混合遗传算法在气田产量构成优化模型中的应用

在气田开发规划产量构成优化模型的基础上,引入一种单目标模型的改进遗传算法,把无约束优化中的直接解法Powell方法作为一个与选择、交叉和变异平行的算子嵌入到基本遗传算法中;并利用了一种新的退火精确罚函数法处理约束条件,运用MATLAB6.0编写算法程序,选取开发规划产量构成优化模型中的定成本产量最大模型,实例分析该算法在气田开发规划产量构成优化模型中的具体应用,取得了较好的效果

对童氏"7.5B"计算及乙型水驱法的改进

研究了童宪章院士"7.5B"的准确性和适应性,指出了"7.5B"方法只适用于多个油田的统计计算,而不完全适应于单一油藏.从理论上推导了"7.5B"的数学表达式,给出了一条准确计算该值的有效途径.对童氏水油比与采出程度关系乙型水驱法及采出程度与含水关系图版作了改进

Common problems and solutions in plotting theoretical curves of water-cut vs. recovery percent of reserves

制作含水率与采出程度关系理论曲线常采用相渗曲线法和流管法两种方法.在采用相渗曲线法制作理论曲线时,采用统计回归分析相对渗透率与含水率的关系,导致在低含水饱和度时期含水率偏高,在高含水饱和度时期含水率偏低的问题,解决的方法是直接使用实验数据点计算.将体积波及系数取常数往往会得出"中低含水期开发效果较差,高含水期开发效果较好"的通用结论,解决的方法是分析体积波及系数与井网系统、流度比、渗透率变异系数的关系,建立统计关系式

油田开发规划优化决策系统研究

介绍了油田开发规划优化系统的原理与方法、系统结构与模块功能设计。在产油量最大、总成本最低、效益最优等不同优化目标下,实现了数据导入、系统配置、油田开发动态预测、规划优化、产量分配、最优控制等效益规划功能。该系统已应用于油田中长远开发规划,取得了较好效果

JUNO Sensitivity on Proton Decay p→νˉK+p\to \bar\nu K^+ Searches

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this paper, the potential on searching for proton decay in $p\to \bar\nu K^+$ mode with JUNO is investigated.The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits to suppress the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via $p\to \bar\nu K^+$ is 36.9% with a background level of 0.2 events after 10 years of data taking. The estimated sensitivity based on 200 kton-years exposure is $9.6 \times 10^{33}$ years, competitive with the current best limits on the proton lifetime in this channel

JUNO sensitivity on proton decay p → ν K + searches*

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this study, the potential of searching for proton decay in the $p\to \bar{\nu} K^+$ mode with JUNO is investigated. The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits suppression of the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via $p\to \bar{\nu} K^+$ is 36.9% ± 4.9% with a background level of $0.2\pm 0.05({\rm syst})\pm$$0.2({\rm stat})$ events after 10 years of data collection. The estimated sensitivity based on 200 kton-years of exposure is $9.6 \times 10^{33}$ years, which is competitive with the current best limits on the proton lifetime in this channel and complements the use of different detection technologies