Evaluating the Transition to Natural Gas-Powered Equipment in Hydraulic Fracturing: Challenges\, Benefits\, and Feasibility Analysis. \n  \nAbstract: \n  \nThe transition to natural gas as a primary fuel source in hydraulic fracturing\noperations launched in 2013 with the introduction of dual fuel engines\,\nwhich use a blend of diesel and natural gas. This transition remains\nongoing\, marked by two fundamentally different approaches to displacing\ndiesel with natural gas. Initially\, dual fuel engines emerged as the\npioneering technology for substituting diesel with natural gas on hydraulic\nfracturing jobs. These engines are still being used and improved upon\ntoday. Subsequently\, circa 2018\, companies began developing and\ndeploying 100% natural gas-powered generation systems to drive\nelectrically powered hydraulic fracturing equipment.\nHistorically\, gas powered generation has been found exclusively in\nstationary applications. Adapting this technology to a mobile package for\nhydraulic fracturing operations has been a formidable industry-wide\nchallenge. Unlike stationary setups\, mobile deployment necessitates\nconsiderations of weight\, ambient outdoor conditions\, and the reliability of\nthe natural gas supply. Prior to equipment construction\, weight\noptimization is imperative\, as off-road mobile units are subject to state-\nspecific road weight restrictions. Compliance with axle\, king pin\, and total\nweight limits is mandatory for roadworthiness. Moreover\, the lack of control\nover outdoor ambient conditions further complicates mobile deployment.\nUnlike ambient conditions\, natural gas supply is within control of the\noperator\, though it cannot be preemptively resolved like weight\nconsiderations. Post-deployment\, natural gas quality and supply often\nemerge as predominant challenges\, highlighting their significance in mobile\ngas-powered generation for hydraulic fracturing operations. \n  \n  \nThe balance of transitioning to natural gas fueled engines\, while the natural\ngas infrastructure catches up\, has been both delicate and complex. Diesel\nhas been used as a fuel source for over 70 years in the industry and thus\nhas firmly established a robust supply chain. In contrast\, the Compressed\nNatural Gas (CNG) supply chain is in the early stages of development and\nhas consistently served as a bottleneck for deploying natural gas\ntechnologies. However\, there is a prevailing belief that the inherent benefits\nof natural gas will act as a catalyst for the evolution of the CNG supply\nchain in the years to come.\nConsidering the ongoing challenges\, it is imperative to evaluate the true\nbenefits of this transition. Assessing whether the opportunity cost\noutweighs the hurdles encountered\, both presently and in the foreseeable\nfuture\, is crucial. This paper aims to address this critical question by\nleveraging empirical data collected during the initial stages of our\norganization’s natural gas transition. Through a comprehensive analysis\, we\nexamine the tangible advantages and potential drawbacks of embracing\nnatural gas technology\, providing insights into the feasibility and\nimplications of this transition for the hydraulic fracturing industry. In this\nanalysis we will focus on Tier IV Dual Fuel engines\, Gas Reciprocating\nGenerators which produce electricity to drive electric frac pumps\, and Gas\nReciprocating Engines that mechanically drive the hydraulic pump. The\nbaseline for comparison is established using Tier II Diesel engines. Each\ntechnology will be evaluated based on multiple criteria including emissions\,\ncost savings found in both fuel purchases and operating costs\, and overall\noperational considerations. Through a combination of Original Engine\nManufacturer (OEM) data and empirical data\, this paper provides valuable\ninsights into the efficacy and feasibility of transitioning to natural gas-\nburning equipment in the context of hydraulic fracturing operations. \n  \nBio: \n  \nMadison Hollaway is the Low Emissions And Sustainability Lead at Liberty\nEnergy. Madison has held a variety of positions throughout her career in the\noil and gas industry\, with experience spanning from engineering and project\nmanagement to research and development. Madison holds a Bachelor of\nEngineering in Petroleum Engineering from Texas A&M University. \n  \nRegister Here
URL:https://denverspe.org/event/5592/
LOCATION:Liberty Energy\, 950 17th St Suite #24\, Denver\, CO\, 80202\, United States
CATEGORIES:Study Groups,Technical
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ORGANIZER;CN="SPE Denver Section":MAILTO:denversection@spemail.org
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DTSTART;TZID=America/Denver:20251119T113000
DTEND;TZID=America/Denver:20251119T130000
DTSTAMP:20251104T161355Z
CREATED:20250902T161353Z
LAST-MODIFIED:20251104T161355Z
UID:5536-1763551800-1763557200@denverspe.org
SUMMARY:SPE Denver General Meeting: November 2025
DESCRIPTION:General Meeting Category: Reservoir \nSpeaker: Reidar Bratvold\, Professor Emeritus\, University of Stavanger \nTitle: Persistent Bias in Probabilistic Production Forecasting and Simple Methods to Overcome It \n  \nAbstract: \n  \n  \nThe one key idea I would like the members to take away: Unbiased probabilistic production forecasts are a crucial component in making good investment decisions — but typical ways of producing them result in persistent\, value-destroying biases. There are quick and easy ways to make accurate\, unbiased forecasts. \nIncreased awareness of uncertainty\, combined with increasingly sophisticated tools and models for quantifying it\, is causing a shift from deterministic to probabilistic production forecasting. For these forecasts to lead to better investment decisions (e.g. assessing how much it’s worth paying to reduce uncertainty\, or incorporating flexibility to manage it)\, they need to be an accurate (unbiased) representation of the uncertainty. However\, our industry has a general history of overconfidence (ranges too narrow) and\, more damaging for value-creation\, optimism (consistent over-estimation). \nA large dataset of historical probabilistic production forecasts was investigated for potential bias by comparing them with actual outcomes. They were found to be both optimistic and overconfident\, bringing into question their usefulness for decision-making and the value of the sophisticated uncertainty modeling techniques that were used to generate them. \nFortunately\, there are quick and easy methods for creating accurate probabilistic forecasts. We describe these and show that they would have produced accurate forecasts for our case study fields (they do not make use of knowing the actual outcomes!). \nFinally\, preliminary results from a similar analysis of renewables projects indicate that the same problems exist. This could significantly impact investment decisions and policy development in renewable projects by governments and companies. \n  \n  \nBio: \n  \n  \nReidar B. Bratvold is Professor (emeritus) of Decision & Data Analytics at the University of Stavanger\, where he teaches and supervises graduate students in decision and data analytics\, including artificial intelligence and machine learning\, project valuation\, portfolio analysis\, real-option valuation\, and the behavioral challenges of decision-making. Before joining academia\, Reidar spent 15 years in industry in various technical and leadership roles\, including Vice President at Landmark Graphics Corporation (a Halliburton company) in Houston; Managing Director of Smedvig Technology Software Solutions (now Roxar); Senior Scientist with IBM; and Reservoir Engineer with Statoil. He began his career offshore as a roughneck and roustabout in the North Sea. \nReidar is a frequent speaker at industry conferences and delivers in-house short courses on decision-making and economic evaluation. He is one of only four individuals to have served as a Society of Petroleum Engineers (SPE) Distinguished Lecturer four times: \n• Uncertainty Assessment and Risk Management in Reservoir Optimization (1998–1999) \n• Would You Know a Good Decision if You Saw One? (2003–2004) \n• Creating Value from Uncertainty and Flexibility (2016–2017) \n• Persistent Bias in Probabilistic Production Forecasting and Simple Methods to Overcome It \nHe has published extensively on topics such as decision-making\, the value of information\, the application of machine learning and AI to support decisions\, Bayesian evidence learning\, stochastic reservoir modeling\, fuzzy logic\, data assimilation\, and reservoir management. He is co-author of the SPE book Making Good Decisions\, commissioned by the SPE. \nReidar was the 2017 recipient of the SPE Management & Information Award\, has served as Executive Editor for SPE Economics & Management\, and in 2024 was selected as Energy Professional of the Year by the SPE Stavanger Chapter. He is a Fellow of both the Society of Decision Professionals and the Norwegian Academy of Technological Sciences\, and a Founding Member of the Friends of SDP initiative. \nHe holds a PhD in engineering and an MSc in mathematics from Stanford University and has completed executive education in business and management science at INSEAD\, MIT\, and Stanford \n  \nRegister Here
URL:https://denverspe.org/event/spe-denver-general-meeting-november-2025/
LOCATION:Rock Bottom Restaurant & Brewery\, 1001 16th Street\, Unit Ste A-100\, Denver\, Colorado\, 80265\, United States
CATEGORIES:General,Technical
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