无黏土相盐水储层钻开液体系构建及其抗污染性能评价

采用NaCl/KCl/HCOONa复配欠饱和盐水作为钻开液的基础液相,自研的聚合物VIS-B作为流型调节剂,可酸溶的改性淀粉STA作为体系的降失水剂,Dua及Jqw作为暂堵材料,构建了一套无黏土相钻开液体系。该体系在密度1.10~1.28 g/cm^3间稳定可调,抗温可达130℃,具有较高的低剪切速率黏度和较好的润滑性能,且能有效抵抗各类储层污染物的污染,满足了Missan油田不同储层段的作业需求。     

优化GD-模糊规则的滑坡预报模型研究

为了提高滑坡灾害预报准确率，改善传统的滑坡监测和预报中存在的参数选取困难及模糊控制系统作为预报模型精确度不高的问题，首先采用山体结构稳定性分析法进行滑坡灾害参数的选取，得出降雨量、含水率、土压力及岩土表面位移增量作为预报参数的结论；其次将选取的参数作为模糊系统的输入，建立滑坡灾害发生概率模型，并引入优化的GD算法修正预报模型中的动态参数，使模糊控制模型具有自适应性；同时与未优化的模糊控制模型以及单独模糊控制模型进行仿真对比，仿真结果表明，该控制算法收敛速度快，具有很好的收敛性；最后将该模型在某滑坡重点灾区实验区进行实验测试，实验结果显示该模型具有较好的收敛性，且预报精度达到90%。     

海月构造带东三段滩坝砂分布及成藏主控因素分析

辽河滩海地区海月构造带东三段沉积期,三角洲前缘砂体受湖岸流和波浪的改造,形成了一系列滩坝砂沉积,细分为滩砂和坝砂,水动力强则形成坝砂,水动力弱则形成滩砂;滩坝砂平行于湖岸线呈带状分布,易于形成岩性圈闭。滩坝砂受砂体发育规模、储层物性、断裂输导体系及封堵性等多种因素控制,在滩坝砂较为发育的东三下段,靠近海南断层、盖州滩断层且封堵条件优越的滩坝砂最易于成藏。     

论三峡水库“蓄清排浑”运用方式及其优化

泥沙问题是三峡工程的关键技术问题之一,在三峡工程论证和初步设计阶段提出水库采取"蓄清排浑"的运用方式,可以解决泥沙问题。2003年水库蓄水运用以来的实践表明,水库基本遵循了"蓄清排浑"调度运用原则,并根据上游来水来沙减少等新情况对水库运用方式进行了优化调整。本文系统分析了三峡水库"蓄清排浑"运行方式及其优化调整的利弊,包括提前5年实施175 m试验性蓄水、汛期水位动态变化、汛末提前蓄水等对水库淤积和坝下游河道演变的影响,提出了进一步优化水库调度方式,形成"蓄清排浑"运用的新模式,保持水库长期使用的建议,试图为三峡水库科学高效安全运用提供科技支撑。     

天荣矿二2煤储层物性及特征

煤储层物性及特征是煤层气地质理论的重要内容，加强其研究对提高煤层气勘探开发成效至关重要。基于天荣矿地质、煤层气勘探及相关测试等资料，采用地质与煤层气地质理论对该矿二₂煤储层物性及特征进行了研究。结果表明:天荣矿二₂煤层物理性质良好，生烃物质丰富；煤层含煤性、稳定性、可采性好，煤层气含量和纯度高，可为煤层气开发提供良好对象和气源条件；煤变质程度高，煤中裂隙相对发育，但其渗透性整体较差，渗透率分异显著且普遍低下；煤储层能量较强且分异显著，煤储层压力状态为欠压—超压型，并以正常—超压煤储层压力状态为主，有利于煤层气高产富集。     

延长油田X油区长8储层孔隙结构特征研究

根据岩心、薄片及压汞资料,系统分析了长8储层的孔隙结构特征,并将其分为4种类型,其中Ⅰ类孔隙类型为优质储层。     

A new method of predicting the saturation pressure of oil reservoir and its application

Saturation pressure is a vital parameter of oil reservoir which can reflect the oilfield characteristics and determine the oilfield development process, and it is determined by experiments in the laboratory in general. However, there was only one well with saturation pressure test in this target reservoir, and it is necessary to determine whether this parameter is right or not.In this work, we present a new method for quickly determining saturation pressure using machine learning algorithms, including random forest regressor (RF), support vector machine (SVM), decision trees (DT), and artificial neural network (ANN or NN). Using these approaches, saturation pressure was obtained by using the initial solution gas-oil ratio (GOR), temperature, API gravity and other reservoir-fluid data available in the oilfields. Compared with the empirical formula for saturation pressure calculation, the calculated result shows that the accuracy given from machine learning is higher than that from other formulas at home and abroad, and has a good match with the lab test. On the basis of the calculated saturation pressure, it can determine whether the reservoir enters into the stage of dissolved gas drive or not, which also provides the basis for maintaining the reservoir pressure by water injection in advance, rational development decision-making and work over measures.This approach above can provide technical guidance for predicting the saturation pressure in the development of different kinds of reservoirs, including the sandstone reservoirs and carbonate reservoirs.