基于GPU的多点地质统计逐点模拟并行算法的研究
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摘要
油气储层的地质结构及储层参数是决定着储层的储量及产能的关键因素。因此,建立能够表征储层结构及性质的三维数字化模型已经成为油田勘探开发中的一项重要工作。目前,进行三维建模主要依赖于研究区域化变量空间结构性和随机性的地质统计学理论。由于传统的基于变差模型的随机模拟方法在描述复杂地质对象空间几何特征方面的局限,能够表征空间多点之间相关关系的多点地质统计理论应运而生。它结合了基于对象和基于像素随机模拟方法优点,近十年来一直是储层三维建模领域研究的热点。
随着研究的深入,对于地质三维模型的规模和精细程度的要求日益提高。在进行大规模精细地质模拟时,逐点模拟的串行多点地质统计模拟方法中存在的不足日益突出。首先,串行多点地质统计方法的模拟效果受输入参数的影响。高质量的模拟效果受到严格参数的限制,从而需要大量计算,导致运行效率严重下降。其次,串行多点地质统计方法在对地质模式进行建模时,需要大量的内存空间。有限的物理内存会使大规模的模拟失败。逐点模拟的串行多点地质统计模拟方法中的局限性使算法的实用性受到了严重影响。
本文从大规模三维精细地质模拟的需要出发,重点研究了基于通用图形处理器(GPGPU)和计算统一设备架构上(CUDA)的多点地质统计领域的并行计算方法,包括应用于离散变量和连续变量的并行随机模拟方法及地质统计模型的并行计算方法。本文的工作和创新主要体现在：
1.提出了应用于离散地质变量的并行SNESIM方法
ⅰ)根据GPU计算架构的特点,提出SNESIM并行方法的问题分解策略,实现了线程级别的问题划分和计算任务的高度并行,显著提高了计算效率。并且该并行策略不需要大量的内存空间,增强了算法的适应性。
ⅱ)为了减少GPU和CPU之间通信交互而造成的计算瓶颈,提出了基于GPU内部数据缓冲的合并方案,并给出了两种子问题并行合并策略,考虑了兼容性和内存访问冲突问题。实验结果表明了并行问题合并策略的有效性和计算效率。
ⅲ)考虑算法的特性和GPU的内存模型,进一步改进了并行方法的GPU实现。不仅避免了对于模板偏移的大量复杂和重复的计算量,并且对高速共享内存和只读纹理内存的使用进行了优化。
2.提出了应用于连续地质变量的并行Direct Sampling方法ⅰ)针对可变大小邻域节点的搜索问题,改进了对并行邻域条件节点的搜索策略。通过稳定的并行排序算法和辅助变量,显著提高了邻域条件节点的搜索效率。
ⅱ)针对对训练图像上目标点的选择问题,提出了一种并行选取策略。通过改进的并行规约二元函数算法,实现了训练图像上目标点选择的并行化和确定性。
ⅲ)针对模拟大范围地质体的连续性的不足,提出了将搜索椭球概率与搜索领域结合的改进方法,实验结果表明了模拟结果的连续性得到显著改善。
3.对于表征区域化变量相关关系的两点地质统计模型,提出了基于研究区域和统计量的两种并行策略。实验结果表明了基于统计量的并行策略在计算效率方面优于基于研究区域的策略。
总而言之,多点地质统计逐点并行模拟方法与现有的串行方法相比,在并行计算与多点地质统计模拟方法的结合中提出了新的思路,改进了多点地质统计随机模拟方法在大规模三维建模中的适应性,且大幅度提高了模拟效率。
The geological structure of the oil and gas reservoir and the reservoir parameters are the key factors which determine the reserves and production capacity of the reservoir. Therefore, the establishment of a three-dimensional digital model which is able to characterize the structure and nature of the reservoir has become an important task in the oil exploration and development. Currently, three-dimensional modeling mainly depends on the geostatistical theory which focuses on the researches of regionalized variable spatial structure and randomness. Due to the limitations for the variogram-based stochastic methods to describe the geometric characteristics of complex geological objects, multiple point geostatistical theory which could characterize the spacial corelations between multiple points came into being. It combines the advantages of object-based and pixel-based stochastic simulation method, and it has been the research focus of the area of3D reservoir modeling over the past decade.
With further research, the requirement for more large and more sophistication three-dimensional models increases. In the applications of large-scale and sophisticate geologic modeling, deficiencies become increasingly prominent for the current serial mutiple-point statistical simulation methods. Firstly, the quality of the multiple point geostatistical simulation results is influenced by the input parameters. High-quality simulation realizations are produced by the severe parameters, which require a lot of calculation, leading to a serious decline in computational efficiency. Secondly, when performing geostatistical tasks on geological model, the serial multiple point geostatistical method need a lot of memory space. The limitations in the traditional multiple point simualtion methods have severely affected the practicality of the algorithms.
This article focuses on the needs of the large-scale three-dimensional geologic simulation, and the reseaches of the prallel multiple point stochastic simulation algorithms which is based on general-purpose graphics processor(GPGPU) and Compute Unified Device Architecture (CUDA), including the parallel stochastic simulation methods for discrete and continuous variables and the parallel computational methods for the geostatistical model. The work and innovation is mainly reflected in:
1. We proposed a parallel SNESIM method for discrete geological variables simulation. i) According to the characteristics of GPU computing architecture, we proposed a parallel decomposition strategy for the SNESIM method, and implemetated thread-level problem partition and highly parallel computing tasks. The parallel strategy significantly improves the computational efficiency, and it does not require a lot of memory space, so it enhanced the adaptability of the algorithm
ii) In order to avoid the computational bottleneck caused by the communications between the GPU and CPU, we proposed a problem merge strategy based on internal data buffer and two parallel implementations considering the compatibility and memory access conflict. The experimental results shows the effectiveness and efficiency of the parallel consolidation strategy
iii) Considering the characteristics of the algorithm and the GPU memory model, we raised further optimization strategies for the parallel implementation of the method. The strategies not only avoid the large number of complex and repetitive computation for the template offset, but also optimize the usage of high-speed shared memory and read-only texture memory.
2. We proposed a parallel Direct Sampling method for continous geological variables simulation. i) A parallel search strategy is proposed for the searching tasks in the variable-sized neighborhood. Stable parallel sorting algorithm and auxiliary variables, significantly improves the efficiency of the search of the neighborhood conditions node. ii) A parallel selecting strategy is proposed for the selection tasks of the target node in the training images. Through the improved dual function in the parallel reduction algorithm, the parallelization and certainty of the target point selected on the training images could be achieved,
iii) For lack of large scale continuity in the simulations of geological bodies, search ellipsoid is combined with neighborhood seach area. Experimental results show that the continuity in the simulation results can be significantly improved
3. For the two-point geostatistical model which characterizes the relationship of regionalized variables, parallel strategies of area-based and statistics-based methods are proposed. The experimental results show that the statistics-based parallel strategy can be much more efficient than the area-based strategy.
In conclusion, comparing the parallel multiple-point geostatistics methods proposed in this thesis with the existing serial methods, this thesis put forward new ideas in the combination of the parallel computing and multi-point geostatistical simulation methods, improved the adaptability of multiple-point geostatistics stochastic simulation in the applications of the large-scale three-dimensional modeling, and greatly improve the efficiency of the simulation.
随着研究的深入,对于地质三维模型的规模和精细程度的要求日益提高。在进行大规模精细地质模拟时,逐点模拟的串行多点地质统计模拟方法中存在的不足日益突出。首先,串行多点地质统计方法的模拟效果受输入参数的影响。高质量的模拟效果受到严格参数的限制,从而需要大量计算,导致运行效率严重下降。其次,串行多点地质统计方法在对地质模式进行建模时,需要大量的内存空间。有限的物理内存会使大规模的模拟失败。逐点模拟的串行多点地质统计模拟方法中的局限性使算法的实用性受到了严重影响。
本文从大规模三维精细地质模拟的需要出发,重点研究了基于通用图形处理器(GPGPU)和计算统一设备架构上(CUDA)的多点地质统计领域的并行计算方法,包括应用于离散变量和连续变量的并行随机模拟方法及地质统计模型的并行计算方法。本文的工作和创新主要体现在：
1.提出了应用于离散地质变量的并行SNESIM方法
ⅰ)根据GPU计算架构的特点,提出SNESIM并行方法的问题分解策略,实现了线程级别的问题划分和计算任务的高度并行,显著提高了计算效率。并且该并行策略不需要大量的内存空间,增强了算法的适应性。
ⅱ)为了减少GPU和CPU之间通信交互而造成的计算瓶颈,提出了基于GPU内部数据缓冲的合并方案,并给出了两种子问题并行合并策略,考虑了兼容性和内存访问冲突问题。实验结果表明了并行问题合并策略的有效性和计算效率。
ⅲ)考虑算法的特性和GPU的内存模型,进一步改进了并行方法的GPU实现。不仅避免了对于模板偏移的大量复杂和重复的计算量,并且对高速共享内存和只读纹理内存的使用进行了优化。
2.提出了应用于连续地质变量的并行Direct Sampling方法ⅰ)针对可变大小邻域节点的搜索问题,改进了对并行邻域条件节点的搜索策略。通过稳定的并行排序算法和辅助变量,显著提高了邻域条件节点的搜索效率。
ⅱ)针对对训练图像上目标点的选择问题,提出了一种并行选取策略。通过改进的并行规约二元函数算法,实现了训练图像上目标点选择的并行化和确定性。
ⅲ)针对模拟大范围地质体的连续性的不足,提出了将搜索椭球概率与搜索领域结合的改进方法,实验结果表明了模拟结果的连续性得到显著改善。
3.对于表征区域化变量相关关系的两点地质统计模型,提出了基于研究区域和统计量的两种并行策略。实验结果表明了基于统计量的并行策略在计算效率方面优于基于研究区域的策略。
总而言之,多点地质统计逐点并行模拟方法与现有的串行方法相比,在并行计算与多点地质统计模拟方法的结合中提出了新的思路,改进了多点地质统计随机模拟方法在大规模三维建模中的适应性,且大幅度提高了模拟效率。
The geological structure of the oil and gas reservoir and the reservoir parameters are the key factors which determine the reserves and production capacity of the reservoir. Therefore, the establishment of a three-dimensional digital model which is able to characterize the structure and nature of the reservoir has become an important task in the oil exploration and development. Currently, three-dimensional modeling mainly depends on the geostatistical theory which focuses on the researches of regionalized variable spatial structure and randomness. Due to the limitations for the variogram-based stochastic methods to describe the geometric characteristics of complex geological objects, multiple point geostatistical theory which could characterize the spacial corelations between multiple points came into being. It combines the advantages of object-based and pixel-based stochastic simulation method, and it has been the research focus of the area of3D reservoir modeling over the past decade.
With further research, the requirement for more large and more sophistication three-dimensional models increases. In the applications of large-scale and sophisticate geologic modeling, deficiencies become increasingly prominent for the current serial mutiple-point statistical simulation methods. Firstly, the quality of the multiple point geostatistical simulation results is influenced by the input parameters. High-quality simulation realizations are produced by the severe parameters, which require a lot of calculation, leading to a serious decline in computational efficiency. Secondly, when performing geostatistical tasks on geological model, the serial multiple point geostatistical method need a lot of memory space. The limitations in the traditional multiple point simualtion methods have severely affected the practicality of the algorithms.
This article focuses on the needs of the large-scale three-dimensional geologic simulation, and the reseaches of the prallel multiple point stochastic simulation algorithms which is based on general-purpose graphics processor(GPGPU) and Compute Unified Device Architecture (CUDA), including the parallel stochastic simulation methods for discrete and continuous variables and the parallel computational methods for the geostatistical model. The work and innovation is mainly reflected in:
1. We proposed a parallel SNESIM method for discrete geological variables simulation. i) According to the characteristics of GPU computing architecture, we proposed a parallel decomposition strategy for the SNESIM method, and implemetated thread-level problem partition and highly parallel computing tasks. The parallel strategy significantly improves the computational efficiency, and it does not require a lot of memory space, so it enhanced the adaptability of the algorithm
ii) In order to avoid the computational bottleneck caused by the communications between the GPU and CPU, we proposed a problem merge strategy based on internal data buffer and two parallel implementations considering the compatibility and memory access conflict. The experimental results shows the effectiveness and efficiency of the parallel consolidation strategy
iii) Considering the characteristics of the algorithm and the GPU memory model, we raised further optimization strategies for the parallel implementation of the method. The strategies not only avoid the large number of complex and repetitive computation for the template offset, but also optimize the usage of high-speed shared memory and read-only texture memory.
2. We proposed a parallel Direct Sampling method for continous geological variables simulation. i) A parallel search strategy is proposed for the searching tasks in the variable-sized neighborhood. Stable parallel sorting algorithm and auxiliary variables, significantly improves the efficiency of the search of the neighborhood conditions node. ii) A parallel selecting strategy is proposed for the selection tasks of the target node in the training images. Through the improved dual function in the parallel reduction algorithm, the parallelization and certainty of the target point selected on the training images could be achieved,
iii) For lack of large scale continuity in the simulations of geological bodies, search ellipsoid is combined with neighborhood seach area. Experimental results show that the continuity in the simulation results can be significantly improved
3. For the two-point geostatistical model which characterizes the relationship of regionalized variables, parallel strategies of area-based and statistics-based methods are proposed. The experimental results show that the statistics-based parallel strategy can be much more efficient than the area-based strategy.
In conclusion, comparing the parallel multiple-point geostatistics methods proposed in this thesis with the existing serial methods, this thesis put forward new ideas in the combination of the parallel computing and multi-point geostatistical simulation methods, improved the adaptability of multiple-point geostatistics stochastic simulation in the applications of the large-scale three-dimensional modeling, and greatly improve the efficiency of the simulation.
引文
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