Journal
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
Volume 177, Issue -, Pages 236-249Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.petrol.2019.02.045
Keywords
Lost circulation; Marun oil field; ANFIS; Artificial neural network; Decision tree
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One of the most prevalent problems in drilling industry is lost circulation which causes intense increase in drilling expenditure as well as operational obstacles such as well instability and blowout. The aim of this research is to develop smart systems for estimating amount of lost circulation making able to use appropriate prevention and remediation methods. To obtain this aim, a large data set were collected from 61 recently drilled wells in Marun oil field in Iran to be used for developing relevant models. After that, using the extracted data set consisting of 1900 data subset, intelligent prediction models including decision tree (DT), adaptive neuro-fuzzy inference systems (ANFIS), artificial neural networks (ANN) and also hybrid artificial neural network namely genetic algorithm-multi-layer perception (GA-MLP) were developed to make a quantitative prediction on lost circulation. The model outputs are then analyzed by various performance indices such as variance accounted for (VAF), root mean square error (RMSE), performance index (PI) and coefficient of determination (R-2). Eventually, it is found that developed models are highly applicable in lost circulation prediction. Concluding remark is that DT model having determination coefficient of 0.9355 and RMSE of 0.091 is superior comparing to other developed models and hybrid ANN (GA-MLP) exhibits lowest prediction performance among other implemented models.
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