Intellicess is happy to announce the release of Sentinel RT Lite, a free version of our plug and play A.I. drilling advisory software. Sentinel RT Lite includes a state of the art real-time rig state back-end engine, that you can integrate into your system and workflows, free of charge. Get in touch with us if…

Intellicess recently hosted a booth at the 2020 SPE/IADC Drilling Conference in Galveston.  Thanks to everyone who was interested in the latest with Intellicess and our drilling advisory software products and services. If you would like to learn more, please reach out to us via the “contact” link on this website.

Sophisticated drilling-analysis software can help drillers set and modify weight on bit (WOB), rev/min, and other drilling parameters, but achieving acceptance of these software-based recommendations by a driller is complicated. Additionally, acceptance of changes to drilling techniques and modified work flows by a driller on one test rig is insufficient. The challenge is to scale…

Intellicess will be hosting a booth at this year’s SPE/IADC conference in Galveston, TX, on March 3-5, so please come by and meet our team, learn about what’s new and how we might help improve your drilling analytics and advisory efforts. We hope to see you at booth #208!

Intellicess, Inc., presented “An Open Source Data Aggregation Platform on a Rig,” at the IADC DEC Q3 Technology Forum In Houston, TX on September 25. Dr Pradeep Ashok, Intellicess President and CTO, presented on behalf of the company.

M. Behounek, B. Millican, B. Nelson, M. Wicks, E. Rintala, M. White, T. Thetford, P. Ashok, D. Ramos: “Change Management Challenges Deploying a Rig-Based Drilling Advisory System,” paper SPE-194184-MS, presented at SPE/IADC International Drilling Conference and Exhibition, 5-7 March, The Hague, Netherlands, 2019.

The system used for this paper consists of a Rig-based Drilling Advisory System (RDAS) where new advisory information is displayed in the driller’s cabin running real-time pattern recognition algorithms to detect drilling dysfunctions. When a drilling dysfunction is encountered, a change in drilling parameters is suggested. Additionally, drilling parameters from offset wells are made automatically available for the driller’s use on the drilling screen. Through this process, we are entrusting the field personnel with a slightly higher level of technical responsibility. The team iteratively improved the system using feedback from drillers who used the RDAS.

Significant progress has been made on physics-based torque-and-drag (T&D) models that can run either offline or in real time. Despite its numerous benefits, real-time T&D analysis is not prevalent because it requires merging real-time and contextual data of dissimilar frequency and quality, along with repeated calibration, the results of which are not easily accessible to…

Apache Corporation will be presenting a paper jointly written with Intellicess, Inc., entitled: “Change Management Challenges Deploying a Rig Based Drilling Advisory System” at the SPE/IADC International Drilling Conference & Exhibition in the Hague, The Netherlands, on March 7th at 1430.  Intellicess representatives will be on site following the presentation to discuss the paper and answer questions.

In recent years, detection and alerting systems have been applied to numerous drilling failures, including stuck pipe, fluid influx/loss, and drilling dysfunctions. But the detection of drillstring washout and mud pump failure has been left primarily to traditional methods that rely solely on standpipe pressure and pump rates or on ­measurement-while-drilling (MWD) sensor data. Drillers…

M. Shahri, T. Wilson, Taylor Thetford, B. Nelson, M. Behounek, A. Ambrus, J. D’Angelo, P Ashok: “Implementation of a Fully Automated Real-Time Torque and Drag Model for Improving Drilling Performance: Case Study,” paper SPE-191426-MS, presented at the SPE Annual Technical Conference and Exhibition, 24-26 September, Dallas, Texas, 2018.

The drilling industry has made significant progress on physics-based torque and drag (T&D) models that can run either offline (pre-job or post-job) or in real-time. Despite its numerous benefits, real-time T&D analysis is not prevalent since it requires merging real-time and contextual data of dissimilar frequency and quality, along with repeated calibration whose results are not easily accessible to the user. Our goal is to implement a rig-based T&D advisory system which overcomes these obstacles.