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

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