In view of the problem that the traditional time series model cannot predict the gas yield of natural gas wells with a short production cycle, the affinity propagation algorithm based on machine learning is used to perform unsupervised clustering for natural gas wells and divide the well group. Combined with the geological and engineering parameters of the well group, principal component analysis is carried out to capture the key factors affecting the fluctuation of gas yield. Then, the maximum likelihood estimation method is used to solve the category of well group that the gas well belongs to, and the convolution neural network of training time for the production data of clustering center for the required category is used to predict the gas yield of the natural gas well in the short term. The results show that the yield prediction model of gas well based on machine learning has an average prediction error of 5.53%, and if compared with the long and short-term memory network (with an average error of 8.98%) and gated cyclic network (with an average error of 9.06%), the prediction error is smaller, indicating that this model can be applied for the yield prediction of carbonatite gas well with a relatively short development cycle. The research results are of great significance for the application research of machine learning in oil and gas reservoir development.
Pang Lansu
,
Wang Yang
,
Jiang Wei
,
Wang Yongsheng
,
Gao Guohai
,
Wang Xin
. Research on Yield Prediction of Carbonatite Gas Well with a Short Production Cycle Based on Machine Learning[J]. Special Oil & Gas Reservoirs, 2023
, 30(2)
: 134
-141
.
DOI: 10.3969/j.issn.1006-6535.2023.02.019
[1] ARPS J J.Analysis of decline curves[J].Petroleum Transactions,1945,160(1):228-247.
[2] FETKOVICH M J.A simplified approach to water influx calculations-finite aquifer system[J].Journal of Petroleum Technology,1971,23(7):814-823.
[3] BLASINGAME T A,MCCRAY T L,LEE W J.Decline curve analysis for variable pressure drop/variable flowrate systems[C].SPE21513-MS,1991:22-24.
[4] AGARWAL R G,GARDNER D C,KLEINSTEIBER S W,et al.Analyzing well production data using combined-type-curve and decline-curve analysis concepts[J].SPE Res Eval & Eng,1999,2(5):478-486.
[5] GREFF K,SRIVASTAVA R K,KOUTNIK J,et al.LSTM:a sear
ch space odyssey[J].IEEE Transactions on Neural Networks & Learning Systems,2016,99:1-11.
[6] HOCHREITER S,SCHMIDHUBER J.Long short-term memory[J].Neural Computation,1997,9(8):1735-1780.
[7] KYUNGHYUN C,BART V M,CAGLAR G,et al.Proceedings of the 2014 conference on empirical methods in natural language processing[C].Pennsylvania:Association for Computational Linguistics,2014:1724-1734.
[8] 谷建伟,周梅,李志涛,等.基于数据挖掘的长短期记忆网络模型油井产量预测方法[J].特种油气藏,2019,26(2):77-81.
GU Jianwei,ZHOU Mei,LI Zhitao,et al.Oil well production forecast with long-short term memory network model based on data mining[J].Special Oil & Gas Reservoirs,2019,26(2):77-81.
[9] LEE K,LIM J,YOON D,et al.Prediction of shale-gas production at duvernay formation using deep-learning algorithm[J].SPE Journal,2019,24(6):2423-2437.
[10] 梁潇,喻高明,辛显康,等.基于GRU循环神经网络的稠油油藏产量预测新方法[J].西安石油大学学报(自然科学版),2020,35(3):54-59.
LIANG Xiao,YU Gaoming,XIN Xiankang,et al.A new method for production prediction of heavy oil reservoirs based on GRU circulation neural network[J].Journal of Xi'an Shiyou University(Natural Science Edition),2020,35(3):54-59.
[11] LUO Shangui,DING Chao,CHENG Hongfei,et al.Estimated ultimate recovery prediction of fractured horizontal wells in tight oil reservoirs based on deep neural networks[J].Advances in Geo-Energy Research,2022,6(2):111-122.
[12] SADIQUE F,SENGUPTA S.Modeling and analyzing attacker behavior in IoT botnet using temporal convolution network[J].Computers and Security,2022,117:102714.
[13] FREY B J,DUECK D.Clustering by passing messages between data points[J].Science,2007,315(5814):972-976.
[14] 李春梅,彭才,韦柳阳,等.小尺度缝洞型碳酸盐岩储集体地震预测技术:以四川盆地台内GS18井区灯影组四段储层为例[J].断块油气田,2022,29(2):189-193.
LI Chunmei,PENG Cai,WEI Liuyang,et al.Seismic prediction technology of small scale fractured-cave carbonate reservoir: taking the intra-platform reservoir of the fourth Member of Dengying Formation in GS18 wellblock, Sichuan Basin as an example[J].Fault-Block Oil & Gas Field,2022,29(2):189-193.
[15] 韩珊,车明光,苏旺,等.四川盆地威远区块页岩气单井产量预测方法及应用[J].特种油气藏,2022,29(6):141-149.
HAN Shan,CHE Mingguang,SU Wang,et al.Prediction method and application of single shale gas well production in Weiyuan Block Sichuan Basin[J].Special Oil & Gas Reserviors,2022,29(6):141-149.
[16] 蔡珺君,彭先,李骞,等.四川盆地重点海相碳酸盐岩气藏产能评价现状及展望[J].断块油气田,2021,28(5):655-660.
CAI Junjun,PENG Xian,LI Qian,et al.Present situation and prospect of productivity evaluation of key marine carbonate gas reservoirs in Sichuan Basin[J].Fault-Block Oil & Gas Field,2021,28(5):655-660.
