Middle East Case-Study Review of a New Free-Pipe Log for Stuck-Pipe Determination and Pipe-Recovery Techniques

Torne, Juan; Rourke, Marvin; Derouen, Brock; Kessler, Calvin

doi:10.2118/147859-ms

Cited by 4 publications

(2 citation statements)

References 3 publications

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“…Recently, some research is being conducted in order to determine the characteristics of stuck pipe such as the depth of pipe sticking. Torne et al (2011) tried to determine the depth of pipe sticking by means of continuous Free-Pipe logs. These studies were the base of primary comparative analysis that could identify the pipe sticking mechanisms in addition to its probability prediction.…”

Section: Introductionmentioning

confidence: 99%

Prediction of drilling pipe sticking by active learning method (ALM)

Naraghi¹,

Ezzatyar²,

Jamshidi³

2013

J. Petroleum Gas Eng.

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Stuck piping is a common problem with tremendous impact on drilling efficiency and costs in oil industry. Generally, the stuck pipe troubles are solved after their occurrences by using some standard techniques; here we attempt to predict the causes of occurrence of such problems to eschew risks and excessive drilling costs. If these risks are identified in advance, better solutions can be provided to reduce the associated consequences. Based on the literature, this problem is caused by numerous parameters, such as drilling fluid properties and the characteristics of the mud cake that is formed while drilling. In this study, an attempt is made to develop a model for stuck pipe prediction. To consider all aspects of pipe sticking and behavior of the involved variables, the fuzzy logic and active learning method (ALM) can be used as a primary predictive tool. Active Learning Method is a robust recursive fuzzy modeling without computational complexity. These methods are broadly used in many industries; including oil and gas. This paper proposes a systematic approach for pipe stuck prediction based on ALM. The results of this method are more accurate than other methods and prediction accuracy is close to perfect either in stuck or non-stuck cases. This study presents a case study in which the ALM is used successfully to estimate pipe sticking. Thus, the proposed method possesses reliable results for prediction of pipe stuck, and can be used in order to minimize the risk of pipe sticking.

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Section: Introductionmentioning

confidence: 99%

Prediction of drilling pipe sticking by active learning method (ALM)

Naraghi¹,

Ezzatyar²,

Jamshidi³

2013

J. Petroleum Gas Eng.

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show abstract

“…In order to back off pipe, estimation of free point of the stuck pipe is the key point. Two common approaches are utilized to locate the free point: pipe stretch calculations and free point (FP) logs that utilize magnetic properties of pipe or applying stretch and torque [2].…”

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confidence: 99%

A New Approach for Estimating Free Point in Fishing of Stuck Pipe using Artificial Neural Network

Nejad¹,

Shahbazi²

2013

IJCA

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Stuck pipe is a common problem in drilling industry. It accounts for the major rig time losses each year in the petroleum industry. In cases where common solutions such as pulling up and pushing down, rotating, jarring, and changing flow rate don't work, then backing off is the last resort. To have a successful back off operation, estimating location of the free point is vital.In this study, an attempt is made to estimate the free point in stuck pipe cases using the drilling data and artificial neural network approach. For this purpose, drilling data such as mud properties, pipe rotation, rate of penetration, and some other parameters are required. In this study, artificial neural networks (ANNs) model using field data from more than 40 wells was employed, and results were compared to field results. ANN model was constructed with a supervised learning algorithm and feed forward back propagation learning rule is used for training the network. The statistical error analysis results obtained by the models and acceptable values for correlation coefficient indicate that ANN model is successful in free point prediction. KeywordsFree point, stuck pipe, artificial neural network.

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Successful Pipe Recovery Solutions in Unique Stuck Scenarios for Coiled Tubing and Downhole Completions

AlMarzooqi,

Faqir,

Nasr

et al. 2023

Day 4 Thu, October 05, 2023

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Considering the advancement of smart completions getting stuck downhole is a high risk in today's working environment. To avoid incurred costs in a rig and rig spread rates, it is eminent to have reliable and quick pipe recovery tools (PRT) and methods for special cases. To complete this challenge the nonexplosive plasma punchers and cutters play an important role. Convectional PRT utilizes HAZMAT materials and involves both HSE and logistical challenges increasing the delivery times to the wellsite for direct execution. This step is bypassed for non-explosive plasma PRT. The slim coiled tubing (CT) recovery puncher and cutter range from ¾″ to 1″ in size; applicable on 1 ¾″ - 2″ CT produces a clean cut for further fishing intervention. For unconventional cases where the standard tools cannot pass the slimmer PRT is required. Finally, a serious challenge is packer mandrels with cut/release windows < 1-2ft could be addressed by plasma PRT. The Adnoc Offshore real results include 4 successful CT recovery operations mobilized in under 24 hours and successfully executed as low as 10 hours of operating time. The plasma PRT also helped to recover the stuck dual completions with small restrictions and managed to cut at points with deviation >80 degrees. Moreover, the PRT can cut in any pipe condition such as in neutral, compression, or under tension allowing to perform the job prior to the rig move. Having the ability to cut in restrictive conditions with high accuracy, more than 100's successful nonexplosive PRT punchers and plasma cutter run with very high success rates. The non-explosive PRT can be air shipped by chopper to offshore locations minimizing downtime on logistics such as vessel availability, and weather challenges. It is fulfilling the governmental security requirements for HAZMAT logistics, storage, and handling that are restrictive to the explosives or chemicals PRTs.

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Middle East Case-Study Review of a New Free-Pipe Log for Stuck-Pipe Determination and Pipe-Recovery Techniques

Cited by 4 publications

References 3 publications

Prediction of drilling pipe sticking by active learning method (ALM)

Prediction of drilling pipe sticking by active learning method (ALM)

A New Approach for Estimating Free Point in Fishing of Stuck Pipe using Artificial Neural Network

Successful Pipe Recovery Solutions in Unique Stuck Scenarios for Coiled Tubing and Downhole Completions

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