摘要
Abstract
To improve the reliability of fluid identification in tight gas reservoirs,address the reduced contribution of logging parameters caused by geological factors,and provide support for oil and gas exploration and development,taking a tight reservoir block in the Sichuan basin as the research object,the method combining Pearson correlation coefficient and SHAP value map is adopted to optimize logging parameters.Four machine learning prediction models(BP neural network,support vector machine,Bayesian optimization,and XGBoost)are established.Model performance is compared via the average IAUC value of ROC curves and actual sample verification.The research results show that:①A total of 6 sensitive logging parameters with significant contributions to fluid identification are optimized by integrating the two methods,including DR/Z(elastic modulus difference ratio),C-P(bulk compressibility-Poisson's ratio),A1(three-porosity difference),La/PL(Lame constant/Poisson's ratio),QFGB(peak-base ratio),and IDTST(Stoneley wave slowness).②The average IAUC values of the validation ROC curves for the four models are 0.955,0.994,0.954,and 0.995 in sequence.③Among 18 verification samples,the accuracy rate of the XGBoost model reached 88.9%,while those of the support vector machine,BP neural network,and Bayesian optimization models are 83.3%,72.2%,and 66.6%respectively.④Application in the blind well section of well D4 showed that the XGBoost model's prediction results had the highest consistency with gas test conclusions,and the overall prediction accuracy rate of all wells in the study area reached 84.52%.It is concluded that the XGBoost model has the optimal performance in fluid identification of tight gas reservoirs.The parameter optimization method combining Pearson correlation coefficient and SHAP value map,along with the multi-model comparison strategy,effectively avoids the subjectivity of traditional methods,improves the objectivity and reliability of fluid identification,and provides an effective idea for fluid identification in tight gas reservoirs.关键词
致密气储层/流体识别/测井参数优选/皮尔逊相关系数/SHAP算法/机器学习/XGBoost/ROC曲线Key words
tight gas reservoirs/fluid identification/logging parameter optimization/Pearson correlation coefficient/SHAP algorithm/machine learning/XGBoost/ROC curve
