Genetic programming application in predicting fluid loss severity

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@Article{AMISH:2023:rineng,

• author = "Mohamed Amish and Eta Etta-Agbor",
• title = "Genetic programming application in predicting fluid loss severity",
• journal = "Results in Engineering",
• volume = "20",
• pages = "101464",
• year = "2023",
• ISSN = "2590-1230",
• DOI = "doi:10.1016/j.rineng.2023.101464",
• URL = "https://www.sciencedirect.com/science/article/pii/S2590123023005911",
• keywords = "genetic algorithms, genetic programming, Lost circulation, Machine learning, Multigene genetic algorithms, Drilling. non-productive time",
• abstract = "Numerous wells worldwide encounter significant, costly, and time-consuming lost circulation issues during drilling or while deploying tubulars across naturally fractured or induced fractured formations. This can potentially lead to formation damage, wellbore instability, and even blowouts. Effectively addressing this problem and restoring fluid circulation becomes crucial to curbing non-productive time and overall operational expenses. Although numerous methods have been introduced, a universally accepted industry solution for predicting lost circulation remains absent due to the complex interplay of various factors influencing its severity. Anticipating the onset of circulation loss is imperative to mitigate its impacts, minimise costs, and reduce risks to personnel and the environment. In this study, an innovative machine learning approach employing multigene genetic algorithms is used to analyse a dataset of 16,970 drilling datasets from 61 wells within the Marun oil field, located in Iran, where severe loss of circulation occurred. Geological characteristics, operational drilling parameters, and the properties of the drilling fluid were all considered. The dataset encompasses 19 parameters, of which seven are chosen as inputs for predicting lost circulation incidents. These inputs are then employed to construct a predictive model, employing an 85:15 training-to-test data ratio. To assess the model's performance, unseen datasets are used. The novelty of this study lies in the proposed model's consideration of a concise set of relevant input parameters, particularly real-time surface drilling parameters that are easily accessible for every well. The model attains a remarkable level of prediction accuracy for fluid loss, as indicated by various performance indices. The results indicate a mean absolute error of 1.33, a root mean square error of 2.58, and a coefficient of determination of 0.968. The suggested prediction model is optimised not only for data reduction but also for universal prediction and compatibility with other existing platforms. Moreover, it aids drilling engineers in implementing suitable mitigation strategies and designing optimal values for key operational surface parameters, both prior to and during drilling operations",

}

Genetic Programming entries for Mohamed Amish Eta Etta-Agbor

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