Real Time Integration of ECD, Temperature, Well Stability and Geo/Pore Pressure Simulations during Drilling a Challenging HPHT Well

Rommetveit, Rolv; Bjorkevoll, Knut Steinar; Cerasi, Pierre; Havardstein, Stein Tjelta; Fjeldheim, Mikkel; Helset, H.M.; Ødegård, Sven Inge; Nordstrand, Christine

doi:10.2118/127809-ms

Cited by 19 publications

(4 citation statements)

References 11 publications

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“…This reduces the number of false fault detections, but computer support during the re-framing process is still missing. During normal modes of work, real-time models of the drilling operation aids the driller in understanding downhole conditions and making optimal decisions [12][13][14]. When a discrepancy between sensor data and the real-time model is observed, an anomaly that is, the driller might enter the re-framing mode.…”

Section: Making Sense Of the Situationmentioning

confidence: 99%

Closing the Integration Gap for the Next Generation of Drilling Decision Support Systems

Nybø

Sui

2014

All Days

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This paper identify the lack of integration between drilling decision support systems and their users as a barrier towards better decision support and increased drilling automation. In part one of this paper we outline the workings of a next generation of decision support systems that reduce this gap. In part two we present our preliminary results with a time series anomaly detection technique, which the decision support systems we have described would require. Part I -IntroductionThroughout the development of integrated operations and intelligent energy, there is a shared expectation of better decision support in the future. This is to be achieved both through better collaboration between people and through improved data analysis and modelling. For the latter, the most frequently cited challenges are data quality [1, 2] and breaking down "data silos" to make data easier to find and integrate with other data sources, or to make different computer systems talk to each other. Another expectation is increased levels of automation, where the aforementioned decision support is gradually expanded to let the computer not only advice on a decision, but to actually make the decision and carry it out. This too requires a close integration of sensors, different computer systems and machinery.

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Section: Making Sense Of the Situationmentioning

confidence: 99%

Closing the Integration Gap for the Next Generation of Drilling Decision Support Systems

Nybø

Sui

2014

All Days

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“…These models are being used in some drilling solutions in real time. Sensor data is then used together with an advanced PDE model of the well to estimate unmeasured variables such as downhole pressure, to detect undesired events like lost circulation or influx and to predict problems such as bad hole cleaning and stuck pipe (see e. g. Iversen, 2010 andRommetveit et. al., 2010).…”

Section: Drilling Automationmentioning

confidence: 99%

“…Drilltronics (Florence and Iversen, 2010) and eControl (Rommetveit et. al., 2010) are commercially available drilling control and supervision systems, where advanced data models are used to supervise and in some instances control the drilling machinery.…”

Section: Automated Drillingmentioning

confidence: 99%

Drilling seeking automatic control solutions

Godhavn

Pavlov

Kaasa

et al. 2011

IFAC Proceedings Volumes

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“…This process is essential for understanding current-day pore pressure distributions, which are important in exploration and development drilling operations. Accurate pore pressure prediction helps avoid drilling risks because it allows improved tuning of the mud weights and one can reduce drilling costs by wisely choosing the casing point before entering the reservoir or some high-pressure formation, see, e.g., Rommetveit et al (2010) or Gholami et al (2015).…”

Section: Introductionmentioning

confidence: 99%

Statistical modeling for real-time pore pressure prediction from predrill analysis and well logs

Paglia¹,

Eidsvik²,

Grøver

et al. 2019

GEOPHYSICS

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The challenge of pore pressure prediction in an overpressured area near a well is studied. Predrill understanding of pore pressure is available from a 3D geologic model for pressure buildup and release using a basin modeling approach. The pore pressure distribution is updated when well logs are gathered while drilling. Sequential Bayesian methods are used to conduct real-time pore pressure prediction, meaning that every time new well logs are available, the pore pressure distribution is automatically updated ahead of the bit and in every spatial direction (north, east, and depth), with associated uncertainty quantification. Spatial modeling of pore pressure variables means that the data at one well depth location will also be informative of the pore pressure variables at other depths and lateral locations. A workflow is exemplified using real data. The prior model is based on a Gaussian process fitted from geologic modeling of this field, whereas the likelihood model of well-log data is assessed from data in an exploration well in the same area. Results are presented by replaying a drilling situation in this context.

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Real Time Integration of ECD, Temperature, Well Stability and Geo/Pore Pressure Simulations during Drilling a Challenging HPHT Well

Cited by 19 publications

References 11 publications

Closing the Integration Gap for the Next Generation of Drilling Decision Support Systems

Closing the Integration Gap for the Next Generation of Drilling Decision Support Systems

Drilling seeking automatic control solutions

Statistical modeling for real-time pore pressure prediction from predrill analysis and well logs

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