摘要：Reservoir water sensitivity damage significantly contributes to production declines in low-permeability oil and gas fields. An accurate and rapid assessment of water sensitivity is essential for effective mitigation or prevention strategies. Facing the intricate challenge of predicting high-dimensional water sensitivity damage, this study leverages trends in intelligent drilling and completion technologies. It adopts a Knowledge-guided, Bayesian Optimization-enhanced Light Gradient Boosting Machine (KBO-LightGBM) for modeling, augmented by Multiple Imputation by Chained Equations and Synthetic Minority Over-sampling Technique (MICE-SMOTE) to address missing and unbalanced data issues in oil and gas fields, thereby enhancing the scientific efficacy of data processing. The framework's precision and practicality were confirmed using data from 270 natural core samples across 15 oil fields. Findings include a correlation coefficient of 0.9679 on the test set, a root mean square error of 3.4797, and a mean absolute percentage error of 4.0936%. Interpretability analysis identified formation water mineralization, burial depth, feldspar content, and initial porosity as the predominant factors affecting water sensitivity. This research distinguishes itself with a broader dataset, covering 15 parameters of formation fluids and rock components. Weighting factor αDK and scale βDK were designed to integrate empirical correlations into LightGBM's loss function, theoretically mitigating model overfitting. Hence, the intelligent framework proposed herein accurately predicts reservoir water sensitivity damage, aiding in the development of reservoir damage control strategies. ? 2024

ISSN号：0952-1976

卷、期、页：卷133子辑E

发表日期：2024-07-01

影响因子：7.400000

期刊分区（SCI为中科院分区）：二区

收录情况：SCIE（科学引文索引网络版）,EI（工程索引）

发表期刊名称：Engineering Applications of Artificial Intelligence

通讯作者：盛科鸣,杜明亮

第一作者：蒋官澄,贺垠博,董腾飞,杨丽丽

论文类型：期刊论文

论文概要：盛科鸣,蒋官澄,杜明亮,贺垠博,董腾飞,杨丽丽，Interpretable knowledge-guided framework for modeling reservoir water-sensitivity damage based on Light Gradient Boosting Machine using Bayesian optimization and hybrid feature mining，Engineering Applications of Artificial Intelligence，2024，卷133子辑E

论文题目：Interpretable knowledge-guided framework for modeling reservoir water-sensitivity damage based on Light Gradient Boosting Machine using Bayesian optimization and hybrid feature mining