Logging prediction model of coal seam gas content in Southern Yanchuan gas field
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摘要:
研究目的 煤层含气量是煤层气资源评价与开发的核心参数，但当前含气量预测模型普遍存在精度不足、泛化能力弱等问题，制约着煤层气的勘探开发。
研究方法 基于延川南气田煤层含气量的测井响应特征，利用MIV (Mean Impact Value)方法优选测井参数，引入BP神经网络与随机森林思想，建立高精度煤层含气量预测模型。
研究结果 相比传统的多元线性回归模型，BP神经网络模型与随机森林模型的预测精度有明显提升，其中随机森林模型预测精度更高。
结论 随机森林模型更适用于研究区煤层含气量的预测，基于模型预测结果，研究区煤层含气量的分布范围为4.84~21.83 m3/t，平均为11.63 m3/t；平面上，煤层含气量由东南向西北逐渐升高，变化规律与煤层埋深规律大体一致；纵向上，随着埋深的增大，煤层含气量逐渐升高，但含气量分布的离散程度增大。
Abstract:Objective The coal seam gas content is a core parameter for resource assessment and development, but current gas content prediction models generally suffer from issues such as insufficient accuracy and weak generalization ability, which hinder the exploration and development of coalbed methane.
Methods Based on the logging response characteristics of coal seam gas content in Southern Yanchuan gas field, the MIV (Mean Impact Value) method was utilized to optimize the logging parameters. The BP neural network and random forest algorithm were introduced to establish a high−precision coal seam gas content prediction model.
Results Compared to the traditional multiple linear regression model, both the BP neural network and random forest models achieved notably higher prediction accuracy, with the random forest model performing even better.
Conclusions The Random Forest model is more suitable for predicting the coalbed methane content in the study area. Based on the model's prediction, the distribution range of gas content in the gas field is 4.84~21.83 m3/t, with an average of 11.63 m3/t. Spatially, the gas content of coal seam increases gradually from southeast to northwest, and its variation law is generally consistent with the buried depth pattern of coal seam. Vertically, with the increase of buried depth, the gas content of coal seam increases gradually, but the dispersion degree of gas content distribution increases.
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Key words:
- gas content /
- logging parameters /
- MIV /
- BP neural network /
- random forest
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表 1 2号煤层含气量与测井参数之间的相关系数
Table 1. The correlation coefficient between gas content and logging parameters of No.2 coal seam
参数 含气量 深度 DEN GR RD RS DT CAL CNL 含气量 1.00 深度 0.48 1.00 DEN 0.52 0.12 1.00 GR 0.03 0.11 0.30 1.00 RD −0.58 −0.21 −0.31 −0.11 1.00 RS −0.58 −0.20 −0.29 −0.02 0.93 1.00 DT −0.53 −0.24 −0.67 −0.37 0.34 0.33 1.00 CAL −0.06 −0.09 −0.24 −0.15 0.09 −0.03 0.32 1.00 CNL −0.12 −0.17 −0.20 −0.45 0.24 0.13 0.42 0.69 1.00 
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表 2 煤层含气量预测模型的预测精度评价指标
Table 2. Evaluation index of prediction accuracy for the coal seam gas content prediction model
模型指标 BP神经网络 随机森林 多元线性回归 训练集 测试集 训练集 测试集 训练集 测试集 R2 0.781 0.710 0.949 0.811 0.636 0.559 RMSE 1.718 2.213 1.071 1.584 2.238 2.383
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