An Artificial Intelligence Belief System Reduces Nonproductive Time
- Pradeep Ashkok (Intellicess) | Michael Behounek (Apache Corporation)
- Document ID
- Society of Petroleum Engineers
- Journal of Petroleum Technology
- Publication Date
- October 2018
- Document Type
- Journal Paper
- 18 - 20
- 2018. Society of Petroleum Engineers
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- 62 since 2007
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This article contains highlights of paper SPE 189799, “Self-Learning Probabilistic Detection and Alerting of Drillstring Washout and Pump Failure Incidents During Drilling Operations,” by A. Ambrus, formerly with Intellicess; P. Ashok, D. Ramos, A. Chintapalli, Intellicess; A. Susich, T. Thetford, B. Nelson, M. Shahri, J. McNab, and M. Behounek, Apache Corp., presented at the 2018 IADC/SPE Drilling Conference and Exhibition, Fort Worth, Texas, 6–8 March. https:/doi.org/10.2118/189700-MS
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 commonly use a simple hydraulic coefficient relating standpipe pressure to pump rate to detect drillstring washouts. MWD pressure and mud motor data may improve detection but this approach remains problematic owing to poor-quality sensor data and various factors that affect flow and pressure data. As a result, drillers often do not recognize a crack in the drillstring before it grows into a washout that may cost operators hundreds of thousands of dollars in lost time, equipment replacement, and fishing operations.
Drillers also rely on standpipe pressure changes to detect mud pump failures and degradation from damaged pump parts. High-frequency pump pressure data may enhance pump wear detection and accelerometer data may be used to infer valve leaks, but these solutions are impractical in most field applications.
To address these shortcomings, a methodology has been introduced using basic rig sensors and contextual information to detect various drilling failure modes. It has applied that same approach to early wash-out and pump failures detection and alerting.
The detection and alerting system uses a Bayesian network, which aggregates key sensor inputs and contextual data and predictions from hydraulic modeling. Cumulatively they are the inputs to a probabilistic belief system. The probabilistic model outputs belief values of between 0 and 1 that are indicative of specific drilling, equipment, and sensor failures.
Just as the human brain uses experience to generate beliefs upon which to act and react to the world around it intelligently, the probabilistic model uses past and present trends and artificial intelligence (AI) to generate beliefs about drilling dysfunctions and equipment failures. The model also assesses these trends to increase its accuracy through self-learning and self-calibration that enables it to adjust for poor sensor data, drilling conditions, and model uncertainties. As a consequence, when the value of a belief rises to a specified level and the system triggers an alert, the driller can act upon it with increased certainty that it is not a false alarm.
Bayesian networks consist of discrete, or continuous-valued, nodes. Nodes are connected via conditional probability tables (CPTs) and linked by arrows generally representing the direction of causation. Each CPT is assigned a specific weight based on its relative impact on specific outcomes.A Bayesian Network in which the key node is “Unplanned Event” includes several event types and their associated probabilities, or beliefs. This network arrangement can be used to detect a broad variety of incidents related to well control and hydraulics, such as washouts, pump failures, kicks, lost circulation, wellbore breathing, packoff and others to which a probability value is assigned.
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