非常规综合物化探油气预测研究

非常规综合物化探方法油气预测研究中存在的主要问题为：缺乏系统与综合的基础性理论研究，异常形成的机理还不十分清楚，在参数的优化组合及异常的求取上尚有诸多不足。针对后一种情况，选择松辽盆地东岭构造作为实验区进行了有益的探索。选取低能吸附烃、放射性测氡及土壤热释光三类参数进行了优化组合，分别计算了综合指标MAE与MAC及组合熵，然后对各值采用泛克里格法求取异常，取得了良好的效果。下一步研究应从异常形成的机理入手，建立三维非常规综合物化探油气预测模式，以提高油气预测成功率。     

东昆仑东段中更新世以来的成山作用及其动力转换

对东昆仑造山带东段第四纪构造及与地貌关系的分析表明，现代山盆相间的地貌格局成型于中更新世，且在中更新世以来发生了多次构造变形体制的转换。根据布青山北部查干额热格地区第四系剖面的构造、地层时代及地层与构造关系的分析表明，中更新世时期为伸展构造体制，昆仑山内部开始发生了差异隆升，布青山开始崛起。中更新世末应力体制发生急剧变化，由伸展体制转为收缩事件，又急速转为伸展构造体系，短暂的收缩事件造成了中更新世冲洪积层的褶皱，随后的伸展则导致了影响深刻的向北依次断落的阶地状正断层系统。晚更新世应力体系再度发生重大转换，伸展正断层体系被左旋走滑运动体系所代替，并一直延续至今。中更新世以来多次隆升构造变形体制的转换说明东昆仑地区的成山过程受控于多种动力背景，而非单一的挤压抬升。隆升构造变形体系的确定及其时代约束为深入刻划青藏高原东北缘隆升作用的动力过程提供了重要信息。     

湘中锑(金)矿床成矿物质来源——Ⅰ.微量元素及其实验地球化学证据

为了查明湘中盆地中-低温梯（金）矿床的成矿物质来源，文章应用高分辨率ICP-MS、ICP-AES等方法对岩石与矿石进行了系统测定，并结合作者以往分析数据和实验地球化学结果，对该区盆地及周边基底地层与矿床中的微量元素分布特征及其水/岩反应效应进行了综合分析和系统研究。结果表明：Sb、Au等成矿元素在元古界基底碎屑岩中具有高的背景含量，在近似成矿条件下的水/岩反应实验中，成矿元素的淋出率达20%～90%，元古界基底碎屑岩中的金（锑、钨）矿床的周围蚀变围岩中出现明显的金负异常区。而盆地内泥盆系统地层中的高锑含量仅局限于锑矿床（化）及其蚀变围岩中；湘中锑（金）矿床中的矿石与基底碎屑岩具有相似的稀土元素球粒陨石标准化配分曲线特征，以上微量元素地球化学特征充分证明该区锑（金）矿床锑、金等成矿物质主要来源于元古界底碎屑岩。     

华北克拉通北部内、外缘中生代流体成矿作用研究--以大井、蔡家营典型矿床为例

华北克拉通北部内、外缘中生代形成的有色金属矿种繁多 ,以外缘富 Sn、内缘富 Mo,Cu、Pb、 Zn遍布内、外缘为主要特色。通过内、外缘典型矿床的研究和综合对比 ,认为外缘的富 Sn是与富 Sn的基底有关 ,内缘的富 Mo是与结晶基底地层和岩浆热液关系密切。而内、外缘 Cu、 Pb、 Zn的成矿与中生代构造转折 ,造成大规模陆相火山活动和成矿流体的卸载密切相关     

川滇密西西比河谷型铅锌矿床成矿流体来源研究:流体Na-Cl-Br体系的证据

应用流体包裹体滤液分析方法，测定了川滇7个MVT铅锌矿床成矿流体的Na,Cl,Br含量，结果表明成矿流体的x(Na/Br)和x(Cl/Br)的平均值分别为185和73，并与高度蒸发浓缩的残留海水的x(Na/Br)和x(Cl/Br)相近。成矿流体的Na,Cl含量呈正相关，在lgNa-lgCl图解中呈线性分布。根据这些事实以及矿床地质特征，认为原始成矿卤水起源于蒸发浓缩的残留海水，原始含矿卤水与富含有机质的大气降水混合导致矿质沉淀而成矿。     

燕山东段地区与花岗岩有关的几个问题探讨

详细研究了燕山东段地区花岗岩类的侵入时代、产出形式和成因类型 ,指出该区与花岗岩有关的三个金属成矿系列。     

富碱斑岩中超镁铁深源包体岩石的矿物学特征

在云南省鹤庆县六合乡富碱斑岩体中找到超镁铁深源岩石包体，这对于研究富碱斑岩的起源和演化及其成岩成矿作用具有重要意义。本文较系统地研究了深源包体岩石的矿物学特征，揭示该深源包体具有原始上地幔岩在地幔条件下受到一定程度富集地幔流体交代作用改造的特征，具有富集地幔低程度部分熔融属性，为富碱斑岩的成岩成矿演化提供了重要的矿物学依据。     

Mobility of Phosphorus during the Weathering of Volcanic Ashes

Data are presented on phosphorus mobility during the weathering of rhyolite–dacite tephra of the modern and ancient eruptions. Distal tephra cannot be considered a phosphorus source, since phosphorous has low mobility during weathering both in subaerial and subaqueous sedimentation conditions. However, volcanic ashes exert a fertilization effect, providing the supply of nutrient elements into the ecosystem of internal basins and coastal zones.     

Evaluation of the Reduction in Uncertainty Obtained by Conditioning a 3D Stochastic Channel to Multiwell Pressure Data

A stochastic channel embedded in a background facies is conditioned to data observed at wells. The background facies is a fixed rectangular box. The model parameters consist of geometric parameters that describe the shape, size, and location of the channel, and permeability and porosity in the channel and nonchannel facies. We extend methodology previously developed to condition a stochastic channel to well-test pressure data, and well observations of the channel thickness and the depth of the top of the channel. The main objective of this work is to characterize the reduction in uncertainty in channel model parameters and predicted reservoir performance that can be achieved by conditioning to well-test pressure data at one or more wells. Multiple conditional realizations of the geometric parameters and rock properties are generated to evaluate the uncertainty in model parameters. The ensemble of predictions of reservoir performance generated from the suite of realizations provides a Monte Carlo estimate of the uncertainty in future performance predictions. In addition, we provide some insight on how prior variances, data measurement errors, and sensitivity coefficients interact to determine the reduction in model parameters obtained by conditioning to pressure data and examine the value of active and observation well data in resolving model parameters.     

Combination of Dependent Realizations Within the Gradual Deformation Method

Gradual deformation is a parameterization method that reduces considerably the unknown parameter space of stochastic models. This method can be used in an iterative optimization procedure for constraining stochastic simulations to data that are complex, nonanalytical functions of the simulated variables. This method is based on the fact that linear combinations of multi-Gaussian random functions remain multi-Gaussian random functions. During the past few years, we developed the gradual deformation method by combining independent realizations. This paper investigates another alternative: the combination of dependent realizations. One of our motivations for combining dependent realizations was to improve the numerical stability of the gradual deformation method. Because of limitations both in the size of simulation grids and in the precision of simulation algorithms, numerical realizations of a stochastic model are never perfectly independent. It was shown that the accumulation of very small dependence between realizations might result in significant structural drift from the initial stochastic model. From the combination of random functions whose covariance and cross-covariance are proportional to each other, we derived a new formulation of the gradual deformation method that can explicitly take into account the numerical dependence between realizations. This new formulation allows us to reduce the structural deterioration during the iterative optimization. The problem of combining dependent realizations also arises when deforming conditional realizations of a stochastic model. As opposed to the combination of independent realizations, combining conditional realizations avoids the additional conditioning step during the optimization process. However, this procedure is limited to global deformations with fixed structural parameters.