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 detection—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.
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 […]
Company
Catch up on the latests AI-driven advancements in energy at the fifth annual Corvacon 2024.
Collaboration
Discover how Corva's Predictive Drilling app revolutionized the industry, winning the 2024 Gulf Energy Excellence Award.