A conceptual model for integrating artificial intelligence with seismic data analysis in reservoir characterization

Abstract

The integration of artificial intelligence (AI) with seismic data analysis marks a significant advancement in reservoir characterization, promising enhanced accuracy, efficiency, and comprehensiveness in geological interpretations. This paper presents a conceptual model that combines machine learning algorithms with traditional seismic processing techniques, creating a hybrid approach for automated, high-precision analysis. The proposed model outlines a systematic workflow involving data preprocessing, feature extraction, application of machine learning, and integration into comprehensive geological models. Key findings emphasize the effectiveness of algorithms like convolutional neural networks in detecting geological features and predicting reservoir properties. The study also highlights the importance of high-quality data, robust validation techniques, and cross-disciplinary collaboration. Practical implications include improved exploration efficiency, optimized drilling strategies, and enhanced reservoir management. Recommendations for industry adoption focus on investing in data quality, computational infrastructure, training, and collaborative efforts. This integration offers a transformative approach to reservoir characterization, driving better decision-making and improved outcomes in exploration and production activities.

Keywords: Artificial Intelligence, Seismic Data Analysis, Reservoir Characterization, Machine Learning, Geological Interpretation, Data Integration.