Optimizing the separation factor along a directional well trajectory to minimize collision risk

Khosravanian, Rasool; Mansouri, Vahid; Wood, David A.; Aadnøy, Bernt S.

doi:10.1007/s13202-020-00876-7

Cited by 15 publications

(2 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the success of directional drilling depends on choosing the best path, which can be only done by the optimization of wellbore trajectory. A wellbore trajectory can be optimized considering several parameters; among them, length, torque, energy, rate of penetration, separation factor is the most influential [ 4 – 9 ]. Some pieces of research optimized the wellbore trajectory by considering one parameter [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

“…Among the metaheuristic algorithms, genetic algorithms (GA), particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony optimization (ABC), harmony search (HS) were utilized for well trajectory optimization [ 2 , 4 , 16 ]. To improve the issues faced by the metaheuristic algorithms and to improve the efficiency, some hybrid algorithms, for example, hybrid cuckoo search optimization (HCSO), hybrid bat flight optimization (HBFO) were introduced [ 17 – 19 ]. Due to the hybridization, these algorithms showed some significant improvements in the exploration capabilities [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A hybrid multi objective cellular spotted hyena optimizer for wellbore trajectory optimization

et al. 2022

View full text Add to dashboard Cite

Cost and safety are critical factors in the oil and gas industry for optimizing wellbore trajectory, which is a constrained and nonlinear optimization problem. In this work, the wellbore trajectory is optimized using the true measured depth, well profile energy, and torque. Numerous metaheuristic algorithms were employed to optimize these objectives by tuning 17 constrained variables, with notable drawbacks including decreased exploitation/exploration capability, local optima trapping, non-uniform distribution of non-dominated solutions, and inability to track isolated minima. The purpose of this work is to propose a modified multi-objective cellular spotted hyena algorithm (MOCSHOPSO) for optimizing true measured depth, well profile energy, and torque. To overcome the aforementioned difficulties, the modification incorporates cellular automata (CA) and particle swarm optimization (PSO). By adding CA, the SHO’s exploration phase is enhanced, and the SHO’s hunting mechanisms are modified with PSO’s velocity update property. Several geophysical and operational constraints have been utilized during trajectory optimization and data has been collected from the Gulf of Suez oil field. The proposed algorithm was compared with the standard methods (MOCPSO, MOSHO, MOCGWO) and observed significant improvements in terms of better distribution of non-dominated solutions, better-searching capability, a minimum number of isolated minima, and better Pareto optimal front. These significant improvements were validated by analysing the algorithms in terms of some statistical analysis, such as IGD, MS, SP, and ER. The proposed algorithm has obtained the lowest values in IGD, SP and ER, on the other side highest values in MS. Finally, an adaptive neighbourhood mechanism has been proposed which showed better performance than the fixed neighbourhood topology such as L5, L9, C9, C13, C21, and C25. Hopefully, this newly proposed modified algorithm will pave the way for better wellbore trajectory optimization.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A hybrid multi objective cellular spotted hyena optimizer for wellbore trajectory optimization

et al. 2022

View full text Add to dashboard Cite

show abstract

Study on separation factor models for well anti-collision analysis

Madeira

Sui

2021

J Petrol Explor Prod Technol

View full text Add to dashboard Cite

Anti-collision analysis has been becoming even more important in the past few years with the increasing amount of wells drilled in highly congested fields. The separation factor (SF) is a critical safety parameter to avoid wellbore collisions, where the pedal curve method (PCM) is the commonly used way for the SF calculation. However, such method may be conservative in many situations and possibly triggers an unnecessary early stop drilling due to its conservation. In this study, our work aims to evaluate and compare the different types of the SF models. In addition, an alternative and supplement method of the PCM, called as elliptic cylinder of uncertainty (ECoU) method, is proposed. The radius of the ECoU considers the distance of the intersecting points of the center-to-center line and the cylinder surfaces of both reference and offset wells. By introducing the cylinder surfaces into the SF calculation, it could extend the collision avoidance along the whole well path to reduce multiple-points collision possibility. The findings in the study help us for better understanding of the SF analysis and also show that using the ECoU indication has a great potential in fields applications as a viable method to keep the drilling operations’ safety.

show abstract

Selection of Optimal Well Trajectory Using Multi-Objective Genetic Algorithm and TOPSIS Method

Yavari,

Qajar,

Aadnoy

et al. 2023

Arab J Sci Eng

View full text Add to dashboard Cite

This study presents a novel approach for optimizing well paths in extended reach drilling (ERD) wells. Different trajectories can be used for ERD wells, each with its pros and cons. Previous research overlooked certain objective functions in single-objective optimization and lacked an autonomous method for selecting the best solution from Pareto optimal solutions in multi-objective optimizations. Furthermore, they lacked comparing different profiles in well design. Risk assessment and operational factors, which greatly influence optimization and drilling success, were insufficiently considered. This study utilized the multi-objective genetic algorithm (MOGA) and the technique for order preference by similarity to an ideal solution (TOPSIS) method to select the optimal well path based on torque, wellbore length, risk (e.g., keyseat), and required tools. First, all possible trajectories were determined, and MOGA identified the optimal path with minimal torque and length. The fuzzy decision-making method automatically selected the best solution from the Pareto optimal solution set. The associated risks and required tools are evaluated for each trajectory. Finally, the TOPSIS method selected the optimal trajectory based on torque, length, risks, and required tools. The case study demonstrated that the undersection path was the most advantageous trajectory for ERD wells, with a 60% closeness to the ideal state. The multiple build trajectory achieved 57% closeness, while the build and hold and double build paths had lower closeness values (43 and 28%, respectively). Consequently, it can be inferred that in the context of ERD wells, it is preferable to carry out the deviation process at deeper depths.

show abstract

Optimizing the separation factor along a directional well trajectory to minimize collision risk

Cited by 15 publications

References 21 publications

A hybrid multi objective cellular spotted hyena optimizer for wellbore trajectory optimization

A hybrid multi objective cellular spotted hyena optimizer for wellbore trajectory optimization

Study on separation factor models for well anti-collision analysis

Selection of Optimal Well Trajectory Using Multi-Objective Genetic Algorithm and TOPSIS Method

Contact Info

Product

Resources

About