Research on Neural Network Prediction Model of Reservoir Particle Size

doi:10.11885/j.issn.1674-5086.2014.01.09.02

Abstract

Abstract:

According to researches at home and abroad，sand control design is based on reservoir particle size characteristic
value. LDA and SA are the conventional methods used to analyze particle size distributions. Both methods requires data through
core particle size testing. But sand control design can only use test well data，because no core at actual producing position can
be used when sand control measure is established，which can result in major errors. This article elaborates the relevance about
median grain size and gamma ray logging or density logging through researches on reservoir particle size and variety of log
curve response relation. And then through establishing sample pool of gamma ray logging or density logging and characteristic
value，and by neural network technology we trained learning network satisfing engineering requirements. Then the particle
size longitudinal distribution profile can be established according to development well logging data. This profile supplied data
basis for sand control layering design. At present，this method has been successfully used in several Chinese offshore oil field
in sand control optimization with errors below 10%.

Key words: layered sand control, characteristic value of particle size, neural network, gamma and density logging, sample
pool

CLC Number:

TE257

WANG Lihua1*, LOU Yishan1, MA Xiaoyong2, CHENG Fushan1, CHEN Yu3. Research on Neural Network Prediction Model of Reservoir Particle Size[J]. 西南石油大学学报(自然科学版), DOI: 10.11885/j.issn.1674-5086.2014.01.09.02.

0
/ / Recommend

Add to citation manager EndNote|Ris|BibTeX

URL: http://journal15.magtechjournal.com/Jwk_xnzk/EN/10.11885/j.issn.1674-5086.2014.01.09.02

http://journal15.magtechjournal.com/Jwk_xnzk/EN/Y2016/V38/I1/53

References

[1] 何生厚，张琪. 油气井防砂理论及其应用[M]. 北京：中
国石化出版社，2003.
[2] 万仁溥. 现代完井工程[M]. 第3 版. 北京：石油工业出
版社，2000.
[3] 杨斌. 神经网络及其在石油测井中的应用[M]. 北京：
石油工业出版社，2005.
[4] HURST A. Natural gamma-ray spectrometry in hydrocarbon-
bearing sandstones from the Norwegian Continental
Shelf[J]. Geological Society，London，Special Publications，
1990，48（1）：211 222.
[5] OYENEYIN M B，FAGA A T. Formation-grain-size prediction
whilst drilling：A key factor in intelligent sand control
completions[C]. SPE 56626，1999.
[6] FAGA A T，OYENEYIN B M. Application of neural networks
for improved gravel-pack design[C]. SPE 58722，
2000.
[7] FAGA A T，OYENEYIN B M. Effects of diagenesis
on neural-network grain-size prediction[C]. Society
of Petroleum Engineers：SPE Rocky Mountain Regional/
Low-Permeability Reservoirs Symposium and Exhibition，
2000.
[8] 邓金根，李萍，王利华，等. 渤海湾地区适度防砂
技术防砂方式优选[J]. 石油钻采工艺，2011，33（1）：
98 101.
DENG Jingen，LI Ping，WANG Lihua，et al. The optimization
of sand control method for moderate sand control
technique application in Bohai Bay[J]. Oil Drilling &
Production Technology，2011，33（1）：98 101.
[9] GILLESPIE G，DEEM C K，MALBREL C. Screen selection
for sand control based on laboratory tests[C]. SPE
Asia Pacific Oil and Gas Conference and Exhibition. Society
of Petroleum Engineers，2000.
[10] SAUCIER R J. Considerations in gravel pack design[
J]. Journal of Petroleum Technology，1974，26（2）：
205 212.
[11] 王利华，邓金根，周建良，等. 适度出砂开采标准金属
网布优质筛管防砂参数设计实验研究[J]. 中国海上油
气，2011，23（2）：107 110.
WANG Lihua，DENG Jingen，ZHOU Jianliang，et al. Experimental
study on premium screen mesh opening design
for reasonable sand control[J]. China Offshore Oil And
Gas，2011，2011，23（2）：107 110.
[12] 邓金根，王利华，李萍，等. 中等胶结储层气井出砂临
界流量实验测试技术[J]. 西南石油大学学报（自然科学
版），2011，33（3）：178 182.
DENG Jingen，WANG Lihua，LI Ping，et al. The sand
production critical flow test technology of gas well in
moderate cemented formation[J]. Journal of Southwest
Petroleum University（Science & Technology Edition），
2011，33（3）：178 182.

[1]	HUANG Xuri, DAI Yue, XU Yungui, TANG Jing. Seismic Inversion Experiments Based on Deep Learning Algorithm Using Different Datasets [J]. Journal of Southwest Petroleum University(Science & Technology Edition), 2020, 42(6): 16-25.
[2]	LIU Zhaonian, ZHAO Ying, SUN Ting. Data-driven Drilling Acceleration in Bohai XX Block [J]. Journal of Southwest Petroleum University(Science & Technology Edition), 2020, 42(6): 35-41.
[3]	TAN Chaodong, HE Jiayuan, ZHOU Tong, LIU Jiankang, SONG Wenrong. A Study on the Optimization of Fracturing Operation Parameters Based on PCA-BNN [J]. Journal of Southwest Petroleum University(Science & Technology Edition), 2020, 42(6): 56-62.
[4]	LIU Haohan, YAN Yongqin, MIN Lingyuan, YUE Ping, YIN Yanling. Prediction of Annual Increase of Oil Production Based on GM (1, 1)Neural Network Combined Optimization [J]. Journal of Southwest Petroleum University(Science & Technology Edition), 2020, 42(6): 89-96.
[5]	WANG Bing, ZHENG Yamei, CHEN Maoke, GAO Lingyun. Question Answering System for Drilling Safety Based on Tri-BiLSTM-CNN [J]. Journal of Southwest Petroleum University(Science & Technology Edition), 2020, 42(6): 157-164.
[6]	LIU Yonghui, LUO Chengcheng, LIU Tong, REN Guirong, WANG Zhongwu. Prediction of Gas-liquid Two-phase Flow Patterns in Horizontal Gas Wells [J]. 西南石油大学学报(自然科学版), 2019, 41(3): 107-112.
[7]	LI Jian, LI Ke, WANG Bing. An Evaluation Model of Drilling Safety Based on Combined Rough Set and Neural Network [J]. 西南石油大学学报(自然科学版), 2017, 39(5): 120-128.
[8]	MO Li1*, WANG Jun1, WANG Jun2, WANG Luyou1. A Rotary Control Head Bearing Temperature Prediction Model Based#br# on GA-BP Algorithm in Underbalanced Drilling [J]. 西南石油大学学报(自然科学版), 2016, 38(1): 164-169.
[9]	Zhang Zhidong1，2*, Ai Zhijiu1, Zheng Wei3, Li Ben4, Zhong Gongxiang1. Study on Fault Diagnosis Technology for Fluid End of Drilling Pump [J]. 西南石油大学学报(自然科学版), 2015, 37(5): 167-173.
[10]	Hou Dali1, Sun Lei1, Pan Yi1, Qin Shanyu1, Dong Weijun2. Predicting Water Content of High CO2 Content Natural Gas by Artificial Neural Network [J]. 西南石油大学学报(自然科学版), 2013, 35(4): 121-125.
[11]	Sun Baocai1, Wu Jianwen1, Li Lei2, She Zhigang3. Prediction of Remaining Strength of Corroded Oil and Gas Pipeline Based on Improved GA-BP Algorithm [J]. 西南石油大学学报(自然科学版), 2013, 35(3): 160-167.
[12]	WU Qiu-bo;WU Yuan WANG Yun-cheng . RESERVOIR PARAMETER PREDICTION BY MULTI-ATTRIBUTE BASED ON HYBRIDLEARNING ALGORITHM OF FEED-FORWARD NETWORK [J]. 西南石油大学学报(自然科学版), 2011, 33(2): 68-72.
[13]	HE Yi;ZHANG Jin;LIU Huai-shan. STUDY ON THE APPLICATION OF ITERATIVE INVERSION OF SURFACE WAVE BASED ON ARTIFICIAL NEURAL NETWORK [J]. 西南石油大学学报(自然科学版), 2010, 32 (1): 40-44.
[14]	LANG Xiao-ling;PENG Shi-mi;KANG Hong-quan;ZHANG Feng-hong. PREDICT FAN DELTA SAND BODY BY USING MULTIATTRIBUTES VOLUME CLASSIFICATION TECHNOLOGY [J]. 西南石油大学学报(自然科学版), 2010, 32 (1): 65-66.
[15]	ZENG Fan-hui LIU Lin WANG Wen-yao WANG Xin-wen . THE WELL AND LAYER SELECTION IN FRACTURING IN ULYASTAI SAG [J]. 西南石油大学学报(自然科学版), 2009, 31(5): 105-108.