Understanding trouble stages in real time is critical to improving completions performance and reducing non-productive time. Traditional approaches often fall short by relying on fragmented data and reactive decision-making. This white paper introduces a structured, data-driven model for stage categorization—enabling teams to quickly diagnose deviations like screenouts and rate drops, distinguish between surface and subsurface issues, and apply targeted remediation strategies. It also explores how this capability sets the stage for predictive insights, empowering operators to identify high-risk stages before problems arise.
Download the full white paper to learn how leading teams are transforming operational awareness into a competitive advantage.
Collaboration
Abu Dhabi, United Arab Emirates – 04 December 2024 In a significant advancement for the energy sector, ADNOC, AIQ, Baker Hughes, and Corva have announced a strategic collaboration to implement AI-ROP Optimization technology across ADNOC’s onshore and offshore assets. This collaboration aims to enhance drilling efficiency by leveraging advanced AI expertise to optimize drilling operations, […]
Company
Corva’s vision is to accelerate the future of energy and to deliver on this promise are eight core values that define how we behave, operate, and interact with each other, our customers, and our partners daily. Our value system is defined to provide a compass that guides our team to the shared vision of radically […]
Webinar
Discover insights from Corva's CEO and Founder, Ryan Dawson, on the critical role of high-quality datasets in AI solutions. Tune in to this insightful SPE Energy Stream episode on-demand for expert advice and real-world examples.