10/24/25

Fresh Tech Is Shaping the Wells of Tomorrow

written by: Blake Wright

If you want to catch a wily mouse, you need to build a better mouse trap. If you want to tap into increasingly more difficult reservoirs in the search for oil and gas, you need to drill a better well. When it comes to global hydrocarbon reserves, the low-hanging fruit was picked over years ago….

12/12/23

Journal of Petroleum Technology: AI, Influencers, and Grit: How Apache’s 8-Year Quest To Build Its Own Drilling Adviser Achieved Full Adoption

written by: Trent Jacobs

Few oil and gas companies give data science projects the better part of a decade to prove out, but that’s just what this one did. When oil prices tumble, upstream research and development projects are among the first casualties. Many are put on the shelf. Few are ever taken off. Then there’s what happened at…

07/01/23

Journal of Petroleum Technology: Natural Language Processing Increases Accuracy of Kick, Lost-Circulation Detection

This paper introduces a method using a Bayesian network to aggregate trends detected in time-series data with events identified by natural language processing to improve the accuracy and robustness of kick and lost-circulation detection. Kick and lost-circulation events are major contributors to nonproductive time. In the absence of good flow-in and flow-out sensors, pit-volume trends…

01/24/22

Sentinel RT™ Lite Now Offered Free of Charge

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…

02/19/20

Journal of Petroleum Technology: Addressing Challenges in Rig-Based Drilling Advisory System Deployment (February 2020)

written by: Judy Feder

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…

02/06/19

Journal of Petroleum Technology: Automated Real-Time Torque-and-Drag Analysis Improves Drilling Performance (February 2019)

written by: Chris Carpenter

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…

10/22/18

Journal of Petroleum Technology: An Artificial Intelligence Belief System Reduces Nonproductive Time (October 2018)

written by: Pradeep Ashok, Michael Behounek

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…

09/21/18

Intellicess Sentinel RT solution helps operators make more accurate decisions in real time (September 2018)

Armed with an abundance of surface and downhole sensors, today’s operators have access to unprecedented volumes of real-time drilling data. However, because they haven’t had the tools to quickly and accurately interpret it, the industry has yet to fully capitalize on this flood of information. The Intellicess Sentinel RT solution answers this challenge by cleansing…

09/12/18

Journal of Petroleum Technology: Probabilistic Drilling-Optimization Index Guides Drillers To Improve Performance (September 2018)

written by: Adam Wilson

This paper proposes a metric for quantifying drilling efficiency and drilling optimization that is computed by use of a Bayesian network. The network combines the identification of drilling dysfunctions (i.e., vibrational modes), autodriller dysfunctions, and mechanical-specific-energy (MSE) tracking into a single, normalized quantity that the driller can use to help decide which control parameters to…