Here is a list of recent publications for RAPID researchers.

Focus Areas

RAPID emphasizes research in four major, highly coupled categories: (i) automated drilling, completion and stimulation control; (ii) modeling, simulation, and empirical validation of downhole environments; (iii) monitoring, data analytics, and “Big Data;” and (iv) intelligent mechanization and automation.

  • An automated drilling control system must equal the performance of operators on modern systems and ideally achieves the best possible drilling performance in the presence of constraints and uncertainties. This motivates our decision to include research into real-time optimization-based drilling control, which incorporates constraints and models of uncertainties into its decision making directly. A university-centric consortium allows us to develop an open architecture and solution framework that produces verifiably correct control methods and algorithms that can be implemented and executed on real-time computers without requiring an expert in the loop. This dramatically increases the potential for overall rig automation and/or remote operation.
  • Modeling, simulation, and empirical validation in downhole environments is the unifying and foundational effort for other three major efforts. Any efforts in this area will be driven by the needs to improve control, data analytics, or mechanization efforts. While efforts in this category are essential, efforts will only be undertaken to support the other research threads, thus ensuring the consortium research efforts are enabling useful technologies and not merely better models.  If model development is motivated by technologies, then there must be an inherent discipline to ensure the models are experimentally verified – which is unfortunately not a common practice in university research.
  • Monitoring, data analytics, and “Big Data” are necessary areas of research to (i) leverage the extensive and already available plethora of data collected on rigs today that is woefully underutilized; (ii) develop the algorithms necessary to leverage this data or determine what data is actually needed, and (iii) ensure that the developed algorithms are scalable, transferable and get the right information to the right location on the planet in the required timeframe for it to be useful. There are established monitoring techniques for select phenomena of interest (vibration, tribology, thermography, etc.) that provide some warning of an impending failure. However there is still a need to recognize slow degradations, recognize sensor failures, and use the collected data to better understand the overall drilling conditions. Once developed, there will only be an increase in the already huge streams of data collected. There is already a limited capacity to use this data in real-time or apply value adding analytics to the streaming data in order to extract key information or assert reliable action recommendations Long-term research is needed to not only develop such algorithms, but to aggregate data so they can be broadly applied.
  • Intelligent mechanization and automation. As confidence in the drilling control and data analytics increase, the motivation to move operators out of remote and hazardous environments to safer operational command centers will certainly increase. Thus a key element to the meet consortium’s goals will be the mechanization, intelligent automation and re-design of rigs to drill with fewer or no workers on site. Many of capabilities necessary to ensure multiple automated devices can work in a shared workspace exist in other industry, but there are significant research and engineering challenges to port these capabilities to the oil and gas industry given the high level of task uncertainty and unpredictable environments. Challenges faced by the oil industry in automation have been addressed in other domains (space, nuclear, etc.) that have similar challenges. These include collision checking, sharing workspaces (with operators or other mechanized systems, operator interfaces, task scheduling, 3D mapping, trajectory planning, tele-operation, and system integration. Researchers at UT Austin will look to adopt existing technologies whenever possible to minimize risk and shorten the path to deployment.

UT Austin’s hardware-in-the-loop (HIL) Simulator which includes a screen that wraps around a driller’s and assistant driller’s chair. Each component in the simulation is managed by its own PLC which provides an accurate depiction rig operation. 

Areas of Interest and Activity

Areas of interest and activity include – but are not limited to – the following topics. The input and advice of consortium members will be the primary influence on research topics, scope, and objectives.

  • Automated drilling control for (remote) directional drilling and geo-steering
  • Automated drilling control to address adverse drilling conditions (i.e. stick-slip, whirl, bit-bounce, etc.)
  • Automated drilling control for wellbore stability and lost circulation prevention
  • Automated managed pressure drilling and dual gradient drilling solutions
  • Automated completion, stimulation / hydraulic fracturing, and well intervention tasks
  • Mechanization for drilling automation (pipe handling, BHA assembly, drilling while circulating, etc.)
  • Mechanization for rig move / transport, mob/demob, and skidding / rig walking
  • Automation and optimal control for tripping
  • Rig design for automation
  • Novel sensor design and development for automation
  • High-frequency downhole and surface sensor analysis
  • Data quality, data analytics and sensor standards
  • Scalable “Big Data” information models and communication protocols
  • Decision support software for data monitoring, event recognition, and process control
  • Application of Hardware-In-the-Loop (HIL) simulators for automation prototyping and testing.
  • Case-based teaching and training using HIL Simulator and UT Austin’s RTOC.
  • Improved surface and downhole models (hydraulics, rock mechanics, hole-cleaning, drillstring dynamics, etc.) for use in controls, design, and training.
  • Control-oriented model development and data analysis (BHA dynamics, bit-rock interaction, etc.).

RAPID’s Real-Time Operations Center (RTOC) for remote operations monitoring and data pattern analysis. Four work stations, a suite of well planning, monitoring, and anomaly detection software with an independent, real-time data server.

For more information, download our Guidance Document or contact Mitch Pryor or Eric van Oort.