Tight sandstone reservoirs have the characteristics of strong heterogeneity,poor physical properties,and difficulty in exploration and development.In order to find the high-brittleness section of tight sandstone reservoirs in the Weibei oilfield and fracture this kind of reservoirs,this paper proposes a method based on ANN (artificial neural network) model for shear wave prediction under the condition of lacking suitable brittleness prediction methods for tight sandstone reservoirs in the Weibei oilfield at present.The predicted value is highly consistent with the measured value,and the brittleness index of each well in the study area is calculated by the elastic parameter method further.For the purpose of improving the accuracy of the brittleness index predicted by this method,X-ray diffraction full-rock analysis of fewer wells in the study area is utilized,and it is concluded that quartz and carbonate rocks are the main brittle minerals of the Yanchang formation in the study area."(quartz+carbonate) content/ total minerals " are adopted to calculate the rock brittleness index and then improve the brittleness index predicted by the elastic parameter method.This technique which takes advantage of the balance between the mineral composition method and the elastic parameter method not only improves the prediction accuracy but also makes up for the lack of array acoustic logging and whole rock analysis data.This method was used to predict the brittleness of tight sandstone reservoirs in the WB2 well area of the Yanchang formation in the Weibei oilfield, and high-brittleness sections of WB52 and WB49 were further chosen to be fractured.It is shown that the production stimulation effect was obvious after fracturing,which is of great significance for guiding hydraulic fracturing.The method and process proposed in this paper have strong application and promotion value.
Zou C N, Zhu R K, Wu S T, et al. Types,haracteristics,genesis and prospects of conventional andunconventional hy drocarbon accumulations:Taking tight oil and tight gas in China an instance[J]. Acta Petrolei Sinica, 2012, 33(2):173-187.
Tian J T, Zhao C F, Zhang W, et al. Analysis of asymmetric hydraulic fracture for borehole microseismic monitoring[J]. Geophysical Prospecting for Petroleum, 2019, 58(4):563-571.
Liu Y, Fang W B, Li Z C, et al. Brittleness prediction and application based on pre-stack seismic inversion[J]. Geophysical Prospecting for Petroleum, 2016, 55(3):425-432.
Zhang X Y, Du Q Z, Ma Z G, et al. Brittleness characteristics of anisotropic shale reservoirs[J]. Geophysical and Geochemical Exploration, 2016, 40(3):541-549.
Ma N, Lin Z L, Hu H F, et al. A seismic-based prediction method for fracture pressure in a shale formation[J]. Geophysical Prospecting for Petroleum, 2019, 58(6):926-934.
[6]
Obert L, Duvall W I. Rock mechanics and the design of structures in rock[M]. Hoboken: Wiley, 1967.
Huang J P, Zhang Z S, Yang Z L, et al. Quantitative prediction of mineral component content and brittleness index in tight rocks based on multivariate regression analysis[J]. Xinjiang Petroleum Geology, 2016, 37(3):346-351.
Ren Y, Cao H, Yao F C, et al. Brittleness and fracability prediction for tight oil reservoir in Jimsar Sag,Junggar Basin[J]. Oil Geophysical Prospecting, 2018, 53(3):511-519.
Zhang P, Xia X M, Cui H, et al. Tight oil reservoir brittleness index prediction based on petrophysical experiments:A case from Yuehui 101 area of Qaidam Basin[J]. Xinjiang Petroleum Geology, 2019, 40(5):615-623.
Shi D H, Zhang B, He J T, et al. Feasibility evaluation of volume fracturing of Chang 7 tight sandstone reservoir in Ordos Basin[J]. Journal of Xi'an Shiyou University:Natural Science Edition, 2014, 29(1):53-55.
Xu J, Liu K Y, Wu Q Z. Evaluation and prediction of shale brittleness in the Jiaoshiba area[J]. Geophysical Prospecting for Petroleum, 2019, 58(3):453-460.
Liu Y J, Li S J, Wang Y G, et al. Reservoir prediction based on shear wave in Sulige Gas Field[J]. Oil Geophysical Prospecting, 2016, 51(1):165-172.
[15]
Castagna J P, Batzle M L, Eastwood R L. Relationships between compressional-wave and shear-wave velocities in clastic silicate rocks[J]. Geophysics, 1985, 50(4):571-581.
doi: 10.1190/1.1441933
[16]
Xu S Y, White R E. A physical model for shear-wave velocity prediction[J]. Geophysical Prospecting, 1996, 44(4):687-717.
doi: 10.1111/gpr.1996.44.issue-4
[17]
LEE M W. A simple method of predicting S-wave velocity[J]. Geophysics, 2006, 71(6):F161-F164.
doi: 10.1190/1.2357833
Liu C, Qiao H Q, Guo Z Q, et al. Shale pore structure inversion and shear wave velocity prediction based on particle swarm optimization(pso) algorithm[J]. Progress in Geophysics, 2017, 32(2):689-695.
Liu Q, Yin X Y, Li C. Rock elastic modulus estimation for tight sandstone reservoirs with disconnected pores[J]. Geophysical Prospecting for Petroleum, 2015, 54(6):635-642.
Zhang B M, Liu Z S, Liu J Z, et al. A new S-wave velocity estimation method for organic-enriched shale[J]. Geophysical Prospecting for Petroleum, 2018, 57(5):658-667.
[21]
Dutta S, Gupta J P. PVT correlations for Indian crude using artificial neural networks[J]. Journal of Petroleum Science and Engineering, 2010, 72(1):93-109.
doi: 10.1016/j.petrol.2010.03.007
[22]
Cheng C S. A multi-yaler neural network model for detecting changes in the process mean[J]. Computers and Industrial Engineering, 1995, 28(1):51-61.
doi: 10.1016/0360-8352(94)00024-H
[23]
史忠科. 神经网络控制理论[M]. 西安: 西北工业大学出版社, 1997.
[23]
Shi Z K. Neural network control theory [M]. Xi'an: Northwestern Polytechnical University Press, 1997.
Zhou X Q, Zhang Z S, Zhang C M, et al. A new brittleness evaluation method for tight sandstonereservoir based on mineral compositions and diagenesis:A case study of a certain block in the northeastern Ordos Basin[J]. Petroleum Geology and Recovery Efficiency, 2017, 24(5):10-16.
Xu L, Shi Y M, Xu C S, et al. Influences of feldspars on the storage and permeability conditions in tight oil reservoirs:A case study of Chang-6 Oil Layer Group,Ordos Basin[J]. Petroleum Exploration and Development, 2013, 40(4):448-454.
[27]
Rickman R, Mullen M J, Petre J E, et al. Apractical use of shale petrophysics for stimulation designoptimization:All shale plays are not clones of the Barnett Shale[C]// SPE Technical Conference and Exhibition,Society of Petroleum Engineers, 2008, 48(3):536-567.