How can you detect drilling hazards and prevent non-productive time?

1 Drilling hazards and NPT causes

Drilling hazards and NPT causes can be numerous, such as wellbore instability, lost circulation, stuck pipe, well control issues, and equipment failure. Wellbore instability occurs when the pressure or stress in the formation exceeds the strength of the wellbore wall, resulting in collapse, caving, or fracturing. Lost circulation happens when the drilling fluid flows into the formation instead of returning to the surface. Stuck pipe is when the drill string cannot be moved up or down due to mechanical or differential forces. Well control is when the formation pressure exceeds the hydrostatic pressure of the drilling fluid. Equipment failure is when any of the drilling equipment malfunctions or breaks down. All of these issues can lead to blowouts, fires, explosions, environmental hazards, delays, repairs or replacements.

2 Drilling data analysis

Drilling data analysis is the process of collecting, processing, and interpreting various types of data generated during a drilling operation. This includes measurements of the drilling fluid properties, such as density and viscosity; and logging while drilling (LWD), which includes the physical and chemical properties of the formation. Mud logging involves measurements of the gas content and composition of the drilling fluid, and real-time monitoring transmits and visualizes data to a remote location. Drilling data analysis can help detect and prevent drilling hazards and NPT by providing early warning signs of potential problems, enabling proactive decision making and optimization, as well as enhancing understanding and characterization of the formation and the wellbore.

3 Drilling data analysis tools and techniques

Drilling engineers need to use various tools and techniques to perform drilling data analysis, such as drilling software applications that can process and display the data in various formats and perform calculations, simulations, or modeling of the drilling operation. Sensors that measure and transmit the data from the surface or downhole are also necessary, as well as a drilling data management system to store, organize, and access the data from different sources. Additionally, drilling data analytics is used to analyze and interpret the data using statistical, mathematical, or machine learning techniques.

4 Drilling data analysis best practices

For effective and efficient drilling data analysis, drilling engineers should define the objectives and scope of the analysis, collaborate and communicate with the drilling team and other stakeholders, validate and verify the drilling data and analysis results, and review and update the drilling data analysis. This includes specifying the type of data, the frequency of data collection and transmission, the level of data quality and accuracy, or the expected outcomes and benefits of the analysis. Additionally, they should share the drilling data, insights, and recommendations with others to align expectations and goals of the drilling operation. Furthermore, engineers should perform quality control checks to ensure reliability and validity of data and analysis results, as well as incorporate new data, adjust analysis parameters, or evaluate performance and impact of the analysis to ensure relevance and usefulness of data and analysis.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?