国家自然科学基金(61922073,61672483,U1605251)
[1] Lin N T, Zhang D, Zhang K, et al. Predicting distribution of hydrocarbon reservoirs with seismic data based on learning of the small-sample convolution neural network. Chinese J Geophys, 2018, 61: 4110--4125. Google Scholar
[2] Zhang Y B, Liu M Y, Xiang J L. Discriminating method of the lateral sealing oil and gas by the transporting fault in hydrocarbon accumulation period and its application. Petrol Geol Oilfield Dev Daqing, 2019, 38: 33--40. Google Scholar
[3] 张晏奇. 测井资料交会图法在火山岩岩性识别中的应用探讨. 西部探矿工程, 2019, 31: 53--54. Google Scholar
[4] Li X Y, Qiao H W, Zhang J K, et al. Linear relationship between mineral content and porosity of Chang 6 reservoir in Jiyuan area, Ordos Basin. Lithologic Reservoirs, 2019, 31: 66--74. Google Scholar
[5] Neiman I B. Volume models of the action of cylindrical-charge explosion in rock. Sov Min Sci, 1987, 22: 44--52. Google Scholar
[6] Zhu Y, Xie J, Yang W. Method for improving history matching precision of reservoir numerical simulation. Pet Exploration Dev, 2008, 35: 225-229 CrossRef Google Scholar
[7] 刘之的. 随钻测井响应反演方法及应用研究. 博士学位论文. 成都: 西南石油大学, 2006. Google Scholar
[8] Mao Z Q, Li J F. Method and models for productivity prediction of hydrocarbon reservoirs. Acta Petrolei Sin, 2000, 21: 58--61. Google Scholar
[9] Wu P Y, Jain V, Kulkarni M S, et al. Machine learning-based method for automated well-log processing and interpretation. In: SEG Technical Program Expanded Abstracts 2018. Tulsa: Society of Exploration Geophysicists, 2018. 2041--2045. Google Scholar
[10] Gupta A, Soumya U. Well log interpretation using deep learning neural networks. In: Proceedigs of International Petroleum Technology Conference, 2020. Google Scholar
[11] Haldorsen J B U, Johnson D L, Plona T, et al. Borehole acoustic waves. Oilfield Rev, 2006, 18: 34--43. Google Scholar
[12] Walsh D, Turner P, Grunewald E. A Small-Diameter NMR Logging Tool for Groundwater Investigations. Groundwater, 2013, 51: 914-926 CrossRef Google Scholar
[13] Yuan J D, Wang Z H. Review of time series representation and classification techniques. Comput Sci, 2015, 42: 1--7. Google Scholar
[14] Ye L X, Keogh E J. Time series shapelets: a new primitive for data mining. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2009. 947--956. Google Scholar
[15] Ye L, Keogh E. Time series shapelets: a novel technique that allows accurate, interpretable and fast classification. Data Min Knowl Disc, 2011, 22: 149-182 CrossRef Google Scholar
[16] Chan K P, Fu A W C. Efficient time series matching by wavelets. In: Proceedings the 15th International Conference on Data Engineering, 1999. 126--133. Google Scholar
[17] Ding H, Trajcevski G, Scheuermann P. Querying and mining of time series data. Proc VLDB Endow, 2008, 1: 1542-1552 CrossRef Google Scholar
[18] Wang X, Mueen A, Ding H. Experimental comparison of representation methods and distance measures for time series data. Data Min Knowl Disc, 2013, 26: 275-309 CrossRef Google Scholar
[19] Zhong S, Ghosh J. HMMs and coupled HMMs for multi-channel EEG classification. In: Proceedings of International Joint Conference on Neural Networks, 2002. 1154--1159. Google Scholar
[20] Povinelli R J, Johnson M T, Lindgren A C. Time series classification using Gaussian mixture models of reconstructed phase spaces. IEEE Trans Knowl Data Eng, 2004, 16: 779-783 CrossRef Google Scholar
[21] Gao R, Wang S, Zheng F. Dynamic full Bayesian ensemble classifiers for small time series. Sci Sin-Inf, 2017, 47: 1445-1463 CrossRef Google Scholar
[22] 武天鸿, 翁小清, 单中南. 基于符号表示的时间序列分类综述. 河北省科学院学报, 2019, 36: 11--20. Google Scholar
[23] Wang J, Liu P, She M F H. Bag-of-words representation for biomedical time series classification. BioMed Signal Processing Control, 2013, 8: 634-644 CrossRef Google Scholar
[24] Lin J, Khade R, Li Y. Rotation-invariant similarity in time series using bag-of-patterns representation. J Intell Inf Syst, 2012, 39: 287-315 CrossRef Google Scholar
[25] Schäfer P, Högqvist M. SFA: a symbolic fourier approximation and index for similarity search in high dimensional datasets. In: Proceedings of the 15th International Conference on Extending Database Technology, 2012. 516--527. Google Scholar
[26] Sch?fer P. The BOSS is concerned with time series classification in the presence of noise. Data Min Knowl Disc, 2015, 29: 1505-1530 CrossRef Google Scholar
[27] Karim F, Majumdar S, Darabi H. Multivariate LSTM-FCNs for time series classification. Neural Networks, 2019, 116: 237-245 CrossRef Google Scholar
[28] Zheng Y, Liu Q, Chen E. Exploiting multi-channels deep convolutional neural networks for multivariate time series classification. Front Comput Sci, 2016, 10: 96-112 CrossRef Google Scholar
[29] Zheng Y, Liu Q, Chen E H, et al. Time series classification using multi-channels deep convolutional neural networks. In: Proceedings of International Conference on Web-Age Information Management, 2014. 298--310. Google Scholar
[30] Karim F, Majumdar S, Darabi H. Insights Into LSTM Fully Convolutional Networks for Time Series Classification. IEEE Access, 2019, 7: 67718-67725 CrossRef Google Scholar
[31] Shang F H, Yuan Y, Wang C Z, et al. A logging data processing method of intelligent optimization logging interpretation model based on knowledge-base. Acta Petrolei Sin, 2015, 36: 1449--1456. Google Scholar
[32] Alaudah Y, Micha?owicz P, Alfarraj M. A machine-learning benchmark for facies classification. Interpretation, 2019, 7: SE175-SE187 CrossRef Google Scholar
[33] Zhang W, Stewart R. Using FWI and deep learning to characterize velocity anomalies in crosswell seismic data. In: SEG Technical Program Expanded Abstracts 2019. Tulsa: Society of Exploration Geophysicists, 2019. 2313--2317. Google Scholar
[34] Tong B, Klinkigt M, Iwayama M, et al. Learning to generate rock descriptions from multivariate well logs with hierarchical attention. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2017. 2031--2040. Google Scholar
[35] Hochreiter S, Schmidhuber J. Long Short-Term Memory. Neural Computation, 1997, 9: 1735-1780 CrossRef Google Scholar
[36] Zhao H K, Wu L K, Li Z, et al. Predicting the dynamics in internet finance based on deep neural network structure. J Comput Res Dev, 2019, 56: 1621--1631. Google Scholar
[37] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2015. 3431--3440. Google Scholar
[38] Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need. In: Proceedings of Advances in Neural Information Processing Systems, 2017. 5998--6008. Google Scholar
[39] Lin T Y, Goyal P, Girshick R, et al. Focal loss for dense object detection. In: Proceedings of IEEE International Conference on Computer Vision, 2017. 2980--2988. Google Scholar
[40] Lopez-Paz D, Bottou L, Schölkopf B, et al. Unifying distillation and privileged information. 2015,. arXiv Google Scholar
[41] Hinton G, Vinyals O, Dean J. Distilling the knowledge in a neural network. 2015,. arXiv Google Scholar
[42] Kingma D P, Ba J. Adam: a method for stochastic optimization. 2014,. arXiv Google Scholar
[43] Ye J, Chow J H, Chen J, et al. Stochastic gradient boosted distributed decision trees. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, 2009. 2061--2064. Google Scholar
Figure 1
(Color online) The sample of oil and gas reservoirs detection
Figure 2
Illustration of GKDMN model
Figure 3
(Color online) The model experimental results of the oil and gas detection in the different parameters on #9FAB2 block dataset (the horizontal axis is the values of parameter $\lambda$, and the vertical axis indicates the matric scores). (a) Precision; (b) recall; (c) $F1$-score
Figure 4
(Color online) The model experimental results of the oil and gas detection in the different parameters on #BF8A9 block dataset (the horizontal axis is the values of parameter $\lambda$, and the vertical axis indicates the matric scores). (a) Precision; (b) recall; (c) $F1$-score
Figure 5
(Color online) The model experimental results of the oil and gas detection over the different epochs. (a) $F1$ scores of #9FAB2 block; (b) $F1$ scores of #BF8A9 block (the horizontal axis is the number of epochs and the vertical axis indicates the $F1$ scores)
| Statistics | #9FAB2 | #BF8A9 |
| Number of total wells | 299 | 180 |
| Number of total samples | 749209 | 541717 |
| Number of reservoir classes | 7 | 6 |
| Number of wells in train set | 239 | 144 |
| Number of sensor features in train set | 21 | 12 |
| Number of wells in test set | 60 | 36 |
| Number of sensor features in test set | 5 | 5 |
| Method | #9FAB2 | #BF8A9 | ||||
| Precision | Recall | $F1$ | Precision | Recall | $F1$ | |
| GBDT | 0.5750 | 0.6579 | 0.5872 | 0.7099 | 0.7555 | 0.7289 |
| LSTM | 0.5565 | 0.6625 | 0.5779 | 0.7426 | 0.7655 | 0.7484 |
| FCN | 0.5758 | 0.6700 | 0.5812 | 0.7461 | 0.7509 | 0.7483 |
| LSTMFCN | 0.6104 | 0.6841 | 0.6069 | 0.7277 | 0.7951 | 0.7493 |
| ALSTMFCN | 0.6155 | 0.5896 | 0.6006 | 0.7305 | 0.7924 | 0.7546 |
| GMN-a | 0.6126 | 0.6863 | 0.6115 | 0.7380 | 0.7965 | 0.7609 |
| GMN | 0.6349 | 0.6995 | 0.6394 | 0.7411 | 0.8004 | 0.7640 |
| GKDMN-a | 0.6330 | 0.6892 | 0.6475 | | 0.7870 | 0.7686 |
| GKDMN | | | | 0.7488 | | |
Download PDF
