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SUMMARY:SPE Denver General Meeting: October 2025
DESCRIPTION:Abstract: \n  \nAs the Oil and Gas industry continues its digital growth and transformation\, the ability to harness data effectively is becoming a defining factor in operational efficiency and strategic decision-making. This presentation explores how cloud-based technologies can modernize data warehousing\, streamline data transformation\, and enhance application integration. Leveraging Databricks for scalable data processing\, Dagster for robust orchestration\, and dbt for reliable data transformations\, we demonstrate an integrated ecosystem that delivers both agility and governance. Attendees will gain insights into best practices for migrating from an on premises to cloud-native data platform\, strategies for overcoming challenges such as siloed systems and legacy workflows\, and practical examples of how these tools can unlock new value from subsurface\, production\, and operational data. By embracing these technologies\, organizations can enable faster analytics and drive better business outcomes across the oil and gas industry. \n  \nMindy Spellman: \n  \nWith over 14 years of experience in the oil and gas industry\, Mindy serves as the Director of Data and Analytics at Permian Resources\, where she leads enterprise-wide data strategy\, advanced analytics\, and digital innovation efforts. Mindy began her career in reservoir engineering at Forest Oil before moving into a corporate analytics role. \nDriven by a passion for data and analytics\, Mindy went on to found Blue River Analytics\, a firm specializing in data visualization and business intelligence solutions for the energy sector. As founder and CTO\, she worked with numerous E&P companies to modernize their data workflows\, transforming how teams interact with and leverage operational and subsurface data. \nFor the past seven years\, Mindy has played a key leadership role at Centennial Resource Development\, now Permian Resources\, where she has helped shape the company’s digital transformation. Her work focuses on unifying data platforms\, enhancing operational visibility\, and deploying advanced analytics to drive efficiency across the company. \n  \nMatthew Mauch: \n  \nMatthew Mauch has seven years of experience in the oil and gas industry\, working across technical\, analytical\, and data-focused roles. He began his career as a Reservoir Engineering Technician\, supporting subsurface teams with data management and analysis. He later transitioned into a Business Analyst role\, where he focused on bridging the gap between technical teams and business stakeholders by delivering data-driven insights. Currently\, Matthew is a Data Engineer\, specializing in cloud-based platforms and modern data technologies to build scalable\, reliable\, and efficient data pipelines at Permian Resources. He has helped lead the development and deployment of data pipelines during their migration from on premises SQL Server to Azure Databricks. \n  \n  \nRegister Here
URL:https://denverspe.org/event/spe-denver-general-meeting-october-2025/
LOCATION:Rock Bottom Restaurant & Brewery\, 1001 16th Street\, Unit Ste A-100\, Denver\, Colorado\, 80265\, United States
CATEGORIES:General,Technical
ATTACH;FMTTYPE=image/png:https://denverspe.org/wp-content/uploads/2025/09/SPE_TechProg_LinkedIn-25.png
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DTSTART;TZID=America/Denver:20251016T160000
DTEND;TZID=America/Denver:20251016T180000
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CREATED:20250911T163428Z
LAST-MODIFIED:20250911T163740Z
UID:5550-1760630400-1760637600@denverspe.org
SUMMARY:SPE Denver Technical Happy Hour - October 2025
DESCRIPTION:Host: Society of Petroleum Engineers\, Denver SectionEvent: October 2025 Technical Happy HourTime: October 16th\, 4-6 (HH begins at 4\, Talk at 430\, Social until 6)Location: Liberty Energy\, 950 17th St\, Suite 2400\, Denver\, CO 80202Sponsor: LibertyStudy Group Category: CompletionsSpeaker: Jessica Iriarte\, General Completions Manager\, Corva \nTitle: Enhancing Operational Awareness in Haynesville Operations with Advanced Stage-Categorization Models \n  \nAbstract: \n  \nThis study presents an AI-driven approach to stage categorization for hydraulic\nfracturing operations\, addressing deviations from pumping designs that result in\nincreased costs\, reduced efficiency\, and operational unpredictability. The dataset\nincludes 1\,595 stages\, of which 970 exhibited at least one issue such as mid-stage\nshutdowns\, screen outs\, and rate reductions. The objective is to improve\noperational decision-making by automating stage categorization\, distinguishing\nbetween surface and subsurface issues\, and lay the foundation for future\npredictive machine learning models to anticipate trouble stages. \n  \n  \nThe stage categorization model evolved through eight iterations to refine detection\nof operational issues and differentiate between surface- and subsurface-driven\nproblems. Version 1 implemented basic pressure slope algorithms\, while Version 2\nimproved accuracy with steady-rate pressure checks. Version 3 expanded\ndiagnostic capabilities by incorporating proppant and chemical concentrations.\nVersion 4 adjusted the search for specific activities like pad for ball-seat stages\,\nflush\, and pressure test. Version 5 introduced dynamic thresholds to account for\nfriction effects from varying casing sizes and stage measured depths. Version 6\noptimized the overall model\, removing factors that reduced Precision\, resulting in\nsignificantly improved performance. Version 7 improved Clean Sweep remediation\ndetection using stage activities\, such as flushes\, to determine the most accurate\nperiod to scan. Screen outs were updated based on a sensitivity analysis around\nthe required pressure per minute reading. Finally\, in Version 8 logic was added to\nconnect subsequent subsurface-related issues to one that occurred earlier in the\nstage’s progression. \n  \n  \nThe finalized stage categorization model analyzes time-series data\, including\npressure\, rate\, proppant and chemical concentration\, and wellbore design\nparameters\, capturing key variables affecting treatment behavior. It achieved 98%\naccuracy and 88% precision in identifying issues like mid-stage shutdowns and \nscreen outs. By distinguishing surface from subsurface problems\, operators can\noptimize resource allocation and treatment designs. The study also revealed\nsignificant correlations between operational challenges and geological variability\,\nhighlighting the importance of integrating rock and operational data. These\ninsights facilitate improved treatment consistency\, reduced costs\, and enhanced\ndecision-making. \n  \nBio: \n  \nJessica Iriarte is the General Manager of Completions at Corva. Jessica is a data\nscience and energy leader\, has held various leadership positions in oil and gas\,\nincluding international experience in data\, research\, and operations. Jessica is an\ninventor\, a distinguished lecturer\, and has 17 publications with SPE\, JPT\, and URTeC.\nJessica holds a Bachelor of Science degree in Petroleum Engineering from\nUniversidad del Zulia and a Master of Science degree in Petroleum Engineering from\nColorado School of Mines. \n  \nRegister Here
URL:https://denverspe.org/event/spe-denver-technical-happy-hour-october-2025/
LOCATION:Liberty Energy\, 950 17th St Suite #24\, Denver\, CO\, 80202\, United States
CATEGORIES:Technical
ATTACH;FMTTYPE=image/png:https://denverspe.org/wp-content/uploads/2025/09/SPE_TechProg_LinkedIn-23.png
ORGANIZER;CN="SPE Denver Section":MAILTO:denversection@spemail.org
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