Prediction of Key Development Indicators for Offshore Oilfields Based on Artificial Intelligence

Ke Li, Kai Wang, Chenyang Tang, Yue Pan, Yufei He, Shaobin Cai, Suidong Chen and Yuhui Zhou ()
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Ke Li: Development Research Institute, China National Offshore Oil Corporation Research Institute, Beijing 100028, China
Kai Wang: Development Research Institute, China National Offshore Oil Corporation Research Institute, Beijing 100028, China
Chenyang Tang: Development Research Institute, China National Offshore Oil Corporation Research Institute, Beijing 100028, China
Yue Pan: Development Research Institute, China National Offshore Oil Corporation Research Institute, Beijing 100028, China
Yufei He: Development Research Institute, China National Offshore Oil Corporation Research Institute, Beijing 100028, China
Shaobin Cai: Development Research Institute, China National Offshore Oil Corporation Research Institute, Beijing 100028, China
Suidong Chen: Hubei Key Laboratory of Oil and Gas Exploration and Development Theory and Technology, China University of Geosciences, Wuhan 430100, China
Yuhui Zhou: School of Petroleum Engineering, Yangtze University, Wuhan 430100, China

Energies, 2024, vol. 17, issue 18, 1-22

Abstract: As terrestrial oilfields continue to be explored, the difficulty of exploring new oilfields is constantly increasing. The ocean, which contains abundant oil and gas resources, has become a new field for oil and gas resource development. It is estimated that the total amount of oil resources contained in ocean areas accounts for 33% of the global total, while the corresponding natural gas resources account for 32% of the world’s resources. Current prediction methods, tailored to land oilfields, struggle with offshore differences, hindering accurate forecasts. With oilfield advancements, a vast amount of rapidly generated, complex, and valuable data has piled up. This paper uses AI and GRN-VSN NN to predict offshore oilfield indicators, focusing on model-based formula fitting. It selects highly correlated input indicators for AI-driven prediction of key development metrics. Afterwards, the Shapley additive explanations (SHAP) method was introduced to explain the artificial intelligence model and achieve a reasonable explanation of the measurement’s results. In terms of crude-oil extraction degree, the performance levels of the Long Short-Term Memory (LSTM) neural network, BP neural network, and ResNet-50 neural network are compared. LSTM excels in crude-oil extraction prediction due to its monotonicity, enabling continuous time-series forecasting. Artificial intelligence algorithms have good prediction effects on key development indicators of offshore oilfields, and the prediction accuracy exceeds 92%. The SHAP algorithm offers a rationale for AI model parameters, quantifying input indicators’ contributions to outputs.

Keywords: offshore oilfield; development indicator prediction; artificial intelligence; SHAP (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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