Integration of Multi-Source Data for Better Reservoir Characterization

Reservoir characterization is a critical aspect of oil and gas exploration, directly influencing the decision-making process in drilling and production. Traditional methods rely heavily on seismic data, but the integration of multi-source data, facilitated by advancements in artificial intelligence (AI) and machine learning (ML), has been shown to enhance the accuracy and reliability of reservoir models significantly. This article explores integrating seismic data with well logs, electromagnetic surveys, and other geophysical methods to improve reservoir characterization. A case study from the North Sea oil field demonstrates the practical application of these techniques, highlighting the improvements in subsurface imaging and reservoir understanding. The article concludes with a discussing of the future directions and potential of multi-source data integration in the oil and gas industry.

کلیدواژه ها:

Reservoir-characterization ، Machine-learning ، Seismic ، Artificial-intelligence ، Inversion