Design Applications of Genetic Algorithms

Abstract

The paper outlines the basis of Genetic Algorithm (GA) optimisation and discusses the areas where these powerful computer based search procedures can best be used. Applications include many kinds of engineering design, financial optimisation, scheduling and finding rules to describe data. An interface between a GA package and the design engineer's problem specification has been developed. The system is simple yet powerful, permitting the engineer to use sophisticated software on a PC without becoming an optimisation expert. Real applications to TLP column design optimisation and scheduling are presented to illustrate the power and flexibility of the method and the simplicity of the interface. Future additional application areas are suggested. Introduction Genetic Algorithms (GA) are a computer based search procedure which can be used for a wide range of optimisation and search problems, including those which are very difficult to handle by more conventional techniques. GAs are particularly suited to problems with nonlinearities, discontinuities or integer parameters, all of which can cause traditional optimisation schemes to fail. Applications include many kinds of engineering design and financial optimisation, scheduling and finding rules to describe data. Suitable problems are those with a solution which can be described by a set of numbers and evaluated by a computer programme to give a single measure of 'goodness'. The GA proposes a population of solutions, choosing parameters from user specified ranges, evaluates the solutions and combines the best ones to generate more and usually better candidates. Most of the computational cost usually lies in the evaluation of the worth of solutions. Typically 2000 evaluations are required for a small problem with 5 independent variables and more for larger problems. 2 Genetic Algorithms 2.1 What is a genetic algorithm? It is a fast computer based SEARCH technique, based on the Darwinian theory of Evolution (ref.1). A population of solutions evolves towards a user defined optimum. GAs find GOOD (but not necessarily optimal) solutions to any problem described by a moderate number of parameters. A major advantage is that they handle DISCRETE parameter values such as number of wells, number of casing types etc. Typical applications in the oil business include: Optimise TLP structures (cost+ weight) Optimise road/drill pad layout Optimise casing program Schedule production Find rules from data GAs perform a fast and efficient (but not necessarily perfect) search among a potentially huge number of solutions. P. 63^

Artificial Intelligence: Excellent Key for Developing E&P Oil Industry in Iraq

“…During the training process the ques- over-trained neural network is similar to a complex non-linear regression analysis (16,24,41) . output is produced as a result of certain set of inputs) and try to mimic its functionality and behavior (4,24,25) .…”

Predicting liquid flow-rate performance through wellhead chokes with genetic and solver optimizers: an oil field case study

“…The GA concept was initially proposed and exploited in the 1960s and 1970s (Holland 1975), and further developed with improved computation power in the 1980s and 1990s (Goldberg 1989;Jefferys 1993;Mitchell 1996). GA methodologies have continued to be refined in the past decade (e.g., Gen and Cheng 2008;Sivanandam and Deepa 2008) and are now frequently applied to provide multi-objective optimization solutions for various oil and gas engineering (Mansouri et al 2015) and portfolio (Wood 2016) challenges.…”

Coupling Programming of an Adaptive Genetic Algorithm with Reservoir Flow Simulation for Infill Oil Well Locations Optimization

“…An optimization Phillips procedure requires the characterization of the function to be optimized (minimized or maximized), known as the objective function, as well as the choice of a proper optimizing method (15) . There are two traditional types of optimizing methods: derivative-based methods, that assure a faster convergence but require evaluations of the objective function derivatives, and direct methods, that require only function evaluations but have slower convergence.…”