基于SOM和PSO的非监督地震相分析技术

地震相分析技术是储层预测的一种重要方法,可以用来描述有利沉积相带的分布规律.传统的地震相聚类分析方法对大数据的处理运算速度较慢,且容易陷入局部极小值,造成聚类分析的结构不准确.本文提出基于自组织神经网络(SOM)和粒子群优化方法(PSO)相结合的地震相分析技术,利用自组织神经网络能够保持原始地震数据的拓扑结构特性的特点,将大量冗余样本压缩为小样本数据,再通过粒子群的全局寻优能力改善K均值聚类的效果.理论模型和实际应用表明该方法能既有效实现数据压缩,又能提供较为准确的全局解,在地震相预测中兼顾计算效率和计算精度.

Unsupervised seismic facies analysis technology based on SOM and PSO

Seismic facies, as the mappable 3D seismic units composed of groups of reflections whose parameters differ from those of adjacent facies units, represent seismic reflections to macro characteristics of sedimentary facies. Seismic facies analysis technique is to describe and interpret the seismic reflection parameters, such as configuration, continuity, amplitude, and frequency, within the stratigraphic framework of a depositional sequence. As a key step in the seismic interpretation workflow, seismic facies analysis determines so much information on depositional process, environment and ultimately can predict potential reservoir only from seismic data in the absence of well data. When the geological information is incomplete or nonexistent, seismic facies analysis is called non-supervised and is performed through unsupervised learning or clustering algorithms. Although unsupervised seismic facies analysis is an effective technique for reservoir prediction, the big seismic data are processed slowly with the traditional methods. In order to overcome the defects of traditional ways which easily fall into the minimum value and lead to the inaccuracy of the cluster of seismic data, this paper proposes a new method to analyse seismic facies combining the Self-Organizing Map (SOM) and the Particle Swarm Optimization (PSO). In this paper, we firstly select the sensitive attribute which can reflect the geological target and normalize the seismic attribute and initialize the SOM network. The reason why we choose SOM is that it can compress a large number of redundant seismic data into a smaller number. As one of the most promising mathematical techniques applied to non-supervised pattern classification, SOM has the characteristics of keeping the topology structure of the original samples. Secondly we will train the seismic attribute one by one in the network, compute the distance between neuron and sample according to Euclidean distance, confirm the optimum matching unit, and update the weight according to renewing criterion. If it reaches to a certain iteration or the weight trends to stabilization, the training is finished, otherwise, return to last step. After the previous data compression, we will improve the K-means cluster using the global optimization of the PSO, which is initialized with a group of random particles (solutions) and then search for optima by updating generations. In every iteration, each particle is updated by following two "best" values. The first one is the best solution (fitness) it has achieved so far, which is called pbest displaying the best location. Another "best" value that is tracked by the particle swarm optimizer is the best value, obtained so far by any particle in the population, which is a global best and called gbest indicating the best swarm. Based on the well trained SOM network, we can find out a proper clustering divide using PSO optimizing method directly, which minimize the fitting degree from which we can get the minimum Euclidean distance then we record down the pbest and gbest. On the basis of results in the last step, we can update all the particles' velocities and locations and cluster them again and keep a record of the corresponding relationship between attribute samples and neurons. If it reaches to the iterative condition, then quits the algorithm, otherwise, returns to last step to recompute them. Finally, we reverse mapping the clustering results into the original samples space to acquire the well classified attribute samples. In the theoretical model, we design a four layers medium model with a horizontal velocity change in the second layer which means the formation lithology variation in the lateral. With the Ricker wavelet forward modeling, we get the synthetic seismogram and then use the algorithm in the last paragraph mentioned to cluster them, which defaults the time samples as the attribute. Based on our method, three kinds of variations in the lateral can be clearly displayed, from which different colors represent the different classifications. Meanwhile, this method has a very good stability and convergence when the iteration times increase the objective function value still is near zero. For the purpose of testing the robustness to noise, we add noise of different Signal to Noise Ratio (SNR) to model, including SNR=25 dB, SNR=10 dB, SNR=2 dB and SNR <1 dB. From the results, we can find that when SNR >1 the clustering performance is very good and the horizontal variation is discriminated very well by the distinct boundaries. Even if SNR <1 we still can detect the changes basically, and the results can be referenced for our research although there are some clustering errors. Especially we select the SNR <1 models to be processed by certain commercial software from which the clustering is completely disordered and disappointing. It indicates that we can get stable results using our method when the seismic data quality is bad. According to the application to real data from Tarim Basin, the seismic facies map based on our method and SOM are better than the commercial software, the border and fault zone of reef facies are depicted more clearly. From comparing the seismic faices to the wells located in the area, we can find out that oil wells W2, W21, W22 and W23 are distributed in the red color area which implies the potential oil reservoir and dry wells W25 and W28 are located in the brown belt which doesn't have oil production processed by our method. At the same time, our improved algorithm can greatly shorten the calculation time from comparison of consuming time between our algorithm and commercial software. The traditional seismic facies analysis methods are usually restricted by the massive seismic data because of very low computational efficiency. In our paper, we try to solve the problem and propose a new multi-attribute clustering method combining the SOM and PSO. We make full use of the SOM advantage of compressing redundant seismic data into a smaller number and keeping the original topology structure, and then improve the K-mean clustering by the PSO global optimizing characteristic. The theoretical model and real data show that our algorithm can realize the compression of the seismic data effectively, and provide a more accurate global solution. For seismic facies prediction, it does well in both the calculation efficiency and the accuracy.