GPU/CPU协同加速的双变网格最小二乘逆时偏移理论方法研究

Through the research of high-precision least-squares reverse time migration (RTM) for the complex structure of unconventional oil and gas reservoirs, We want to establish a set of GPU/ CPU collaborative computing bivariate grid least squares RTM method, which is applicable to the needs of the seismic imaging of deep unconventional complex structure and lithology reservoir. RTM has no angle limit and adapts to any complex velocity model, by inducing the traditional RTM into the framework of the least-squares RTM can further improve the limitation of RTM which mainly contains the imaging noise, limited resolution and the amplitude is not balanced for deep reservoir imaging. In this project, we will mainly research the following issues: 1. grid triangulation strategy based on the velocity field changes; 2. the formation of the multi-source simultaneous least-squares migration; 3, R & D dynamic phase encoding algorithm to suppress the crosstalk noise of the imaging; 4. deriving an efficient pre-condition operator and defuzzification operator to reduce the number of inversion iterations to improve the convergence rate; 5. based on the GPU/ CPU algorithm transplant, the speedup ratio could be 30 times faster than the traditional CPU version which makes the algorithm be an appropriate technology for industrial usage. The image obtained by lest-squares migration developed in this project will have higher resolution, more balanced amplitued and faster speed than conventional RTM, which will be of very important practical significance and application value in the exploration and development of unconventional oil and gas reserviors under complex structrure in our country.

通过开展复杂构造下深部非常规油气藏的高精度最小二乘逆时偏移研究，建立一套适用于深部非常规油气藏成像的GPU/CPU协同加速的双变网格最小二乘逆时偏移的理论方法、优化算法、处理模块和适用技术，满足深部非常规复杂构造和岩性油藏地震成像的需求。逆时偏移无倾角限制、适应复杂速度场，将传统的逆时偏移纳入最小二乘反演框架，可进一步改进逆时偏移方法的成像噪音、深部成像振幅不均衡且深部成像分辨率较低等不足，研究内容主要包括：1、针对研究区域速度场分布特征的变网格速度剖分策略；2、形成基于多炮同时偏移的双变网格最小二乘逆时偏移理论方法；3、研发动态的相位编码算法，压制成像的串扰噪音；4、推导高效的预条件算子和去模糊化算子，降低反演迭代次数，加快收敛速度；5、基于GPU/CPU算法移植，使程序的加速比提高30倍，实现基于GPU/CPU协同加速的双变网格最小二乘逆时偏移理论方法及适用技术。

随着勘探对象变得日趋复杂，地震勘探重点正逐步由常规油气储层向深部为主的碳酸盐岩储层及非常规油气储层转移。针对深部储层中构造复杂、成藏机理复杂、储层埋藏较深、信号弱等问题，将传统的逆时偏移纳入最小二乘反演框架实现，开展了复杂构造下深部非常规油气藏的高精度最小二乘逆时偏移研究。同时，结合基于变时间变空间的双变网格策略及相位编码技术，建立一套适用于深部非常规油气藏成像的GPU/CPU协同加速的双变网格最小二乘逆时偏移的理论方法、优化算法、处理模块和适用技术，满足深部非常规复杂构造和岩性油藏地震成像的需求。研究内容主要包括：(1) 研发最小二乘反演框架下优化的高精度最小二乘逆时偏移方法理论方法；(2) 实现基于GPU/CPU协同并行计算加速的高阶有限差分叠前正演模拟和逆时偏移理论方法；(3) 发展变网格建模策略，并研发基于变时间变空间的双变网格高阶有限差分正演及逆时偏移算法；(4) 推导基于偏移过程的动态相位编码算子。通过研究表明：最小二乘逆时偏移方法可以提高常规逆时偏移的成像分辨率，改善常规逆时偏移的保幅性能，补偿深部储层成像能量；GPU/CPU协同并行计算加速算法降低了计算成本；基于变时间变空间的双变网格策略及相位编码技术减小了数据正演模拟及偏移的计算量，进一步改善最小二乘逆时偏移的计算效率，提高最小二乘偏移算法的实用性。整套技术能够进一步推动最小二乘逆时偏移成像技术在深部非常规油气勘探中走向实用化。