1) prediction procedure facing microfacies
面向微相预测法
1.
In order to improve the speed and precision of the BP artificial neural network and the predicted results of reservoir paprameter, this paper starts from the study of samples to select a corresponding study sample model——the prediction procedure facing microfacies according to the different microfacies and their depositional environments of the reservoir.
为了提高ＢＰ人工神经网络学习速度和精度，以及对储参预测的效果，本文从学习样本着手，针对储集层不同的沉积微相，及其沉积环境等特点的不同，选取相应的学习样本模型——面向微相预测法。
2) Microfacies forecasting
储层微相预测
3) step-by-step (forward)(forecasting) method
单步向前预测法
4) future extraction and image correlation
预测相关算法
5) Similar product prediction method
相似产品预测法
6) predictable surface mapping technique
预测面成图法
1.
In the paper, the most predictable surface mapping technique (MPS methed), is used for quantitative on sedimentary environment.
本文运用最优预测面成图法（缩写“ＭＰＳ”）对浙江西部地区晚震旦世含磷地层进行了定量分析，通过各种岩性数据和地理座标的两变量之间的相关关系及趋势面分析，揭示了含磷层的沉积环境。
补充资料：发育进度预测法(见发生期预测)
发育进度预测法(见发生期预测)
发育进度预测法见发生期预测。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条