SBIR Phase II: A Novel Three-Dimensional Seismic Imaging Method by Migration to Common-Offset in Varible Velocity Media

SBIR 第二阶段：一种新颖的三维地震成像方法，通过迁移到变速介质中的共同偏移距

• 批准号: 9531316
• 负责人: Alexander Popovici
• 金额: $ 31.04万
• 依托单位: 3DGEO DEVELOPMENT INC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-02-15 至 1999-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9531316&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Novel; Three

9531316 Popovici This Small Business Innovation Research Phase II project will develop and test computational software that offers the same accuracy as full 3-D prestack migration at a much lower cost. Applying prestack depth migration to 3-D seismic information is the main computational challenge facing the oil industry today. Cheaper and faster 3-D prestack imaging methods could open up new exploration venues in extremely complicated geological conditions such as those that exist in the overthrust region of the Western United States and below layers of salt in the Gulf of Mexico. The novel imaging algorithm migrates seismic data to a common offset in a laterally-variable velocity earth, and can simplify 3-D prestack depth migration, reduce the number of calculations, and overcome the problems associated with manipulating very large prestack data volumes. The end result of this research effort is a migration to common-offset (MCO) algorithm that can be used to process the two main types of seismic data: marine and land. For marine datasets, MCO coherently stacks data along several azimuths, and is followed by common-azimuth migration for final imaging. For land datasets, MCO coherently stacks data for a predetermined number of vector offsets (fixed azimuth and offset length) and is followed by 3-D Kirchhoff migration for imaging the reduced size dataset. The new MCO algorithm is of interest to oil companies and oil-field contractors. The proposed method better delineates the oil field structure and is used in oil exploration and reservoir monitoring and characterization. Both the oil companies that process seismic data in-house and the dataprocessing contractors would benefit from such a method because it allows sophisticated detailed processing at a much lower cost and over a shorter time frame.

9531316 Popovici这个小企业创新研究第二阶段项目将开发和测试计算软件，以更低的成本提供与全三维叠前偏移相同的精度。将叠前深度偏移应用于三维地震信息是当今石油工业面临的主要计算挑战。更便宜和更快的三维叠前成像方法可以在极其复杂的地质条件下开辟新的勘探地点，例如美国西部的逆掩断层地区和墨西哥湾盐层以下的地质条件。 新的成像算法将地震数据迁移到横向变速地球中的共同偏移距，并且可以简化3-D叠前深度偏移，减少计算次数，并且克服与操纵非常大的叠前数据体相关联的问题。这项研究工作的最终结果是一个共偏移距（MCO）算法，可用于处理两种主要类型的地震数据：海洋和陆地。对于海洋数据集，MCO沿沿着几个方位角相干叠加数据，然后进行共方位角偏移以进行最终成像。对于陆地数据集，MCO相干地堆叠预定数量的矢量偏移（固定方位角和偏移长度）的数据，然后进行3-D基尔霍夫偏移，用于对减小尺寸的数据集进行成像。 新的MCO算法是石油公司和油田承包商感兴趣的。所提出的方法更好地描绘了油田结构，并用于石油勘探和储层监测和表征。在内部处理地震数据的石油公司和数据处理承包商都将受益于这种方法，因为它可以以低得多的成本和较短的时间进行复杂的详细处理。