Wellbore Trajectory Optimization Research Articles

Optimizing the wellbore trajectory of directional wells considering wellbore stability Subjected to the non-independence and uncertainty of geomechanical parameters

Effectively analyzing the wellbore stability risk in directional wells is important in exploring oil and gas resources in complex deep formations. An evaluation model of wellbore stability prefers to determine the wellbore instability pressures related to collapse pressure and fracture pressure that controls the optimized wellbore trajectory and is strongly related to the characteristics of geomechanical parameters, including the mechanical and strength parameters, in-situ stresses, and pore pressure, However, the measured errors and inherent fuzziness during the obtaining of geomechanical parameters introduce the uncertainty characteristics and further produce a probability distribution within a certain range of the wellbore instability pressures. Besides, the determination of horizontal in-situ stresses and the strength parameters also are correlated with the mechanical parameters including elastic modulus and Poisson's ratio. Therefore, an effective means of risk assessment of wellbore instability resorts to introducing uncertainty quantification, depending on the appropriate distribution function, and correlations of geomechanical parameters into the evaluation model of wellbore stability. This work proposes a risk analysis method for wellbore instability considering the uncertainty and correlation of geomechanical parameters. Firstly, the uncertainty characteristics and parameter correlations are quantified by Kolmogorov-Smirnov (K–S) test and Pearson linear coefficient through data sampling of geomechanical parameters. Then, the Monte Carlo method and the Nataf transform are combined to make the randomly generated samples restore the uncertainty and correlation characteristics of the parameters themselves. Finally, the sample is brought into the optimal model to realize the risk analysis of wellbore instability and parameter sensitivity evaluation of the target formation under parameter uncertainty and non-independent conditions. The main research shows that the uncertainty of the prediction results of collapse pressure and fracture pressure is significantly reduced by using the newly proposed method to analyze the risk of wellbore instability. In addition, the results of wellbore trajectory optimization indicate that the uncertainty of in-situ stress makes it possible for the formation rock to experience a changing type of stress state, for example, normal stress faulting changing to the strike-slip one, which significantly affects the evaluation of wellbore stability and the selection of wellbore trajectory optimization. The uncertainty range of collapse pressure prediction is greater than that of fracture pressure regardless of whether the parameters are independent or not. However, the overall value of the fracture pressure compared to the collapse pressure is significantly affected by the wellbore trajectory. Meanwhile, considering the direct effect of a single factor, the in-situ stress has a great influence on the risk analysis of wellbore instability, especially the minimum horizontal in-situ stress has the most obvious influence on the fracture pressure. The influence of geomechanical parameters such as elastic modulus, Poisson's ratio, cohesion, internal friction angle, and tensile strength on wellbore instability is relatively weak. However, the aforementioned variation ignores the correlation characteristics of the parameters themselves. With the correlation characteristics considered, weak parameters such as elastic modulus, Poisson's ratio, and pore pressure have a strong correlation with in-situ stress, and thus exert the indirect effect on wellbore instability.

Well trajectory optimization of ultradeep and high-pressure drilling engineering based on high in situ stress as the main control factor: A case study from the Ordovician carbonate reservoir in the Shunbei area of the Tarim Basin

With increasing oil and gas exploration and development, well trajectory optimization has gradually become the focus of the oil and gas industry. Considering the wellbore instability in the Shunbei area of the Tarim Basin, the well trajectory was scientifically optimized under the guidance of rock mechanics, drilling engineering, and mathematical methods, combined with actual geologic data, and with in situ stress as the main controlling factor. In this paper, the stress state of the wellbore is analyzed by linear elastic theory to establish the stress distribution model of the wellbore. The safety window model of wellbore stability is established using different rock failure criteria to calculate the collapse pressure and fracture pressure of the formation. Based on this, the safe mud density window is defined to achieve wellbore trajectory optimization. Finally, the influence factors of wellbore stability are discussed, and the applicability of different rock failure criteria is evaluated. The results indicate that under the normal faulting stress regime condition in the study area, the direction of horizontal minimum principal stress is the best drilling direction, where the borehole inclination angle of [Formula: see text] > 50° is the optimal well trajectory. The wellbore stabilities of high-angle deviated wells and horizontal wells are better than those of low-angle deviated wells and vertical wells. The calculation results of the Mogi-Coulomb criterion can describe the conditions of the in situ stress field more accurately. The safe windows for different well trajectories are obtained directly by the numerical method, which is very practical for optimizing well trajectories and improving wellbore stability.

Optimization of Drilling Well Design: A Review

Drilling well design optimization reduces total Authorization for Expenditures (AFE) by decreasing well constructing time and expense. Well design is not a constant pattern during the life cycle of the field. It should be optimized by continuous improvements for all aspects of redesigning the well depending on the actual field conditions and problems. The core objective of this study is to deliver a general review of the well design optimization processes and the available studies and applications to employ the well design optimization to solve problems encountered with well design so that cost effectiveness and perfect drilling well performance are achievable. Well design optimization processes include unconventional design(slimhole) compared with fat design, in addition to optimizing casing setting depth selection and casing string loads. Finally, we demonstrate well trajectory design considerations and optimization. The optimization process that mentioned above is significantly reduce drilling cost and time since, slimhole design with smaller casing and hole size reduce mud volume cost, steel cost and pump fuel cost. Optimum casing seat selection can ovoid serious problem such as kick and losses that increase nonproductive time (NPT) if kick tolerance and downhole pressure profile is not considered. Anticipating optimum stress loads in casing design is most effective way to reduce casing strings cost avoiding additional cost for designing with useless worst conditions. Wellbore trajectory optimization with geomechnic consideration is major concern to reduce the problem encountered with high torque, drag, formation collapse that result stuck pipe and non-productive time (NPT).

Constrained optimization assists deviated wellbore trajectory selection from families of quadratic and cubic Bezier curves

The ease of calculation of families of cubic and quadratic Bezier curves using just one or two control variables makes them well-suited for identifying smooth, deviated wellbore trajectories using easy-to-implement, constrained optimization techniques. Most wellbore trajectory optimization methods currently applied during the wellbore planning stages aim to minimize measured depth (MD) while constraining dog-leg severity (DLS) and/or torque and drag to select drillable, smooth wellbores. This study identifies the benefits of conducting MD optimization of Bezier curves using multiple constraints related to different characteristics of DLS and absolute changes in tool-face angle (ΔTFA). Whereas DLS is a major influence on wellbore tortuosity on the multi-meter scale, ΔTFA influences wellbore roughness on a finer scale and in a different way than DLS. As rotary-steerable bottom-hole assemblages are now the standard for drilling the multiple horizontal wells required for tight gas and oil reservoirs, minimizing the magnitude of TFA changes can speed up drilling and reduce borehole roughness. This study identifies the benefits of applying a sequence of up to four constraints (maximum DLS combined with average DLS and/or average or maximum ΔTFA) to minimize MD while achieving the smoothest possible wellbore trajectory from a family of cubic Bezier curves, configured to drill from a vertical starting location to a horizontal finishing location offset in 3D space. On the other hand, families of quadratic Bezier curves drilling some relatively simple trajectories between two points located on a two-dimensional plane can be effectively optimized for MD and smoothness using solely a maximum DLS constraint.

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

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.

Real-time forward modeling and inversion of logging-while-drilling electromagnetic measurements in horizontal wells

Based on the pseudo-analytical equation of electromagnetic log for layered formation, an optimal boundary match method is proposed to adaptively truncate the encountered formation structures. An efficient integral method is put forward to significantly accelerate the convergence of Sommerfeld integral. By asymptotically approximating and subtracting the first reflection/transmission waves from the scattered field, the new Sommerfeld integral method has addressed difficulties encountered by the traditional digital filtering method, such as low computational precision and limited operating range, and realized the acceleration of the computation speed of logging-while-drilling electromagnetic measurements (LWD EM). By making use of the priori information from the offset/pilot wells and interactively adjusting the formation model, the optimum initial guesses of the inversion model is determined in order to predict the nearby formation boundaries. The gradient optimization algorithm is developed and an interactive inversion system for the LWD EM data from the horizontal wells is established. The inverted results of field data demonstrated that the real-time interactive inversion method is capable of providing the accurate boundaries of layers around the wellbore from the LWD EM, and it will benefit the wellbore trajectory optimization and reservoir interpretation.

Study and application of wellbore trajectory optimization for dense cluster wells

In the middle and late stage of oilfield exploration and development, the difficulties such as land acquisition on the surface, high density of underground well pattern and high development cost are serious. Dense cluster Wells are mainly way to development mode transformation and quality and efficiency improvement. Combined with the practice of dense cluster well in Dagang Oilfield, the paper detailed analyzes the difficulties faced by the design of dense cluster well drilling engineering, which including well pattern arrangement, wellbore trajectory optimization, wells anti-collision, all of those are beneficial to realize benefit drilling development. The applications show that this technology has a significant effect on improving wellsite utilization rate, shortening drilling cycle and reducing drilling cost in Dagang oilfield, and has a very important reference and popularization significance in similar oilfield development.

Cellular automata-based multi-objective hybrid grey wolf optimization and particle swarm optimization algorithm for wellbore trajectory optimization

Wellbore trajectory design is a nonlinear and constrained mathematical optimization problem that is used to build a cost-efficient, safe, and easily reachable trajectory. True measured depth (TMD), torque, and strain energy are used as objective functions to evaluate the wellbore trajectory design in this work. The minimum values of these objective functions enable a trajectory to be drilled with minimum drilling cost and maximum safety. A lot of modifications to the original metaheuristic methods were made during previous applications, which mostly improve the exploration capability of original algorithms keeping exploitation capability unaddressed. To address this issue a new hybridization of cellular automata (CA) technique with grey wolf optimization (GWO) and particle swarm optimization (PSO) algorithms is proposed in this paper which solves these three optimization objectives of drilling through 17 tuning variables. The improvements of the original PSO Algorithm are proposed by updating its exploitation phase with the incorporation of the GWO algorithm and the exploration phase by using a cellular automaton. During the optimization, the operational constraints of a wellbore such as true vertical depth and casings along with the bounds of tuning variables were utilized. Better performances were observed in cases of Pareto optimal front, search capabilities, and diversity of solutions by comparing the proposed method with other standard methods like MOCPSO, MOGWO, and MOPSO. Several parametric tests (IGD, SP, MS) were done to investigate the effect of proposed hybridization. The mean value of IGD was 0.0208 by the proposed method, which is 46.8% better than MOCPSO, 49.78% than MOPSO, and 60.80% better than the MOGWO. The proposed optimization method also had the minimum spacing metric and maximum spread.

A Review of Metaheuristic Algorithms for Optimizing 3D Well-Path Designs

Due to a variety of possible good types and so many complex drilling variables and constraints, optimization of the trajectory of a complex wellbore is very challenging. There are several types of well such as directional wells, horizontal wells, redrilling wells, complex structure wells, cluster wells, and extended reach wells etcetera. Over the past few years, the number of unconventional well including deviated well, highly deviated well is steadily increasing. Directional drilling has some advantages over vertical drilling though it is more expensive. In drilling engineering optimization of wellbore plays an important role, which can be optimized based on the minimization of the length of wellbore, minimization of mud pressure, critical pressure etcetera. Till today so many approaches or methods used to optimize this wellbore trajectory. From those methods here in this study we focused on metaheuristic approaches that used to optimize wellbore trajectory. This reduction of the wellbore length helps to establish cost-effective approaches that can be utilized to resolve a group of complex trajectory optimization challenges. For efficient performance (i.e.; quickly locating global optima while taking the smallest amount of computational time) we have to identify flexible control parameters. Later this parameter can be effectively fixed to tune different algorithms. This research will develop a review of the various algorithm used to optimize deviated wellbore trajectories. In the environment of challenging commodity prices, engineers and commercial firms bound to utilize technology to find out a low-cost solution. Technology is also helpful for the automatic solution of a complex problem, which also allows for the better utilization of resources. This review paper helps to find out optimal wellbore trajectory optimization algorithms that can close the technology gap by giving a useful method. This method can generate a solution automatically. Before establishing a different algorithm, those previous authors considered a number of factors and limitations. Safety is one of them. Before drilling depleted zones should be well studied, otherwise, it may be the reason for so many problems. The heuristic approach builds up solutions through the trial and error method. Those methods take a decent amount of time for solving a complex problem. For a single known problem, different metaheuristic approaches take a different amount of time to find the optimal solution. Among different metaheuristic algorithm which was used to find minimum true measured depth (TMD), we try to make a review where we focused on their computational time, minimization of length, method, etcetera.

Wellbore Trajectory Optimization of an Iranian Oilfield Based on Mud Pressure and Failure Zone

Determination of the borehole and fracture initiation positions is the main aim of a borehole stability analysis. A wellbore trajectory optimization with the help of the mud pressure may be unreasonable since the mud pressure can only reflect the degree of difficulty for the initial damage to occur at the wellbore rather than the extent of the wellbore damage. In this work, we investigate the failure extension in different arbitrary inclination boreholes under different in-situ stress regimes. Assuming the plane strain condition, the Mohr-Coulomb, Mogi-Coulomb, and Modified Lade rock failure criteria are utilized. We present an analytical equation to determine the optimum drilling trajectory of an Iranian oilfield. In order to predict the degree of wellbore damage, the initial shear failure location, failure width, and failure depth of arbitrary wellbores are determined. Then a new model is derived to calculate the initial failure area of a directional wellbore because it is more efficient in a wellbore stability analysis. The results obtained show that in the target oilfield, the vertical and low-deviated direction is the optimum drilling path. According to the results of this work, optimization of the wellbore trajectory based on the estimated failure zone is a reasonable method if a considerable failure zone takes place around the borehole wall.

FSQGA based 3D complexity wellbore trajectory optimization

Determination of the trajectory of a complex wellbore is very challenging due to the variety of possible well types, as well as the numerous complicated drilling variables and constraints. The well type could be directional wells, cluster wells, horizontal wells, extended reach wells, redrilling wells, and complex structure wells, etc. The drilling variables and constraints include wellbore length, inclination hold angles, azimuth angles, dogleg severity, true vertical depths, lateral length, casing setting depths, and true vertical depth. In this paper, we propose and develop an improved computational model based on Fibonacci sequence to adjust the quantum rotation step in quantum genetic algorithm for achieving cost-efficient complex wellbore trajectories. By using Fibonacci sequence based quantum genetic algorithm (FSQGA) in a complex searching problem, we can find high-quality globally optimal solutions with high speed through a parallel process. The simulation results show that FSQGA can significantly reduce computation complexity, and reach minimum objection values faster. Meanwhile, minimization of the true measurement depth of complex wellbore trajectory in actual gas-oil field shows that the drilling cost can be reduced up to 4.65%. We believe this new algorithm has the potential to improve drilling efficiency, to reduce the drilling time and drilling cost in real-time wellbore trajectory control.

Hybrid cuckoo search optimization algorithms applied to complex wellbore trajectories aided by dynamic, chaos-enhanced, fat-tailed distribution sampling and metaheuristic profiling

Hybrid cuckoo search optimization (hCSO) algorithms are described and developed in comparison with the standard cuckoo search algorithm (Yang & Deb, 2009). The hCSO involves potentially eight metaheuristic components that complement each other in their search contributions and operate as a “tool box” of modules such that six of them can be easily switched on or off. The metaheuristics are coordinated to progress through five distinct steps that constitute hCSO: (1) initialization; (2) exchange; (3) modification; (4) replacement; (5) metaheuristic labelling, ranking and carry forward. Key amendments introduced to hCSO involve replacing Levy flight solution space sampling with stochastic random sampling of simpler fat-tailed distributions with dynamic sampling windows that move through the distribution as iterations of the algorithm advance. The randomly-extracted samples are further adjusted with scaled-chaotic sequences to provide more flexibility and control over the granularity of the sampling of the solution space. In addition other metaheuristics are added to the standard CSO that improve the balance of the algorithm between local and global searching. Three of the metaheuristics include chaotic adjustments to dynamic stochastic sampling of search metrics distributions (fat-tailed and other, highly non-linear, stepped ranges). Several configurations of the metaheuristics available in the hCSO algorithm are applied to a well-reported complex wellbore trajectory optimization problem. Their performance is compared with the aid of metaheuristic profiling and statistical analysis of the minimum total measured depth (TMD) found in multiple sequential runs. Several configurations of the hCSO are shown to work efficiently in locating the global optimum, avoiding being trapped by the many local optima within the solution space. They do so requiring less computational time than six other evolutionary algorithms evaluated with the same number of iterations and population of generated solutions and similarly developed in Excel VBA code to facilitate metaheuristic profiling.

Key issues and investigation of horizontal well drilling and multistage fracturing in shale gas reservoir

Unconventional shale gas is rapidly increasing as an available source of natural gas in the worldwild. Horizontal well drilling and multistage fracturing are two principle techniques of shale gas resources production. Benefiting most from these two techniques, the United States, Canada, and China have become only three countries to produce shale gas in commercial quantities. The key technical issues emerged in the horizontal drilling and fracturing involve the rock fragmentation issues, wellbore trajectory optimization and control, wellbore stability, cementing and its quality control for long horizontal lateral, and hydraulic fracturing design. Although some progress associated with those technical issues have been achieved in China, there are still many geologic and engineering challenges: (1) the low rate of penetration and fast bit wearing for both PDC and Cone bits are still the primary issues, which makes hard achieve the “one-trip” drilling; (2) wellbore collapse and lost circulation are serious with a certain collapsing period due to natural fractures system; (3) complex multi-fractures propagation behaviors still need to be understood and the fundamentals of fracturing design should be established after undstanding multi-fractures propagation behaviors.

Metaheuristic profiling to assess performance of hybrid evolutionary optimization algorithms applied to complex wellbore trajectories

Metaheuristic profiling is proposed as an effective technique with which to evaluate the relative contributions of the metaheuristic components of hybrid evolutionary optimization algorithms in progressing searches of feasible solution spaces to locate global optimum values of their objective functions. Although many useful evolutionary algorithms have been successfully proposed and tested to solve a wide range of complex mathematical optimization problems, when applied to real-world optimization tasks their performance can often be improved by hybridization with other metaheuristics. A case is made here that in developing optimization algorithms for specific practical applications it is better to treat the available evolutionary algorithms as part of a “toolbox” of metaheuristic components that can be configured in various hybridized combinations. The technique of metaheuristic profiling is evaluated as means of identifying the relative contributions of individual metaheuristic components in contributing to the discovery of optimum solutions over multiple iterations of hybrid algorithms. The metaheuristic profiling technique of a toolbox of metaheuristic components is evaluated in terms of applying seven hybrid evolutionary algorithms to optimize a previously studied complex well-bore trajectory optimization problem. The seven hybrid evolutionary algorithms developed with multiple metaheuristics are built upon standard: genetic; particle swarm; bee colony; ant colony; harmony search, cuckoo search and bat flight algorithms. Pseudocode for each of the hybrid algorithms studied are provided in an appendix. These codes identify the metaheuristics included and the sequence in which they are applied in the hybrid algorithms. All seven hybrid algorithms are coded in VBA based in Microsoft Excel with the assistance of the metaheuristic profiling technique, to provide reliably reproducible solutions to well-bore trajectory design optimization. Analysis of metaheuristic performance also confirms the benefits of fat-tailed distributions, sampled chaotically, in a novel way, to drive certain metaheuristics.

Hybrid bat flight optimization algorithm applied to complex wellbore trajectories highlights the relative contributions of metaheuristic components

A hybrid bat-flight optimization (BFO) algorithm is described and developed in comparison with the original bat-inspired algorithm (Yang, 2010). The changes made remove the need to evaluate and store velocities from previous iterations to calculate new solutions, thereby reducing the computational requirements without negatively impacting the performance of the algorithm as an efficient optimizer. The hybrid BFO consists of six metaheuristic components that complement each other in their contributions to global and local search of solution spaces. The hybrid BFO algorithm is applied to a well-reported complex wellbore trajectory optimization problem previously used to evaluate the performance of evolutionary optimization algorithms. The hybrid BFO is shown to work effectively and efficiently in finding the optimum solution space, requiring significantly less iterations to do so than a hybrid genetic algorithm applied to the same problem, both developed in VBA code.The performance of the hybrid BFO algorithm is further evaluated by a novel technique of metaheuristic profiling introduced in this work. By recording the origin of each solution generated in each iteration of the algorithm, in terms of which metaheuristic component is responsible for producing it, a profile of the origin of the ten highest-ranking solutions in each iteration is constructed. This profile reveals that the metaheuristic components driven by the frequency, loudness and pulse rate (i.e., the bat-echolocation-inspired metrics used to drive the algorithm) contribute to the solutions derived in complementary, but varying ways as the iterations of the algorithm progress. Metaheuristic profiling is considered to be a promising technique for design, performance comparison, improvement and customization of evolutionary algorithms.

The effect of kerogen on the elastic anisotropy of organic-rich shales

The elastic properties of reservoir rocks are important for geomechanics applications; the most important of which are: analysis of stress changes due to production, analysis of rock deformation and failure, wellbore trajectory optimization, and the design of hydraulic fractures. Organic-rich shales are often observed to be strongly anisotropic due to the partial alignment of anisotropic clay minerals and the bedding-parallel lamination of organic material within the shale. Neglecting shale anisotropy may lead to incorrect estimates of the in situ stress or stress changes resulting from production. As a result, isotropic models may fail to describe geomechanical behavior correctly. The distribution of the organic phase plays an important role in determining the elastic properties of organic-rich shales, and this has a significant effect on production-induced stress changes. The presence of kerogen leads to a decrease in all of the elastic moduli, and has a significant effect on the geomechanical behavior of shales. The change in horizontal effective stress for a given change in pore pressure resulting from production is greater for kerogen-rich shales, and the neglect of anisotropy in predicting such stress changes may lead to significant errors.