A New Optimization Model for 3d Well Design

Shokir, E.M. El-M.; Emera, Mohammed Kamal; Eid, Saad; Wally, A. W.

doi:10.2516/ogst:2004018

Cited by 40 publications

(17 citation statements)

References 13 publications

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“…Using the FSQGA, the optimization result of TMD is 14 807.5 ft in optimal complexity wellbore trajectory. Running time of the algorithm is 3.5075 s. Using the FSQGA to optimal the TMD, it is improved greatly running efficiency of the algorithm and shortened sharply running time which is compared the algorithm optimization results with NPSO (Atashnezhad et al, 2014), GA (Shokir et al, 2004), hCSO (Wood, 2016a), hBFO (Wood, 2016b) and PSO (Shokir et al, 2004). Therefore, it not only enhances the real-time of optimal progress, drilling efficiency and success rate, but also reduces the drilling time and total drilling cost.…”

Section: Discussionmentioning

confidence: 99%

“…j is the number of casing section, j = 1, 2, 3. D 1 to D 5 are the calculated measured depths of specific segments of the wellbore trajectory (Shokir et al, 2004;Atashnezhad et al, 2014). The boundaries of variables are defined in Table A1 in the Appendix.…”

Section: Objective Function Of Optimal 3d Wellbore Trajectorymentioning

confidence: 99%

“…Vertical plane cross-section of a typical complex wellbore trajectory (from Atashnezhad et al, 2014 and modified afterShokir et al, 2004). D1: First build-up; D2: tangent section; D3: drop-off section; D4: hold section; D5: second build-up section; and HD: horizontal section.…”

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confidence: 99%

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FSQGA based 3D complexity wellbore trajectory optimization

Sha¹,

Pan²

2018

Oil & Gas Science and Technology - Rev. IFP Energies nouvel

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Objective Function Of Optimal 3d Wellbore Trajectorymentioning

confidence: 99%

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FSQGA based 3D complexity wellbore trajectory optimization

Sha¹,

Pan²

2018

Oil & Gas Science and Technology - Rev. IFP Energies nouvel

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“…Generic algorithm utilizes three fundamental genetic operations of selection (by selecting two chromosomes according to their fitness. The two selected chromosomes are called parents), crossover (take a copy of the selected parents and apply a crossover operation on them, with a certain probability, to produce two new children), and mutation (after the crossover operation, the two children are produced and later mutated with a certain probability to produce two new children) (Shokir et al, 2004). These operations are used to adjust the chosen solutions and select the most suitable offspring to pass on to succeeding generations.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Application Of Artificial Intelligence Methods In Drilling System Design And Operations: A Review Of The State Of The Art

Bello

Holzmann

Yaqoob

et al. 2015

Journal of Artificial Intelligence and Soft Computing Research

106

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Artificial Intelligence (AI) can be defined as the application of science and engineering with the intent of intelligent machine composition. It involves using tool based on intelligent behavior of humans in solving complex issues, designed in a way to make computers execute tasks that were earlier thought of human intelligence involvement. In comparison to other computational automations, AI facilitates and enables time reduction based on personnel needs and most importantly, the operational expenses.Artificial Intelligence (AI) is an area of great interest and significance in petroleum exploration and production. Over the years, it has made an impact in the industry, and the application has continued to grow within the oil and gas industry. The application in E & P industry has more than 16 years of history with first application dated 1989, for well log interpretation; drill bit diagnosis using neural networks and intelligent reservoir simulator interface. It has been propounded in solving many problems in the oil and gas industry which includes, seismic pattern recognition, reservoir characterisation, permeability and porosity prediction, prediction of PVT properties, drill bits diagnosis, estimating pressure drop in pipes and wells, optimization of well production, well performance, portfolio management and general decision making operations and many more.This paper reviews and analyzes the successful application of artificial intelligence techniques as related to one of the major aspects of the oil and gas industry, drilling capturing the level of application and trend in the industry. A summary of various papers and reports associated with artificial intelligence applications and it limitations will be highlighted. This analysis is expected to contribute to further development of this technique and also determine the neglected areas in the field.

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“…Genetic programming (GP) is a recent development in the field of evolutionary algorithms which extends the classical genetic algorithms 17,18 to a symbolic optimization technique 19 . It is based on so called "tree representation''.…”

Section: Genetic Programmingmentioning

confidence: 99%

Dewpoint Pressure Model for Gas Condensate Reservoirs Based on Genetic Programming

Shokir

2008

All Days

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Successful prediction of the future performance of condensate reservoirs requires accurate values of dew point pressures of the interesting reservoirs. Although the dew point pressure can be determined experimentally from collected laboratory samples, these measurements are frequently not available. In these cases, fluid reservoir properties are determined with the use of empirical correlations or determined iteratively using an equation of state (EOS). The objective of this paper is to present an application of genetic programming (GP)-Orthogonal Least Squares algorithm (OLS) to generate linear-in-parameters dew point pressure model represented by tree structures. The GP-OLS based gas condensate reservoir dew point pressure model was generated as a function of reservoir fluid composition (in terms of mol fractions of methane through heptanes-plus, nitrogen, carbon dioxide, and hydrogen sulphide, and the molecular weight of the heptanes-plus fraction), and reservoir temperature. The new model was developed using experimentally measurements of 245 gas condensate systems covering a wide range of gas properties and reservoir temperatures. One hundred-thirty five gas condensate systems that not introduced in building the new model were used to test and validate it against the other early published correlations. The validity test shows that the new model is more accurate than the other tested correlations, whereas the new model has the lowest average absolute relative error. Therefore, the new model can be considered an alternative method to estimate the dew point pressure when the experimentally measurement is not available.

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A New Optimization Model for 3d Well Design

Cited by 40 publications

References 13 publications

FSQGA based 3D complexity wellbore trajectory optimization

FSQGA based 3D complexity wellbore trajectory optimization

Application Of Artificial Intelligence Methods In Drilling System Design And Operations: A Review Of The State Of The Art

Dewpoint Pressure Model for Gas Condensate Reservoirs Based on Genetic Programming

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