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SUMMARY:SPE Technical Happy Hour: April 2026
DESCRIPTION:Description\n\nHost: Society of Petroleum Engineers\, Denver Section \nEvent: April 2026 Technical Happy Hour \nTime: April 23\, 4-6pm (HH begins at 4\, Talk at 430\, more Social until 6) \nLocation: Liberty Energy\, 950 17th St\, Suite 2400\, Denver\, CO 80202 \nSponsor: Novi Labs \nStudy Group Category: Data Analytics \nSpeaker: Brett Sinclair\, Research Director\, Novi Labs \nTitle: Meeting the Moment: Using Machine Learning to Assess How the Marcellus and Haynesville will\nSupply Rising Gas Demand \n  \nAbstract:\nOne of the key challenges we face when trying to analyze scenarios that rely on forecasting the\nproduction from future wells\, is how granular we get in forecasting those wells. Traditionally\,\noperators or analysts looking at very specific asset packages will have highly fine-tuned Type\nCurve Areas to hand-select analogous wells\, creating type curves by bench\, by area\, by\nparent/child designation\, and many more cuts of the data. When doing higher-level analysis –\nhow much will a basin produce over time – analysts might use high-level averages of production\nper well or per rig (or frac crew) across an entire region or basin. \nAt Novi\, we train machine learning models across dozens of features that include subsurface\ngeotechnical attributes\, wellbore and completion design\, well spacing\, and how much prior\ndepletion a future well would experience based on its drainage radius and the amount of\nresource extracted thus far. \nThis provides individual well-level production forecasts custom to each potential location that\ncan be used for extremely focused analysis or high-level macro analysis and everything in\nbetween. It also gets rid of the machine learning “black box” and explains the variation from\naverage in a well’s predicted performance due to different geological\, spacing\, completion\, or\ndepletion features. \nIn this presentation\, we will utilize Novi’s proprietary machine learning-derived inventory\nforecasts to analyze how the Marcellus and the Haynesville are each positioned to supply the\nincreased natural gas demand coming from power generation (largely from data centers) and\nLNG export terminals. How much inventory is left\, and what is the quality of that inventory? \nWhich companies are best positioned\, not only in the near term but for the foreseeable future?\nHow have activity levels responded to structural shifts in demand? \nWe will attempt to show that we don’t have to sacrifice well-level forecast accuracy for scale\,\nand that a machine learning approach also improves individual forecast accuracy while at the\nsame time saving time\, providing the analyst more scope to analyze the forecast’s impact\ninstead of in its derivation. \n  \n  \nBio:\nBrett Sinclair is a Research Director at Novi Labs\, where he focuses on applying the outputs of\nNovi’s data and machine learning analytics platform to US upstream oil and gas research. \nPrior to Novi\, Brett spent 10 years at Kimmeridge Energy in the Investment and Operations\nteams. At Kimmeridge\, Brett and his team conducted analysis and research from technical\,\noperational\, and investment perspectives\, to understand the energy landscape\, to develop and\nexplore investment theses\, to underwrite deals\, and to directly manage portfolio assets. \nPrior to Kimmeridge\, Brett worked as a Directional Driller for Baker Hughes\, drilling horizontals\nin the US Lower 48\, the Alaskan North Slope\, and Saudi Arabia. \nBrett has a bachelor’s degree in mechanical engineering and an MBA\, both from the University\nof Oklahoma. \n  \n  \nSpeaker Photo: \n \n\n  \n  \nRegister Here
URL:https://denverspe.org/event/spe-technical-happy-hour-april-2026/
LOCATION:Liberty Energy\, 950 17th St Suite #24\, Denver\, CO\, 80202\, United States
CATEGORIES:Study Groups,Technical
ATTACH;FMTTYPE=image/png:https://denverspe.org/wp-content/uploads/2026/03/SPE_TechProg_LinkedIn-5.png
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