Drilling can be a challenging and expensive operation and the industry is always looking for ways to innovate and make the operation more efficient and safer.
Two assistant professors in the University of Houston College of Technology, Jiefu Chen, Ph.D., and Xuqing (Jason) Wu, Ph.D., along with Zhu Han, PhD, a professor in the Electrical and Computer Engineering Department at the Cullen College of Engineering, created a software that uses “a Deep-Learning model to automatically analyze the monitoring video stream from the onshore and offshore drilling rigs and classify the volume of drilled cuttings on the shale shakers in real-time.”
“Cutting analysis is an important task for an efficient, low-cost, and risk-free drilling execution,” Chen said.
Houston-based startup company DrillDocs has realized the potential for this technology and has entered into a license agreement with UH.
“Our mission is to provide oil and gas operators with healthier wellbores by preventing stuck pipe and lost-in-hole events while reducing hole cleaning time and hole instability issues,” said Calvin Holt, DrillDocs chief executive officer.
DrillDocs, which is backed by an experienced SCADA, drilling engineering and geomechanics team with decades of wellsite experience and millions of feet drilled, will provide key real-time surface data from existing and/or purpose-built computer vision systems on the rig.
This data will be analyzed by the UH software “to help customers make more informed drilling decisions, reduce safety and environmental risks, and improve drilling performance and production.”
Chen and his colleagues wanted to work with DrillDocs because of the company’s work and DrillDocs recently won the Paragon Innovations Prize at the 2021 Texas A&M New Ventures Competition.
The purpose of this solution, which DrillDocs calls the Pulse™ cuttings classification service, is to make it easier to monitor advanced drilling equipment.
“The functionality provided by this software is critical for an integrated control and information system to boost drilling operations, reduce costs and minimize drilling risks,” Chen said.
Drill techs usually must repeatedly examine and analyze cutting manually, which could hold-up progress and opens the possibility of human errors.
“As a result, this technology will help build an “intelligent digital infrastructure to improve drilling efficiency, reduce costs, and minimize environmental impacts,” Chen said.
“The DrillDocs team is excited to license the Pulse Deep-Learning solution from the University of Houston and look forward to helping reduce costs and risks while creating better more sustainable wellbores. We plan to continue advancing this important work beyond determining the volume in the shale shaker and move into object classification, detection, and recognition,” said Holt.
This technology was featured by the Journal of Petroleum Technology, the Society of Petroleum Engineers’ flagship magazine.