Torque and Drag Software Model Comparison: Impact on Application and Calibration of Field Data

McCormick, John; Frilot, Melissa Anne; Chiu, T. W.

doi:10.2118/143623-ms

Cited by 8 publications

(3 citation statements)

References 4 publications

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“…The soft string model assumes that the entire drill string lies against the low side of the wellbore meaning that the stiffness of the drill string is not accounted for. For modelling purposes, the drill string is represented by a cable divided up into small elements that only carry axial loads and torque, while the contact forces are supported by the wellbore [5].…”

Section: Introductionmentioning

confidence: 99%

“…McCormack et al [5] describe that stiff string models endeavour to do this by taking into account the stiffness and bending moment in the tubular and radial clearance in the wellbore that results. However, they also suggest that even though stiff string models incorporate more variables, it is not necessarily more accurate than a soft string model.…”

Section: Introductionmentioning

confidence: 99%

“…The decision to choose between a soft string or stiff string model is dependent on the well situation. Stiff string models are more beneficial for wells that have high tortuous trajectories, high dogleg severity or stiff tubulars [5].…”

Section: Introductionmentioning

confidence: 99%

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Torque and drag modelling for Redhill South-1 in the Northern Perth Basin, Australia

Smith

Rasouli

2012

Petroleum and Mineral Resources

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Drilling deviated and horizontal wells is commonly used in the oil and gas industry for different purposes. Particularly in unconventional reservoirs such as gas shales or tight formations, horizontal wellbores provide a larger exposure to the formation, which enhances the production from such tight formations. The increase in torque and drag forces downhole in deviated borehole trajectories is one of the technical challenges that needs to be carefully studied during the design phase. There have been a number of different approaches to the way that torque and drag has been modelled in the industry. These include the soft string and stiff string approach and accounting for the effects of viscous fluid flow. The soft string model treats the drill string as a cable and assumes that it lies against the low side of the wellbore, meaning that the stiffness of the drill string is not accounted for. On the other hand, stiff string models take into account the stiffness and bending moment in the drill string and the radial clearance in the wellbore. Fluid flow during drilling results in the loss of the normal component of fluid pressure on the drill string as the flowing condition becomes dynamic. There is also an additional tangential component caused by viscous drag on the drill string due to the fluid flow.In this paper we will present the findings of a study aimed at determining the most appropriate type of torque and drag modelling approach that should be applied for Norwest Energy's Redhill South-1 well and future wells of similar nature. Redhill South-1 was directionally drilled to test the gas potential of the Permian sands in a fault dependent structural closure. Both soft and stiff string approaches will be studied. The necessity to account for viscous drag effects will also be analysed.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Torque and drag modelling for Redhill South-1 in the Northern Perth Basin, Australia

Smith

Rasouli

2012

Petroleum and Mineral Resources

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Use of Machine Learning and Data Analytics to Detect Downhole Abnormalities While Drilling Horizontal Wells, With Real Case Study

Alsaihati

Elkatatny

Mahmoud

et al. 2020

Journal of Energy Resources Technology

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The standard torque and drag (T&D) modeling programs have been extensively used in the oil and gas industry to predict and monitor the T&D forces. In the majority of cases there has been variability in the accuracy between the pre-calculated (based on a T&D model) and actual T&D values, because of the dependence of the model's predictability on guessed inputs (matching parameters) which may not be correctly predicted. Therefore, to have a reliable model, program users must alter the model inputs, and mainly the friction coefficient to match the actual T&D. This, however, can conceal downhole conditions such as cutting beds, tight holes and sticking tendencies. The objective of this study is to develop an intelligent machine to predict the continuous profile of the surface drilling torque to enable detection of operational problems ahead of time. This paper details the development and evaluation of an intelligent system which could promote safer operation and extend the response time limit to prevent undesired events. Actual field data of Well-1, starting from the time of drilling a 5-7/8-inch horizontal section until one day prior to the stuck pipe incident, was used to train and test three models: random forest, artificial neural network, and functional network, with an 80/20 training-to-testing data ratio, to predict the surface drilling torque. The independent variables for the model are the drilling surface parameters, namely: flow rate (Q), hook load (HL), rate of penetration (ROP), rotary speed (RS), standpipe pressure (SPP), and weight-on-bit (WOB). The prediction capability of the models was evaluated in terms of correlation of coefficient (R) and average absolute error percentage (AAPE). The model with the highest R and lowest AAPE was selected to continue with the analysis to detect downhole abnormalities. The best developed model was used to predict the surface drilling torque in the last day leading up to the incident in Well-1, which represents the normal and healthy trend. Then the model was coupled with a multivariate metric distance called “Mahalanobis” to be used as a classification tool to measure how close an actual observation is to the predictive normal and healthy trend. Based on a pre-determined threshold, each actual observation was labeled “NORMAL” or “ANOMAL”. Well-2 with a stuck pipe incident was used to assess the capability of the developed system in detecting downhole abnormalities. The results showed that in Well-1, where a stuck pipe incident was reported, a continuous alarm was detected by the developed system nine hours before the drilling crew observed any abnormality, while the alarm was detected seven hours prior to any observation by the crew in Well-2. The developed intelligent system could help the drilling crew to detect downhole abnormalities in real time, react and take corrective action to mitigate the problem promptly.

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Torque and Drag Modeling Advanced Techniques and Troubleshooting

McCormick

Liu

2012

SPE Annual Technical Conference and Exhibition

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Extreme torque and drag (T&D), especially when unplanned, is a primary limiter to the reach of horizontal and extended-reach wells. Many engineers use T&D software models without a thorough understanding of how the input affects the calculations in the program. This paper describes best practices in complicated T&D modeling applications. The discussion in this paper is applicable to wells with horizontal laterals, extended-reach wells, wells with difficult geometries, and all complicated well operations that require T&D models for planning and troubleshooting. Specifically applicable are operations that T&D programs have difficulty handling, such as running dual strings, expandable casing operations, drilling with casing, and jarring operations. Most engineers involved in downhole operations from operators to service companies run either T&D models or base decisions related to their wells and operations on reports from these programs. Many of the engineers and managers in the oilfield can benefit from this guide on best practices for a commonly executed, but little understood, component to well planning. Few papers address the practical application side of T&D modeling, and most engineers and managers have only a basic understanding of how to create a model or interpret the results. This paper disseminates technical information to increase global competence in a vital part of any challenging well drilled. Drilling extreme and challenging wells is becoming more common, and increased knowledge for T&D modeling and interpretation is needed.

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Torque and Drag Software Model Comparison: Impact on Application and Calibration of Field Data

Cited by 8 publications

References 4 publications

Torque and drag modelling for Redhill South-1 in the Northern Perth Basin, Australia

Torque and drag modelling for Redhill South-1 in the Northern Perth Basin, Australia

Use of Machine Learning and Data Analytics to Detect Downhole Abnormalities While Drilling Horizontal Wells, With Real Case Study

Torque and Drag Modeling Advanced Techniques and Troubleshooting

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