Analysis of Hook Load Data to Optimize Ream and Wash Operations

Freithofnig, H.J.; Spoerker, H.F.; Thonhauser, G.

doi:10.2523/85308-ms

Cited by 3 publications

(2 citation statements)

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“…From those 2 (wells) then being identified of each activity into KPI processed on excel. Each of KPI then being targeted to get the ILT (Invisible Lost Time) [6,11].…”

Section: Materials and Methods 21 Materialsmentioning

confidence: 99%

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The Effect of Invisible Lost Time on Drilling Performance of Geothermal Wells

Pangaribuan¹,

Kasmungin

Prakoso

2020

Journal of Earth Energy Science, Engineering, and Technology

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<em>Drilling activity has been focused in time on each activity to reach target depth (TD) immediately and efficient in cost. The priority also aimed to Geothermal drilling by doing specific measurement on Invisible Lost Time (ILT) as new focus to perform. Time becomes main aspect which it would affect the cost, therefore it is important to complete the well in time manner. The research was done to analyze the offset well of well A, B, C and D in order to identify Productive Time and Non Productive Time. Key Performance Indicator (KPI) has been identified from each activity also targeted from two wells of well B dan Well D due to time efficiency used during operation. The method used by comparing offset wells then continue to identify each KPI by measuring each activity based on ASCII time and Daily Drilling Report (DDR). The result from offset wells showed inefficiency in time with Flat time 49%, Drilling 42% and non-flat time (NPT) 9% from 28 days without completion. KPI based on the crew performance has confirmed that day shift crew performed better than night shift crew. KPI on rate of penetration (ROP) on day shift crew at 6 m/hr and night crew at 3 m/hr. KPI on Weight to Weight on day shift crew at 28.43 minute/stand faster than night shif crew at 34.65 minute/stand. KPI on Tripping in cased hole on day shift crew at 4.5 minute/stand faster than night crew shift at 4.6 minute/stand. KPI on Tripping in open hole on day shift crew at 2.7 minute/stand faster than night shift crew at 3.7 minute/stand. KPI on Tripping out open hole on day shift crew at 3.0 minute/stand slower than night shift crew at 2.8 minute/stand. KPI on Tripping out cased hole on day shift crew at 3.36 minute/stand faster than night crew shift at 3.74 minute/stand. ILT from both wells to 20 % or 5 days inefficiency on each well. It detects of potential savings to 10 billion rupiah from both wells.</em>

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“…From those 2 (wells) then being identified of each activity into KPI processed on excel. Each of KPI then being targeted to get the ILT (Invisible Lost Time) [6,11].…”

Section: Materials and Methods 21 Materialsmentioning

confidence: 99%

“…By combining both ASCII time and DDR, it would be useful to identify each activity more accurate to get exact time of each activity/KPI. Related to this reseach, there are six steps to run the analysis ( Figure 2) such as [6,8]:…”

Section: Methodsmentioning

confidence: 99%

The Effect of Invisible Lost Time on Drilling Performance of Geothermal Wells

Pangaribuan¹,

Kasmungin

Prakoso

2020

Journal of Earth Energy Science, Engineering, and Technology

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Automated Real-Time Hookload and Torque Monitoring

Kucs

Spoerker

Thonhauser

et al. 2008

IADC/SPE Drilling Conference

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Today important parameters to enable a reduction of lost and hidden lost time during drilling, like hook loads for pulling and slacking off the drill string, and torque for drill string rotation, are still taken manually. Analysing torque trends during drilling and reaming is hardly done.A key to reduce lost and hidden lost time by analysing drilling data is to automatically recognize ongoing operations in real time from this data. That allows the automatic identification and picking of pulling-up; slacking-off and rotating hook loads, as well as torque values without spending extra time or work force. Relevant parameters can be sampled and analysed automatically in real time from the rig sensor data stream without interfering drilling operations. This automated process allows monitoring changing torque and drag trends for each stand of drill string moved during drilling, tripping, or reaming operations. To monitor trends in the torque and drag development in real time, parameters may be combined with simulated hook load and torque curves in a graphical way. Simulated values are imported from engineering applications. This allows the user to react immediately, not only after hand taken values are manually entered into a graph. The possibility to broadcast all the data to computers worldwide enables a great level of cooperation. Instant measures against increasing torque and drag can be taken before reaching critical ranges on a stand per stand basis. Also excessive reaming and washing can be avoided as low torque and drag may be an indication for a good hole quality. This can save wellbore treatment time. The paper presents the development of the system, its validation against live well data from recent 20,000ft wells in the Vienna basin and the planned application of the system on an extended reach field development project offshore New Zealand.

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Case History: Automated Drilling Performance Measurement of Crews and Drilling Equipment

Andersen

Sjowall

Maidla

et al. 2009

SPE/IADC Drilling Conference and Exhibition

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This paper describes the development and implementation of a unique new capability to automatically measure the performance of drilling crews, drilling equipment and downtime. The methodology and supporting technology make it possible to carry out a detailed comparison of the performance of equipment and crews across a large number of installations. The system was tested on 8 drilling units (fixed and mobile) in the North Sea.When the data is analysed and plotted as a histogram (frequency against time taken for each task) the information provided gives:• The management a process to select the best supplier based on the performance of its equipment when compared to the technical limit of that equipment.• The crews the most consistent and efficient way of working (best practice). • The management an automated measuring tool to set and follow up the targets for crew performance. • A tool that makes 'hidden down time' visible, so appropriate actions can be taken to improve performance. • A process to see if operations can be eliminated or considerably reduced in time (reaming, circulating, etc).When it comes to crew handling of the equipment the results show a performance improvement of 30%. There are potential time savings of between 40 to 60% in some individual drilling tasks such as slip to slip connection times and weight to weight connection times. This is achieved by performing each operation in a more efficient and consistent way. Optimizing a number of identified KPIs for drilling, including tripping and casing running times, shows a potential saving of between 8 to 15% of the total well construction time. Automated Drilling Performance Measurement (ADPM) has been proven in over 30 wells on 8 rigs in the North Sea. This paper also describes the implementation strategy and approach including the elements of the change management and training that are required to implement this new drilling performance process.

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Analysis of Hook Load Data to Optimize Ream and Wash Operations

Cited by 3 publications

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The Effect of Invisible Lost Time on Drilling Performance of Geothermal Wells

The Effect of Invisible Lost Time on Drilling Performance of Geothermal Wells

Automated Real-Time Hookload and Torque Monitoring

Case History: Automated Drilling Performance Measurement of Crews and Drilling Equipment

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