Farayandno

Farayandno

Application of Artificial Intelligence Algorithms in Predicting and Optimizing Rate of Penetration in the Shadegan Oilfield Formations Based on Hydraulic and Formation Pressure Data

Document Type : Original research

Authors
Mahdi Nazari Saram 1
Rahim Dabiri 2
1 Department of Petroleum, Mining and Materials, CT.C, Islamic Azad University, Tehran, Iran
2 Department of Geology, Ma.C., Islamic Azad University, Mashhad, Iran
10.22034/farayandno.2025.2069800.2012
Abstract
Optimizing the Rate of Penetration (ROP) is a key objective in drilling operations, directly impacting time and cost efficiency. However, accurate ROP prediction remains challenging due to complex, non-linear influences such as formation mechanical properties, hydraulic parameters, and formation pressure. This study employs advanced deep learning architectures—Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM)—to model and predict ROP in the Shadegan oil field. Real-world field data, including flow rate, pump pressure, and formation pressure, were pre-processed, normalized, and structured as time-series inputs. Performance evaluation based on RMSE, R², and MAE demonstrated the superior accuracy of the LSTM model, attributed to its capability to capture long-term temporal dependencies. These findings provide a reliable foundation for optimizing drilling design and execution in similar hydrocarbon reservoirs, ultimately enhancing operational productivity.
Keywords
Rate of Penetration (ROP)
Shadegan oil field
Artificial Intelligence (AI)
LSTM network

Subjects

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