02/19

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

written by: Mojtaba Shahri, Timothy Wilson, Taylor Thetford, Brian Nelson, Michael Behounek, Adrian Ambrus, John D’Angelo, and Pradeepkumar Ashok

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…

10/18

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…

09/18

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…

05/18

American Oil and Gas Reporter: Decision Support Solution Improves Drilling Performance, Reduces Non-Productive Time (April 2018)

written by: By P. Ashok, A. Ambrus, T. Thetford, B. Nelson, M. Behounek

Today, the effectiveness of real-time adjustments to drilling parameters may be hindered by uncertain surface and downhole measurements and the inability of humans to aggregate multiple streams of data in real-time. A recently introduced drilling optimization system addresses these issues by incorporating a Bayesian network into operators’ drilling rig data aggregation systems. This Bayesian network-enhanced…

09/17

Journal of Petroleum Technology: Data Aggregation and Distribution with Human Factors Incorporated (September 2017).

written by: M. Behounek & P. Ashok

To optimize the value of surface and downhole drilling data, an operator, a software company, and a service company have combined forces to develop a system to seamlessly integrate all forms of data, file types, and communication protocols. To ensure that personnel on the rig and in the office are able and willing to act…

08/17

World Oil: Drilling Advances (July 2017)

written by: J. Redden

The human component figures prominently in Apache Corporation’s well-documented drive toward developing an automatic rig-based drilling advisory system. The foundation has been laid with the installation of data aggregation boxes on 21 Apache rigs, which integrate seamlessly with the rig’s electronic drilling recorder to run physics-based models built around a basic Bayesian probabilistic network. The…

08/17

World Oil: Plug the value leak: Fix your drilling data (October 2015)

written by: P. Ashok, A. Ambrus, E. van Oort, N. Zenero & M. Behounek

Today, drilling data are marred by a lack of quality assurance, which can result in costly consequences from poor analytics and real-time decisions made using faulty data. A new system boosts drilling efficiency through the validation of real-time or historical data, which enables operators to significantly reduce both non-productive time and invisible lost time.