%0 Journal Article %T 基于矿物组分的水平井岩石可钻性级值预测
Prediction of Drillability Level of Horizontal Rock Based on Mineral Composition %A 王伟 %A 李猛 %A 魏健 %A 曾治友 %A 邹文彬 %A 王亮 %A 黄昱昊 %J Mine Engineering %P 164-172 %@ 2329-731X %D 2024 %I Hans Publishing %R 10.12677/me.2024.122019 %X 都沃内油田深部储层以页岩为主,页岩具有明显的层理构造,具有高地层压力、高岩石硬度、强各向异性的特点,导致沿着水平方向钻进过程中储层可钻性差。针对以上问题,为了方便、准确地预测都沃内油田储层段的可钻性,本文在室内实验的基础上,首先,通过扫描电镜和X射线能谱仪得到该区块井的矿物组分和矿物含量,其次为了减少可钻性级值方程的建模参数,降低计算维度,故将矿物组分归类为砂质、泥质、钙质并分别将其作为单参数分析与岩石可钻性的关系,最后建立砂质、泥质、钙质与可钻性级值的多元非线性回归预测模型,其相关性在0.9以上,预测效果精准。依据此模型方法填补了技术空白,完善了页岩储层横向可钻性预测理论,为高效开发页岩油气藏钻头选型和优化钻井设计提供依据。
In the deep reservoirs of the Duvernay Oilfield, shale predominates and exhibits distinct bedding structures characterized by high formation pressure, rock hardness, and strong anisotropy. These properties contribute to poor drilling conditions along the horizontal axis. To address these challenges and facilitate accurate prediction of drilling viability in the Duvernay Oilfield, this study employs a methodological approach. Firstly, based on laboratory experiments, we utilize scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS) to analyze the mineral composition and content of the wells in the area. This helps us simplify the modeling process and decrease computational complexity, mineral components are categorized into sandy, clayey, and calcareous fractions. Each fraction is then analyzed individually to establish its relationship with drilling viability. Finally, a multi-dimensional nonlinear regression prediction model is developed to correlate sandy, clayey, and calcareous fractions with drilling viability, achieving a correlation coefficient of above 0.9 and ensuring precise predictive accuracy. This methodological approach fills a technological gap, enhances the lateral drilling viability prediction theory for shale reservoirs, and provides a basis for efficient drilling tool Selection and optimization of drilling designs for shale oil and gas reservoir development. %K 都沃内油田,页岩,多元非线性回归,矿物组分,横向可钻性预测
Duvernay Oilfield %K Shale %K Multivariate Nonlinear Regression %K Mineral Composition %U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=85107