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A deep learning model for predicting the production of coalbed methane considering time, space, and geological features
- Release time:2023-02-08
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Journal:
Computers and GeosciencesAbstract:
煤层气 （CBM） 是一种高质量的清洁能源，准确预测煤层气的日天然气产量对于煤层气工程至关重要。然而，煤层气的生产过程是一个不稳定的动态，波动很大，很难通过传统的统计方法进行预测。本研究处理了一个考虑时间、空间和地质特征的深度学习模型 T-DGCN，用于预测复杂的长气体生产序列。T-DGCN 创新性地使用动态时间规整 （DTW） 测量井间地质特征的相似性，并将地质和空间特征融合在一起，在具有多个聚合的多层神经网络中动态校正权重矩阵。然后，该模型使用门控循环单元（GRU） 提取天然气生产的时间特征并预测每日天然气生产序列。使用山西省数据集的实验表明，T-DGCN 在短期生产预测中达到了 0.9298 的精度，高于基线模型。此外，DTW 在 T-DGCN 中计算的地质相似性显着提高了模型的性能。而T-DGCN在长期预测任务中仍能有较好的性能，准确率在0.9以上。本研究为调整煤层气开发方案和地球科学长时序列预测提供了理论指导新方法。 Coalbed methane (CBM) is high-quality clean energy and accurate prediction of daily gas production of CBM is critical for CBM engineering. However, the production process of CBM is a non-stable dynamic with significant fluctuation, and it is hard to predict by traditional statistical methods. This study processes a deep learning model T-DGCN considering time, space, and geological features for predicting complex long gas production sequences. T-DGCN innovatively measures the similarity of geological features between wells with Dynamic Time Warping (DTW), and merges geological and spatial features to dynamically correct the weight matrix in a multilayer neural network with multiple aggregations. Then, the model uses the Gated Recurrent Unit (GRU) to extract the temporal features of gas production and predict the daily gas production sequence. The experiments with the data set from Shanxi Province showed that T-DGCN achieves an accuracy of 0.9298 in short-term production prediction, which is higher than the baseline models. In addition, the geological similarity calculated by DTW in T-DGCN significantly improves the performance of the model. And T-DGCN can still have better performance in long-term prediction tasks with accuracy above 0.9. This study provides a new method for the theoretical guidance for adjusting development schemes of CBM and the prediction of long-time series in geoscience.Indexed by:
Journal paperDocument Code:
105312Volume:
173Translation or Not:
noDate of Publication:
2023-02-08Included Journals:
SCILinks to published journals:
https://doi.org/10.1016/j.cageo.2023.105312
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