This article explores the application of big data technology in oil and gas exploration, with a particular emphasis on the benefits and impacts of using Apache Spark as a distributed computing engine. The study begins by examining the significant big data challenges encountered in seismic exploration, such as the requirements for efficiently storing and processing vast amounts of seismic data. It then highlights the advantages provided by Spark technology, including its performance as a high-performance distributed computing engine, memory computing capabilities, support for multiple programming languages, scalability, and fault tolerance. By integrating case studies from real-world oil and gas exploration projects, the paper demonstrates Spark's ability to enhance exploration and development efficiency. This includes managing large-scale data I/O operations, performing parallel sorting, and improving overall data processing speed. The case studies further confirm the practical application and benefits of Spark technology in the field of geophysics, offering valuable insights for its technological innovation and development. This study aims to bridge the gap between big data technologies and engineering practices in oil and gas exploration, providing a comprehensive evaluation of Spark's impact on the industry.