基于Markov链模型的储层岩相随机模拟

在油气储层随机建模研究中，基于Markov链模型的方法是一类较受欢迎的技术，同时也是一类不成熟的技术，问题的症结之一在于侧向的转移概率矩阵很难求取，针对这种情况，作者在深入理解Walther相律的基础上，借鉴模拟退火算法的相应思路，提出了一种岩相模拟的新方法，该方法依据不同岩相的百分比进行随机模拟得到一幅初始图象，而后以按岩相组织剖面得到的垂向和侧向的岩相转移概率矩阵的相似性作为判别标准对图象进行扰动，直至得到满意的图象，二维模型试算结果表明了这种岩相随机模拟方法的可行性。     

Geochemical evidence for contribution of ore-forming materials from peraluminous granite basement —— Taking Fucheng pluton and No. 6722 uranium deposit in southern Jiangxi Province as examples

Source of ore-forming material is always one of the fundamental subjects in the study of mineral deposits. Recently, in order to probe deep sources of ore-forming materials, many interna-tional and Chinese investigators have paid much attention to the rel…     

提高叠加地震波分辨率的一种反演方法

子波提取是采用优化快速模拟退火法进行反演，提高叠加地震波分辨率的关键。本文应用最小平方法计算在合成地震记录与井旁地震记录差平方条件下的地震子波，制作合成地震记录，并进行反演以达到提高信噪比的目的。这是一种通过钻井资料来去噪、提高地震分辨率的方法，该方法原理简单、使用方便、效果较理想，有推广的意义。     

On Modelling Discrete Geological Structures as Markov Random Fields

The purpose of this paper is to extend the locally based prediction methodology of BayMar to a global one by modelling discrete spatial structures as Markov random fields. BayMar uses one-dimensional Markov-properties for estimating spatial correlation and Bayesian updating for locally integrating prior and additional information. The methodology of this paper introduces a new estimator of the field parameters based on the maximum likelihood technique for one-dimensional Markov chains. This makes the estimator straightforward to calculate also when there is a large amount of missing observations, which often is the case in geological applications. We make simulations (both unconditional and conditional on the observed data) and maximum a posteriori predictions (restorations) of the non-observed data using Markov chain Monte Carlo methods, in the restoration case by employing simulated annealing. The described method gives satisfactory predictions, while more work is needed in order to simulate, since it appears to have a tendency to overestimate strong spatial dependence. It provides an important development compared to the BayMar-methodology by facilitating global predictions and improved use of sparse data.     

模拟退火算法在水驱气藏物质平衡计算中的应用

在利用物质平衡方程计算水驱气藏地质储量时 ,传统的试凑法虽然有一定科学合理性 ,但由于存在很多不确定参数 ,在精确计算中因工作量大 ,很难推广。为此 ,在分析水驱气藏物质平衡方程的基础上 ,建立最优化模型 ,并引入全局寻优性较好的模拟退火方法进行求解。经实际计算表明 ,方法具有全局收敛性 ,计算结果有较高可信度。     

极大熵准则在降雨强度-历时-频率模型参数估计中的应用

根据频率与重现期的关系推导出三类常用降雨强度-历时-频率模型的无条件及条件概率分布(概率密度)函数及与模型相对应的约束。极大熵与极大似然准则产生一致估计,本文尝试基于极大熵准则建立降雨强度-历时-频率模型参数估计的优化模型,应用模拟退火算法求解该优化模型。根据比较发现,极大熵估计有时比常用的极大似然估计和最小二乘估计更精确。     

混合模型试验等效水深截断系统优化设计研究

进行了深海海洋平台混合模型等效水深截断系统优化设计试验研究,优化方法选用混合离散变量模拟退火法,目标函数为截断系统和原系统的静力特性相似程度。以一个转塔式系泊浮式生产系统为例进行了等效水深截断系统优化设计计算。     

复杂地表条件下共反射面元(CRS)叠加方法研究

在地表地形复杂的情况下,静校正不易做好,这是制约山地资料处理质量的一个很重要的因素.复杂地表共反射面元（CRS）叠加不需对叠前数据做静校正,而且在得到叠加剖面后可以利用叠加得到的波场参数剖面实现基准面重建.地震数据的试算表明,复杂地表CRS叠加得出的剖面与常规处理剖面相比有着较高的信噪比和同相轴连续性.与水平地表CRS叠加不同的是,在复杂地表CRS叠加的时距公式中,波场三参数耦合,难以通过简化CRS道集的方法将它们全部分离并逐个优化.引入模拟退火算法后,有效地解决了这一组合优化的难题.     

Effective medium theory and multiple linear regression-based velocity estimation in syn-rift clastic sequence of the Krishna–Godavari basin,India

An inclusion model, based on the Kuster–Toksöz effective medium theory along with Gassmann theory, is tested to forward model velocities for fluid-saturated rocks. A simulated annealing algorithm, along with the inclusion model, effectively inverts measured compressional velocity (V_P) to achieve an effective pore aspect ratio at each depth in a depth variant manner, continuously along with depth. Early Cretaceous syn-rift clastic sediments at two different depth intervals from two wells [well A (2160–2274 m) and well B (5222–5303 m)], in the Krishna–Godavari basin, India, are used for this study. Shear velocity (V_S) estimated using modelled pore aspect ratio offers a high correlation coefficient (>0.95 for both the wells) with measured data. The modelled pore aspect ratio distribution suggests the decrease in pore aspect ratio for the deeper interval, mainly due to increased effective vertical stress. The pore aspect ratio analysis in relation to total porosity and volume of clay reveals that the clay volume has insignificant influence in shaping the pore geometry in the studied intervals. An approach based on multiple linear regression method effectively predicts velocity as a linear function of total porosity, the volume of clay and the modelled pore-space aspect ratio of the rock. We achieved a significant match between measured and predicted velocities. The correlation coefficients between measured and modelled velocities are considerably high (approximately 0.85 and 0.8, for V_P and V_S, respectively). This process indicates the possible influence of pore geometry along with total porosity and volume of clay on velocity.     

CNOP-P-based parameter sensitivity for double-gyre variation in ROMS with simulated annealing algorithm

Reducing the error of sensitive parameters by studying the parameters sensitivity can reduce the uncertainty of the model,while simulating double-gyre variation in Regional Ocean Modeling System(ROMS).Conditional Nonlinear Optimal Perturbation related to Parameter(CNOP-P)is an effective method of studying the parameters sensitivity,which represents a type of parameter error with maximum nonlinear development at the prediction time.Intelligent algorithms have been widely applied to solving Conditional Nonlinear Optimal Perturbation(CNOP).In the paper,we proposed an improved simulated annealing(SA)algorithm to solve CNOP-P to get the optimal parameters error,studied the sensitivity of the single parameter and the combination of multiple parameters and verified the effect of reducing the error of sensitive parameters on reducing the uncertainty of model simulation.Specifically,we firstly found the non-period oscillation of kinetic energy time series of double gyre variation,then extracted two transition periods,which are respectively from high energy to low energy and from low energy to high energy.For every transition period,three parameters,respectively wind amplitude(WD),viscosity coefficient(VC)and linear bottom drag coefficient(RDRG),were studied by CNOP-P solved with SA algorithm.Finally,for sensitive parameters,their effect on model simulation is verified.Experiments results showed that the sensitivity order is WD>VC>>RDRG,the effect of the combination of multiple sensitive parameters is greater than that of single parameter superposition and the reduction of error of sensitive parameters can effectively reduce model prediction error which confirmed the importance of sensitive parameters analysis.