Notice-and-Access Notification

New patent issued to Pason for detecting influx and loss events (USA)

PATENT: Published on United States Patent, June 16, 2020

Method and System for Detecting at Least One of an Influx Event and a Loss Event During Well Drilling

Methods, systems, and techniques for detecting at least one of an influx event and a loss event during well drilling involve using one or both of errors between 1) estimated and measured pit volume, and 2) estimated and measured flow out, to identify or determine whether the influx or loss event is occurring, or to sound some other type of related alert. These determinations may be performed in a computationally efficient manner, such as by using one or both of a time and depth sensitive regression.


Torrione; Peter (Calgary, CA)
Morton; Kenneth (Calgary, CA)
Unrau; Sean (Calgary, CA)

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Machine Learning Can Reduce False Alarms on Rigs, Well Control Incidents

INTERVIEW: Published on Drilling Contractor, February 14, 2018

Modern drilling rigs use electronic systems to alert crews to potentially hazardous or costly events such as kicks, losses or spills, but wide fluctuations in measured values generate many false alarms. This can lead to alarm fatigue in crews and, consequently, well control incidents, Sean Unrau, Product Line Manager at Pason Systems, said at the IADC HSE&T Conference in Houston. In this video from the event on 6 February, Mr Unrau discusses how machine learning can be used to significantly reduce false alarms and improve rig safety through early detection. He also highlights results from an analysis of 80,000 hours of data from rigs equipped with machine learning technology, in which all 33 kicks that occurred were detected before the gains outgrew 2.5 bbl and all 20 losses that occurred were detected before the loss outgrew 6.3 bbl. Further, the false alarm rates for kicks and losses were less than one every 5 hours and one every 10 hours, respectively.


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Adaptive Real-Time Machine Learning-Based Alarm System for Influx and Loss Detection

SPE PAPER: Published on One Petro, October 1, 2017

Unexpected influxes and losses pose a significant risk to rig personnel, the environment, and drilling efficiency. Influxes and losses typically manifest in the circulation system (e.g., as increases or decreases in flow and mud-volume) and other rig surface measurements. Automated, real-time interpretation of these drilling parameter traces is complicated by the highly variable and transient nature of the circulation system during normal drilling operations. As a result, the most commonly deployed automated alarm systems (e.g., fixed +/- bounds) have high false alarm rates, and are sometimes treated as unreliable by rig personnel. Recent advances in machine learning enable data-driven algorithms to identify anomalous behavior in real-time data traces, but until recently, the uptake of these algorithms has been hindered by driller's lack of trust in these automated systems, and the complexity of explaining why these so-called "intelligent" algorithms do (or don't) generate alarms in any given scenario. This paper documents a novel machine-learning algorithm framework for circulation system monitoring that was designed to maintain a very low false-alarm rate and earn the driller's trust by explicitly providing expected safe operating bounds on flow-out and pit-volume, so that even during long (e.g., 24-hour) periods without alarms, the driller knows that the system is operational and trustworthy. We present performance results generated across a massive body of drilling data that illustrate the trade-offs between detection rate and false alarm rate that are inherent to any machine learning (indeed, any algorithm) approach to event detection, and show how explicit bound generation can be used to improve driller trust and acceptance.


S. Unrau (Pason Systems Corporation)
P. Torrione (CoVar Applied Technologies)

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Machine Learning Algorithms Applied to Detection of Well Control Events

SPE PAPER: Published on One Petro, April 2, 2017

When a drilling rig takes a kick or experiences lost circulation it is both dangerous and expensive. The earlier these events are detected the sooner the crew can take critical corrective action, minimizing both the danger and cost associated with the event. Detecting these events early allows the crew to take corrective action early thus minimizing both the danger and the cost associated with the event. Early detection of these events requires the crew to notice subtle changes in mud volumes and flow rates on the surface. As a kick enters the well bore and begins making its way to the surface it shows up as a gain in the volume of mud at surface and also an increase in mud flow rate out of the well. Conversely lost circulation occurs when some of the drilling mud is lost down hole. This shows up as a decrease in surface mud volume and a decrease in flow rate out of the well. These increases and decreases can be subtle when compared to the normal fluctuations in the mud system during drilling operations. The mud system undergoes significant changes in volume and flow rate as connections are made, as pipe is moved in and out of the hole, as pump rates change, and even as more depth is drilled. Traditional alarm systems that trigger on simple changes in mud volume and flow rate generate a large number of false alarms. Standard mud system alarms are not effective at detecting these dangerous events. The signature of the event can be lost in the normal variance of the data. Even if a traditional alarm sounds the crew is unlikely to take it seriously due to the large number of false alarms they have encountered leading up to the event.

This paper describes a system that utilizes machine learning algorithms to maintain an accurate estimate of what mud volumes and flow rates should be during all phases of the drilling process. Alarm thresholds are calculated and adapt in real time to the current rig activity. False alarms are dramatically reduced even while enforcing tight alarm bounds that enable early detection. Since the crew is left only with meaningful alarms, they are more likely to take corrective action in a timely manner.


Sean Unrau (Pason Systems Corp.)
Pete Torrione (CoVar Applied Technologies Inc)
Mark Hibbard (CoVar Applied Technologies Inc)
Russell Smith (Pason System Corp.)
Lars Olesen (Pason System Corp.)
Joe Watson (Rawabi-Pason LLC.)

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Stick/Slip Detection and Friction-Factor Testing With Surface-Based Torque and Tension Measurements

SPE PAPER: Published on One Petro, May 1, 2016

Recently, there has been a strong push toward automation and the use of real-time models in the drilling industry. However, it has been recognized that these new methods require a dramatic improvement in the quality of sensor data gathered at the rig. In this paper, we investigate how accurate measurement of drillpipe torque and tension at the surface can be used to diagnose downhole conditions. A surface-based torque-and-tension sub (TTS) was used to perform measurements while drilling several horizontal wells in the Dilly Creek area of the Horn River Basin, which is onshore in Canada. A filtered version of surface torque was used to calculate a stick/slip metric, which was compared to stick/slip measurements acquired with a downhole tool. The results show that there is reasonable correlation between surface and downhole metrics, but the correlation is highly dependent on torque filter start and stop frequencies. A comparison is also performed between the hookload measured with a deadline sensor and the tension measurement from the surface sub. The results show a systematic discrepancy of approximately 5% that is likely caused by sheave friction. A commercial torque-and-drag (T&D) software package is used to show that values for casing friction factor (FF) may be underestimated if sheave friction is present but ignored in the analysis. The results from this study show that an advanced measurement sub placed below the topdrive can provide valuable information regarding drilling performance. Specifically, the torque signal can be used to estimate the level of downhole stick/slip, which alleviates the need for an expensive downhole-dynamics tool. Also, the tension signal can be used to obtain accurate measurements of wellbore FF, which can be compared with theoretical values obtained with T&D analysis. With an appropriate-software implementation, these measurements can be performed in real time, which would enable rig crews to react quickly whenever excessive stick/slip or wellbore drag is encountered during drilling operations.


Stephen William Lai (Pason Systems)
Matthew Richard Bloom (Nexen Energy ULC)
Mitch Jason Wood (Pason Systems)
Aaron John Eddy (Pason Systems)
Trevor Leigh Holt (Pason Systems)

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