Intelligent Real-Time Status Analysis For Rig-Site Drilling Engineering

Parigot, P.; Havrevold, L.

doi:10.4043/5789-ms

Cited by 11 publications

(3 citation statements)

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“…These distributions look very similar in almost all similartype drilling rigs (offshore or land rigs). This gives the drilling process a similarity property [1,2]. Here we can find a big chance to generalize our findings and analyses.…”

Section: A U T H O R C O P Ysupporting

confidence: 67%

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Distributed recognition system for drilling events detection and classification

Arnaout

O’Leary

Esmael

et al. 2013

HIS

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Several sensor measurements collected from drilling rig during oil well drilling process. These measurements carry information not only about operational states of drilling rig but also about all high-level operations and activities performed by drilling crew. The work presented in this paper shed the light on analysis of hidden lost time in drilling process through automatic detection and classification of drilling operations. This paper develops a novel algorithm for detecting drilling events and operations in sensor data of drilling rig. Expectation Maximization EM and Piecewise Linear Approximation PLA algorithms applied for detecting drilling events. The Expectation Maximization algorithm performs high-level segmentation on hook-load sensor data. In addition, Piecewise Linear Approximation algorithm slices standpipe pressure; pump flow rate; rotational speed and torque of top drive motor into labeled segments (low-level segmentation). Merging results from both Expectation Maximization and Piecewise Linear Approximation gives the suggested algorithm ability to detect all drilling events and activities performed by drilling rig and crew. Moreover, this paper shows the usage of discrete orthonormal basis functions (Gram basis) as a tool to classify drilling operations from detected segments in drilling time series. The classification process performed in cooperation with the concept of Patterns Templates Base. The optimal polynomial degree to represent drilling operations has been concluded through analysis of polynomial spectrum of each drilling operation.

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Section: A U T H O R C O P Ysupporting

confidence: 67%

“…Drilling operations such as formations drilling, making connection for new drillstand, breaking connection, pulling out of hole, running in hole, and cleaning hole are carefully chosen as basic drilling operations performed by drilling crew [6]. Each of those drilling operation has a specific pattern in rig sensors measurements [1].…”

Section: A U T H O R C O P Ymentioning

confidence: 99%

Distributed recognition system for drilling events detection and classification

Arnaout

O’Leary

Esmael

et al. 2013

HIS

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“…3. These include flagging up kicks, lost circulation, and drillstring washout events (Parigot and Havrevold 1988;Aldred et al 2008). More recently, work has been performed focusing on early warning and alarm generation of events, such as the deterioration of hole conditions in the hours/days timescale (Cayeux and Daireaux 2009).…”

Section: Intelligent Alarmsmentioning

confidence: 99%

Automation in Drilling: Future Evolution and Lessons From Aviation

Thorogood

2013

SPE Drilling &Amp; Completion

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Automation of the drilling process has been gathering pace over the last 10 years, with progress made on many fronts: from choke control in managed-pressure-drilling (MPD) operations through a new generation of autodrillers to intelligent systems that prevent the driller from exceeding parameters that might cause downhole problems.Given the current state of automation in drilling, it is clear that in the case of MPD, automation has resulted in closer and more stable control of a process than can be achieved by a manual system. Autodrillers combined with downhole rotary-steerable systems have resulted in improved progress rates compared with manual systems. By contrast, modern rigs with highly mechanized pipe-handling systems are still controlled manually with a crewing arrangement that has changed little, although the technology has evolved dramatically. These developments pose a number of questions: In what direction is automation likely to evolve in the future, and why? Can the drilling community avoid the mistakes of other industries, and how?This paper reviews developments of automation in aviation and describes some of the problems that have arisen. It then outlines the analogous efforts in drilling and identifies lessons that could be learned. This paper presents a rationale on which further automation initiatives might be based and some of the related changes that might be necessary as a result.

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Analysis of the Offshore Drilling Process for Safety Aspects

Bugge¹,

Havrevold²

1992

Fault Detection, Supervision and Safety for Technical Processes 1991

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Intelligent Real-Time Status Analysis For Rig-Site Drilling Engineering

Cited by 11 publications

References 0 publications

Distributed recognition system for drilling events detection and classification

Distributed recognition system for drilling events detection and classification

Automation in Drilling: Future Evolution and Lessons From Aviation

Analysis of the Offshore Drilling Process for Safety Aspects

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