![]() The obtained results showed the accuracy of predicting the GR using ANN and ANFIS. The data from wells 1 and 2 were used to develop the AI models, while well 3 was used to validate these models. A total of 4609 data entries from three wells in the Middle East were used to train and test the models and later validate them. These techniques were artificial neural networks (ANN), adaptive network-based fuzzy logic (ANFIS), and functional networks (FN). In this study, three artificial intelligence (AI) techniques were assessed for their ability to produce accurate models for predicting a synthetic GR log using surface drilling parameters. Both methods require a long time and high cost besides the complexity of applying them. ![]() ![]() ![]() GR can be measure for a retried core or by running the logging while drilling (LWD) tools. Gamma-ray logging (GR) is one of the most crucial elements in petroleum engineering that would assist evaluate oil and gas reservoirs and identify the formation lithology.
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