Managed-Pressure Drilling: Using Model Predictive Control To Improve Pressure Control During Dual-Gradient Drilling

Breyholtz, Øyvind; Nygaard, Gerhard; Nikolaou, Michael

doi:10.2118/124631-pa

Cited by 23 publications

(9 citation statements)

References 24 publications

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“…In normal circumstances, the pressure at the standpipe is usually available. Regarding the other measurements, Carlsen et al (2012) have proposed an instrumentation of the standpipe with a differential pressure sensor to measure the density of the fluid inside the pseudovertical section of the standpipe. Regardless, any solutions that provide the pressure, temperature, and density of the fluid as close as possible to the inlet of the relevant hydraulic network branch are acceptable.…”

Section: Status Of the Underreamer Arms (Point D) And Openholementioning

confidence: 99%

Toward Drilling Automation: On the Necessity of Using Sensors That Relate to Physical Models

Cayeux

Daireaux

Dvergsnes

et al. 2014

SPE Drilling &Amp; Completion

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Current topside and downhole instrumentation at the wellsite has been developed to manually conduct drilling operations. The emergence of automatic drilling-analysis software shows the limitations of today's measurement capabilities. It is therefore time to analyze the requirements for on-site instrumentation to implement new, efficient drilling-automation technologies.On one hand, drilling is facing more and more difficult conditions with narrow geopressure windows, deepwater, or high-pressure/high-temperature (HP/HT) conditions. On the other hand, unconventional hydrocarbon reserves may require a considerable amount of wells to be profitable. Drilling automation, by means of smart safeguards, automatic safety triggers, managed-pressure drilling (MPD), and ultimately complete or semiautonomous drilling rigs, can provide the solution to safely construct wells in these challenging settings.The common denominator for the vast majority of drilling-automation solutions is the use of physical models of the drilling process in the form of heat-transfer, mechanical, and hydraulic models. By analyzing the requirements of those models for necessary boundary conditions, it is possible to derive which measurements should be made both at surface and downhole to obtain stable and accurate calculations. This analysis also provides a way to estimate the necessary accuracy of the boundary conditions to ensure reaching the target control tolerance.By use of the boundary-condition analysis, it is possible to derive precisely which measurements should be performed and where they should be performed. As a result, a typical organization of sensors that is compatible with the implementation of drilling-automation solutions is derived.

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Section: Status Of the Underreamer Arms (Point D) And Openholementioning

confidence: 99%

Toward Drilling Automation: On the Necessity of Using Sensors That Relate to Physical Models

Cayeux

Daireaux

Dvergsnes

et al. 2014

SPE Drilling &Amp; Completion

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“…There are several techniques available to achieve this goal: backpressure MPD [7], low annulus level MPD [8], dualgradient MPD [9], etc. As a summary, all those automation techniques rely on a good estimation of the state of the well, in terms of hydraulic, thermodynamic [10] and mechanical quantities.…”

Section: Concurrent Drilling Automationsmentioning

confidence: 99%

From machine control to drilling control

Cayeux

Daireaux

2012

2012 IEEE International Instrumentation and Measurement Technology Conference Proceedings

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The complexity of drilling operations is constantly increasing. By changing the perspective of automation from a pure machine control mindset to a drilling control one, it is possible to provide proactive assistance to the drilling crew during drilling operations. This needs to take in use physical models of the drilling process. Such mechanical, hydraulic and heat transfer models are dependent on physical quantities that can be derived from real-time sensors. The correlation between the existing measurements and the physical model parameters needs to be analyzed in order to extract which corrections need to be applied on the measurement to extract the required physical values and whether the total sensory system, at the rig site, is complete and robust.

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“…[4], [5]), oil well drilling control systems (e.g. [6]), robotic systems (e.g. [7]), aerospace (e.g.…”

Section: Introductionmentioning

confidence: 99%

Toward Dependable Embedded Model Predictive Control

Johansen

2017

IEEE Systems Journal

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Abstract-While Model Predictive Control (MPC) is the industrially preferred method for advanced control in the process industries, it has not found much use in consumer products and safety-critical embedded systems applications in industries such as automotive, aerospace, medical and robotics. The main barriers are implementability and dependability, where important factors are implementation of advanced numerical optimization algorithms on resource-limited embedded computing platforms and the associated complexity of verification. This challenge comes from a requirement of the use of ultra-reliable hardware and software architectures in safety-critical applications, low-cost hardware in consumer products, or both. This paper surveys the state-of-the-art in the emerging field of dependable embedded MPC, and discusses some key challenges related to its design, implementation and verification. A novel result is the study of a simulator-based performance monitoring and control selection method that monitors and predicts MPC performance and switches to a highly reliable backup controller in cases when the MPC experiences performance issues.

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Managed-Pressure Drilling: Using Model Predictive Control To Improve Pressure Control During Dual-Gradient Drilling

Cited by 23 publications

References 24 publications

Toward Drilling Automation: On the Necessity of Using Sensors That Relate to Physical Models

Toward Drilling Automation: On the Necessity of Using Sensors That Relate to Physical Models

From machine control to drilling control

Toward Dependable Embedded Model Predictive Control

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