Abstract: With the continuous advancement of global oil and gas resource exploration technology,data-intensive seismic exploration is facing the dual challenges of processing efficiency and computing power. Traditional centralized computing architectures are difficult to meet the current efficient processing needs of TB-level or even PB-level seismic data. To cope with this dilemma,this paper proposes and implements an application solution for distributed computing platforms based on Apache Spark in oil exploration,making full use of its memory computing,elastic scheduling and distributed data management capabilities to perform efficient reading,writing,sorting and preprocessing operations on massive seismic data. Through comparative experiments with traditional commercial software in terms of I/O performance,computing parallelism and scalability,the performance advantages and feasibility of the Apache Spark architecture in oil exploration business are verified.

Key words: big data, oil and gas exploration, Apache Spark, distributed computing, data processing