A new correlation for calculating carbon dioxide minimum miscibility pressure based on multi-gene genetic programming

Accurate model based on artificial intelligence for prediction of carbon dioxide solubility in aqueous tetra‐n‐butylammonium bromide solutions

“…This figure is a typical structure of a chromosome that is composed of two genes that are expressed algebraically by using Karva language. GEP algorithm uses basic operators such as selection, replacement, cross over, and mutations to minimize the error of solutions . The selection operator is responsible for choosing proper solutions using the concept of survival of the fittest based on cost values of solutions .…”

“…GEP algorithm uses basic operators such as selection, replacement, cross over, and mutations to minimize the error of solutions. 59 The selection operator is responsible for choosing proper solutions using the concept of survival of the fittest based on cost values of solutions. 60 The mutation operator prevents the model to converge at the initial steps of modeling procedure.…”

Carbon dioxide (CO 2 ) handling and carbon capture utilization and sequestration (CCUS) research relevant to natural gas: A collection of published research (2009–2015)

“…The five articles included in this section do provide information that is relevant to natural gas reservoirs, even though oil reservoirs are their main focus. Kaydani et al (2014) formulated a correlation for calculating carbon dioxide minimum miscibility pressure (CO 2 MMP) based on a multi-gene genetic program/algorithm (MGGP). MMP is an important variable in the design of efficient enhanced oil recovery (EOR) using the miscible gas injection technique, but is expensive to derive experimentally.…”

Toward gene expression programming for accurate prognostication of the critical oil flow rate through the choke: correlation development

“…Other types of intelligent methods are called genetic algorithm (GA) which is used widely for the aim of problem optimization successfully. The newer version of GA, which is called genetic programming (GP), was developed to create empirical models between the output and input data . In such computation strategy, the biological behavior of genes and chromosomes are imitated.…”

“…The coefficients of such GEP‐based models can be easily tuned when more databases are available. There are a large number of successful researches in a wide variety of engineering areas which have been conducted in the literature by the use of genetic‐based computations and programmings …”