Tripping Performance Analysis and Quantifying Invisible Lost Time

Contreras, William; Al-Hamrany, Adnan; Wuhaimed, Ali; Mathew, Binil; Albassam, Bassam A.; Ashraf, Hamood; Kashif, Muhammad

doi:10.2523/iptc-19657-ms

Cited by 6 publications

(2 citation statements)

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“…Flat-time operations are of utmost importance and consume a significant portion, ranging from 30% to 40%, of the overall duration dedicated to well construction. Flat time operations encompass various activities including static time without drilling, cessation of drilling, pulling out of the hole (POOH), casing running, cementation, wellhead and blowout preventer (BOP) installation [1]. Historically, a well performance management system (WPMS) has been utilized to reduce the well's flat time by making the learning curve steep and transferable [2] Contreras et al introduced a novel methodology to boost cost savings in the oil and gas drilling industry by enhancing the efficiency of tripping operations.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Approach for Enhancing Drilling Efficiency Through Flat Time Optimization

Elekhteiar,

Salem,

El-Fakharany

2024

Preprint

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Section: Introductionmentioning

confidence: 99%

“…This approach enables real-time performance adjustments and supports a comprehensive historical analysis to monitor and enhance overall rig efficiency. Implementing this method allows operators to significantly cut operational costs, enhance drilling practices, and ensure the timely completion of wells [1].…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Enhancing Drilling Efficiency Through Flat Time Optimization

Elekhteiar,

Salem,

El-Fakharany

2024

Preprint

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Tripping Optimisation for Drilling Systems Automation: Potential of Digital Twins, Transient Models and Control Systems to Reduce Invisible Lost Time in Well Construction

Forshaw

Madeira

Arévalo

2022

Day 2 Thu, March 17, 2022

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Tripping, the process whereby a string is moved in either axial direction makes up 30% of the well construction time and is therefore responsible for a significant portion of capital expenditure by operators. Typically, the focus in the industry in optimizing this segment of the operation has centered on minimizing the slips-to-slips connection time. This references the time taken to swing in and make-up, or breakout and rack-back a stand before engaging elevators to either run-in or pull-out with the next component. This required both human-process optimization through training and technological development of topside equipment, first in isolation and then through systems automation. This paper recognizes these optimization efforts but identifies additional potential to significantly reduce invisible-lost-time (ILT) during tripping operations even further by reducing out-of-slips running time in tripping operations, all while keeping wellbore pressures within the safe operating envelope. Physics-based steady-state fluid dynamics models have been in use for decades to define boundary conditions for these operations. These swab and surge calculations output a velocity limit for moving pipe. Models that are more complex have begun to diffuse into the commercial environment over the last decade and enhance borehole protection by providing a coupled acceleration limit. Acceleration and velocity are inherently linked so an optimization must be performed to arrive at the optimum velocity-time curve. In this paper we present real-time engineering simulations to create a digital twin of the downhole environment and calculate optimum tripping parameters for every stand. The parameters are then passed as set-points to automated rig control systems. The paper summarizes the physics-based modelling as well as the mathematical optimization. The system, including interfaces required to implement control in the context of drilling systems automation is also described. Field examples are presented whereby exposing actual real-time measurements and derived tripping boundary conditions in an intuitive, accessible user interface can lead to performance improvements. The ability to calculate the optimum velocity-time curve is the essential ingredient in gaining efficiency while out-of-slips during tripping operations, and simultaneously staying within a safe operating envelope. The resulting reduction in invisible-lost-time demonstrated, and associated reduction in rig time has obvious financial implications for operators and increasingly more important, helps achieve critical ESG targets. Finally, the paper will touch the need, and applicability of such technology in the energy transition new frontiers, specifically geothermal.

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Drilling Operations Real-Time Monitoring and Alerting Using Advanced Machine Learning Algorithms

Alzahrani

Alotaibi

Aman

2022

Day 3 Wed, November 02, 2022

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Well construction is a lengthy, complex and an expensive process. A drilling rig can spend several months to complete the operation in a single well. When analyzing the type of operations performed by rigs, we can see that almost 30% of the time is consumed while making new hole (i.e., drilling time), while the majority of the rig time represents what is called the flat time. Any optimization of flat time can significantly reduce the well construction cost. Flat time is a group of operation types that involve no drilling operations, such as BHA making, drill pipe tripping, hole conditioning, casing running, cementing, etc. For instance, the performance of two specific types of these operations is only determined by rig crew and rig capabilities, and is not influenced by hole condition or formation related factors. These operations are the drill pipe tripping in cased hole (DPTCH) and the drill pipe connection operations (CONN). In typical well construction operations, the DPTCH and CONN operations represent 30% and 20% of the flat time, respectively. In this paper, we discuss the potential benefits of developing and deploying an automated solution to monitor and alert the performance of these two operations in real time. The paper will explain how the solution can employ advanced machine learning (ML) algorithms to process rig operation sensor data and realize rig states. Then, we discuss the impact on the operation when applying this solution to optimize the DPTCH and CONN operations. A developed solution of real-time monitoring and alerting can contribute to a significant time and cost savings. It also can improve early warning signs and detection of operation troubles. Also, it can be the foundation of many real-time operation optimization solutions.

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Tripping Performance Analysis and Quantifying Invisible Lost Time

Cited by 6 publications

References 0 publications

A Novel Approach for Enhancing Drilling Efficiency Through Flat Time Optimization

A Novel Approach for Enhancing Drilling Efficiency Through Flat Time Optimization

Tripping Optimisation for Drilling Systems Automation: Potential of Digital Twins, Transient Models and Control Systems to Reduce Invisible Lost Time in Well Construction

Drilling Operations Real-Time Monitoring and Alerting Using Advanced Machine Learning Algorithms

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