Real-Time Drilling Operations Centers: A History of Functionality and Organizational Purpose—The Second Generation

Booth, J.E.

doi:10.2118/126017-pa

Cited by 19 publications

(4 citation statements)

References 35 publications

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“…Recently, it also turned out that efficient machine learning strategies allow for the increasing of information content via multiple real-time data stream analysis. Thus, the data cannot only be analyzed on a single rig, but also in real-time operating centers (RTOC), which has several advantages, as discussed by Booth in [14].…”

Section: Related Workmentioning

confidence: 99%

Towards Managing Uncertain Geo-Information for Drilling Disasters Using Event Tracking Sensitivity Analysis

Tavakoli

Poslad

Frühwirth³

et al. 2023

Sensors

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In sub-surface drilling rigs, one key critical crisis is unwanted influx into the borehole as a result of increasing the influx rate while drilling deeper into a high-pressure gas formation. Although established risk assessments in drilling rigs provide a high degree of protection, uncertainty arises due to the behavior of the formation being drilled into, which may cause crucial situations at the rig. To overcome such uncertainties, real-time sensor measurements are used to predict, and thus prevent, such crises. In addition, new understandings of the effective events were derived from raw data. In order to avoid the computational overhead of input feature analysis that hinders time-critical prediction, EventTracker sensitivity analysis, an incremental method that can support dimensionality reduction, was applied to real-world data from 1600 features per each of the 4 wells as input and 6 time series per each of the 4 wells as output. The resulting significant input series were then introduced to two classification methods: Random Forest Classifier and Neural Networks. Performance of the EventTracker method was understood correlated with a conventional manual method that incorporated expert knowledge. More importantly, the outcome of a Neural Network Classifier was improved by reducing the number of inputs according to the results of the EventTracker feature selection. Most important of all, the generation of results of the EventTracker method took fractions of milliseconds that left plenty of time before the next bunch of data samples.

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Section: Related Workmentioning

confidence: 99%

Towards Managing Uncertain Geo-Information for Drilling Disasters Using Event Tracking Sensitivity Analysis

Tavakoli

Poslad

Frühwirth³

et al. 2023

Sensors

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“…Understanding a problem is to build a mental model of it based on the relevant information. Often the problem for a drilling engineer is to combine all the information or to find the right clue even when the data is available (Booth 2011). Some of the information is provided in real time, such as some physical models that are updated accordingly, while other information needs to be computed manually.…”

Section: The Problemmentioning

confidence: 99%

“…Another advantage of RTOCs is cost reduction as subject matter experts are centralized. Booth (2011) has documented the advantages and history of RTOCs.…”

mentioning

confidence: 99%

A Real‐Time Decision Support System for High Cost Oil Well Drilling Operations

Gundersen¹,

Sørmo²,

Aamodt

et al. 2013

AI Magazine

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In this article we present DrillEdge — a commercial and award winning software system that monitors oil-well drilling operations in order to reduce non-productive time (NPT). DrillEdge utilizes case-based reasoning with temporal representations on streaming real-time data, pattern matching and agent systems to predict problems and give advice on how to mitigate the problems. The methods utilized, the architecture, the GUI and development cost in addition to two case studies are documented.

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“…Thus the data can be analyzed not only on a single rig but also in real-time operating centers (RTOC) which has several advantages as e.g. discussed by (Booth, 2011).…”

Section: Related Workmentioning

confidence: 99%

Recommendation of Counteractions for Prevention of Critical Events in Sub-Surface Drilling Environments

Winter

Riedel

Lee

et al. 2013

International Journal of Decision Support System Technology

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Sub-Surface Drilling is the process of making boreholes into the Earth, which can reach depths of many kilometers. One of the major purposes of such boreholes is the exploration of oil or gas bearing formations with the goal to recover the content of such reservoirs. Problems in drilling operations pose serious risks for the crew and the environment and can cause significant financial losses. Critical events usually do not arise abruptly, but develop over time before they escalate. In this work, the authors present a system that integrates sensor data and machine learning algorithms into a decision support system (DSS), thus helping to avoid critical events by monitoring and recommending preventive measures. The authors describe how the DSS is implemented as a distributed system and how data-driven decision support processes are implemented and integrated into the system. The DSS detects drilling operations by recognizing temporal patterns in the sensor data and uses a combination of detected operational rig-states and sensor data to predict and recommend preventive measures for the stuck pipe problem. The sensor data, detection results and predictions are distributed to all stakeholders and displayed in appropriate user interfaces.

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Real-Time Drilling Operations Centers: A History of Functionality and Organizational Purpose—The Second Generation

Cited by 19 publications

References 35 publications

Towards Managing Uncertain Geo-Information for Drilling Disasters Using Event Tracking Sensitivity Analysis

Towards Managing Uncertain Geo-Information for Drilling Disasters Using Event Tracking Sensitivity Analysis

A Real‐Time Decision Support System for High Cost Oil Well Drilling Operations

Recommendation of Counteractions for Prevention of Critical Events in Sub-Surface Drilling Environments

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