RSS and Motor Directional Analysis Modelling: The Effect of Local Doglegs on RIH of Completions and Casing

Nobbs, Benjamin; Menand, Stéphane; Dao, Ngoc-Ha

doi:10.2118/203328-ms

Cited by 3 publications

References 2 publications

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A supervised machine learning model to select a cost-effective directional drilling tool

Nour,

Elsayed,

Mahmoud

2024

Sci Rep

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With the increased directional drilling activities in the oil and gas industry, combined with the digital revolution amongst all industry aspects, the need became high to optimize all planning and operational drilling activities. One important step in planning a directional well is to select a directional tool that can deliver the well in a cost-effective manner. Rotary steerable systems (RSS) and positive displacement mud motors (PDM) are the two widely used tools, each with distinct advantages: RSS excels in hole cleaning, sticking avoidance and hole quality in general, while PDM offers versatility and lower operating costs. This paper presents a series of machine learning (ML) models to automate the selection of the optimal directional tool based on offset well data. By processing lithology, directional, drilling performance, tripping and casing running data, the model predicts section time and cost for upcoming wells. Historical data from offset wells were split into training and testing sets and different ML algorithms were tested to choose the most accurate one. The XGBoost algorithm provided the most accurate predictions during testing, outperforming other algorithms. The beauty of the model is that it successfully accounted for variations in formation thicknesses and drilling environment and adjusts tool recommendations accordingly. Results show that no universal rule favors either RSS or PDM; rather, tool selection is highly dependent on well-specific factors. This data-driven approach reduces human bias, enhances decision-making, and could significantly lower field development costs, particularly in aggressive drilling campaigns.

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A supervised machine learning model to select a cost-effective directional drilling tool

Nour,

Elsayed,

Mahmoud

2024

Sci Rep

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Stiff String Torque and Drag: Choosing a Realistic Tortuosity Model

Nobbs¹,

Plee²,

Dao³

2022

IADC/SPE Asia Pacific Drilling Technology Conference and Exhibition

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Selection of the correct tortuosity model is critical when performing a pre-planning stiff string torque and drag analysis. Various tortuosity models exist in the literature however selection and calibration can be complicated. This paper presents a methodology to aid in selecting an appropriate tortuosity model as well as real case studies. The goal is to apply a suitable tortuosity to a planned trajectory that will mimic the expected tortuosity along the wellbore. Depending on the directional drilling driving system employed, the trajectory can be smooth or very tortuous with significant additional local doglegs. Overall, the effect of standard tortuosity models on a run-in-hole (RIH) analysis using a stiff string torque and drag model will be investigated. Industry standard tortuosity models such as sinusoidal, random, random with dependent azimuth and helical will be investigated to help identify when they are appropriate depending on the directional driving system employed. In today's high-cost and complex 3D wells, simulations using smooth planned trajectories together with conventional soft-string torque and drag models have shown its limits in anticipating bottom hole assembly (BHA), casing or completion strings lock-up events. Hence, new methodology shall be introduced to better predict field observations. This methodology shows that the applicable tortuosity model may change depending on the directional driving system used. The drilled path or a motor, with a slide-rotate pattern, may be significantly different to that of the cyclical path drilled by a rotary steerable system (RSS). The case study shows that the additional tortuosity that was generated by the directional driving system had a significant effect on predicted hookload margin as well as elevated forces and stresses along the liner. The stiff string torque and drag and buckling model can analytically calculate the contact points which may vary significantly, both axially and radially, in a tortuous wellbore. Ultimately the selection and calibration of an applicable tortuosity model is necessary when performing a robust stiff string torque and drag analysis. This ability to correctly apply a tortuosity model in preplanning reduces the risk of BHA, casing and/or completion strings getting stuck or locked-up when running in hole, paving the way for a reduction in non-productive time (NPT) and loss of equipment.

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Avoiding High Local Doglegs by Integrating the Drill Bit into the Rotary Steerable System

Hahn,

Heinisch,

Small

et al. 2024

IADC/SPE International Drilling Conference and Exhibition

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Drilling operations in the US Permian Basin shale play with a new 4-¾" Rotary-Steerable Drilling System (RSS) revealed that with high Rates Of Penetration (ROP), high Weight On Bit (WOB) (and other drilling parameters) encountering stringers or interbedded formations can lead to unwanted effects. The Bottom Hole Assembly (BHA) can be deflected before the steering unit with its steering pads can counteract the deflection. This can lead to deviations from the directional plan and High Local Doglegs (HLD) when drilling in the horizontal/lateral section, which can have a negative impact on the electronics within the steering unit. As a countermeasure, a combination of a pin-down connection steering unit with a short box-up connection drill bit, or Integrated Bit (IB), was developed. This combination allows a much shorter bit to steering pad distance. Due to the shorter bit-to-pad-distance an improved steering response is expected resulting in fewer high local dogleg occurrences. The integrated box-up bit basically moves the drive-shaft pin-down connection into the drill bit. The remaining bit shank is very short requiring modifications to the make-up and break-out procedure. Results from the data acquired in the Permian Basin shale play yielded promising results in terms of steering accuracy. When comparing bending cycle load vs. local Dogleg Severity (DLS), local DLS are typically lower and have less bending cycles drilling through these critical local DLS when IB were deployed. The overall average ROP of runs with IB is higher than the overall average ROP of runs with standard bits. The amount of data collected for this study allows a good statistical approach for comparison of the performance of a standard drilling BHA and bit versus a BHA featuring an IB. It proves that by further reducing the distance between the first formation contact point at the drill bit to the second contact point at the steering unit there is an overall improvement in performance and wellbore placement. This provides opportunities for incremental future optimization of the drill bit and BHA system.

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RSS and Motor Directional Analysis Modelling: The Effect of Local Doglegs on RIH of Completions and Casing

Cited by 3 publications

References 2 publications

A supervised machine learning model to select a cost-effective directional drilling tool

A supervised machine learning model to select a cost-effective directional drilling tool

Stiff String Torque and Drag: Choosing a Realistic Tortuosity Model

Avoiding High Local Doglegs by Integrating the Drill Bit into the Rotary Steerable System

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