AI DRIVEN FORECAST ERROR ASSESSMENT MODELS FOR DRILLING IN OIL AND GAS

Accurate forecasting is a significant part of the drilling process in the oil and gas industry as a whole. Geological formation uncertainties in addition to drilling process parameters, are causing various issues in terms of cost overruns, non productive time, and safety issues, necessitating more accurate forecast methods in the process. In addition to this, conventional methods in terms of forecasting are unable to meet the mark in accurately estimating the drilling process's nonlinear relationships, causing issues in forecasts as a prominent feature in them. Nowadays, recent advances in AI technologies introduced numerous other options to accurately predict the forecast error in addition to accuracy in the context of drilling processes in the oil-gas industry as a whole, placing a high emphasis on Forecast error assessment using AI-driven models as a core part of a respective workflow in the context of drilling processes in the oil-gas industry. In the context of recent advances in AI technologies, artificial neural networks, ensemble machine learning, deep learning, as a major part of AI-driven forecast error assessment models in the context of drilling processes in the oil-gas industry, are analyzed to understand the respective benefits of employing respective error estimation methods to accurately assess forecast error in respective process parameters, thereby reducing the forecast error to a certain extent in the context of respective drilling process parameters.