Analytical Determination of Interwell Connectivity Based on Interwell Influence

Jiangru Yuan; Xingjie Zeng; Haiyun Wu; Weishan Zhang; Jiehan Zhou; Bingyang Chen

doi:10.26599/TST.2020.9010039

Tsinghua Science and Technology 2021, 26(6): 813-820 https://doi.org/10.26599/TST.2020.9010039

Open Access | Issue | Published: 09 June 2021

Analytical Determination of Interwell Connectivity Based on Interwell Influence

Show Author's Information Hide Author's Information Jiangru Yuan, Xingjie Zeng(

), Haiyun Wu, Weishan Zhang, Jiehan Zhou, Bingyang Chen

Research Institute of Petroleum Exploration and Development, Beijing 100083, China

College of Computer Science and Technology, China University of Petroleum, Qingdao 266580, China

Department of Computer and Electronic Engineering, University of Oulu, Oulu FI-90014, Finland

Keywords:

Particle Swarm Optimization (PSO), interwell connectivity, interwell influence, CatBoost

Cite this article:

Yuan J, Zeng X, Wu H, et al. Analytical Determination of Interwell Connectivity Based on Interwell Influence. Tsinghua Science and Technology, 2021, 26(6): 813-820. https://doi.org/10.26599/TST.2020.9010039

Download citation

EndNote(RIS)

BibTeX

517

Views

Downloads

Citations

Crossref

WoS

Scopus

CSCD

Abstract Full text About this article

Abstract

Interwell connectivity, an important element in reservoir characterization, especially for water flooding, is used to make decisions for better oil production. The existing methods in literature directly use related data of wells to infer interwell connectivity, but they ignore the influence between different wells. The connection of one well to more than two wells (as is often true in the oil field well pattern) will impact the accuracy of the connectivity analysis. To address this challenge, this paper proposes the Particle Swarm Optimization-based CatBoost for Interwell Connectivity (PSOC4IC) based on relative features to analyze interwell connectivity with the combination of joint mutual information maximization-based denoising sparse autoencoder for inter-feature construction and extraction and PSO-based CatBoost (PSO-CatBoost) for connectivity prediction with high-dimensional noise data. The experimental results show that the PSOC4IC improves analysis accuracy.

Full text

Abstract

Full text

Outline

About this article

Analytical Determination of Interwell Connectivity Based on Interwell Influence

Show Author's information Hide Author's Information Jiangru Yuan, Xingjie Zeng(

), Haiyun Wu, Weishan Zhang, Jiehan Zhou, Bingyang Chen

Research Institute of Petroleum Exploration and Development, Beijing 100083, China

College of Computer Science and Technology, China University of Petroleum, Qingdao 266580, China

Department of Computer and Electronic Engineering, University of Oulu, Oulu FI-90014, Finland

Abstract

Keywords: Particle Swarm Optimization (PSO), interwell connectivity, interwell influence, CatBoost

References(16)

[1]

Y. Zhang, B. Wu, Y. Liu, and J. Lv, Local community detection based on network motifs, Tsinghua Science and Technology, vol. 24, no. 6, pp. 716-727, 2019.

DOI Google Scholar

[2]

A. Belhassena and H. Wang, Trajectory big data processing based on frequent activity, Tsinghua Science and Technology, vol. 24, no. 3, pp. 317-332, 2019.

DOI Google Scholar

[3]

W. Jiang, J. Lin, H. Wang, and S. Zou, Hybrid semantic service matchmaking method based on a random forest, Tsinghua Science and Technology, vol. 25, no. 6, pp. 798-812, 2020.

DOI Google Scholar

[4]

Z. C. Yi, A new method of interwell connectivity evaluation for water flooding reservoirs using injection and production data, (in Chinese), Sino-Global Energy, vol. 24, no. 1, pp. 40-47, 2019.

Google Scholar

[5]

H. B. Cheng, V. Vyatkin, E. Osipov, P. Zeng, and H. B. Yu, LSTM based EFAST global sensitivity analysis for interwell connectivity evaluation using injection and production fluctuation data, IEEE Access, vol. 8, pp. 67 289-67 299, 2020.

DOI Google Scholar

[6]

H. B. Cheng, X. N. Han, P. Zeng, H. B. Yu, E. Osipov, and V. Vyatkin, Ann based interwell connectivity analysis in cyber-physical petroleum systems, in Proc. 2019 IEEE 17th Int. Conf. on Industrial Informatics (INDIN), Helsinki, Finland, 2020.

DOI

[7]

A. A. Yousef, P. H. Gentil, J. L. Jensen, and L. W. Lake, A capacitance model to infer interwell connectivity from production and injection rate fluctuations, SPE Res. Eval. Eng., vol. 9, no. 6, pp. 630-646, 2006.

DOI Google Scholar

[8]

J. M. Pang, Z. H. Pang, Y. L. Kong, L. Lu, Y. C. Wang, and S. F. Wang, Interwell connectivity in a karstic geothermal reservoir through tracer tests, (in Chinese), Chin. J. Geol., vol. 49, no. 3, pp. 915-923, 2014.

Google Scholar

[9]

X. Zhai, T. L. Wen, and S. Matringe, Production optimization in waterfloods with a new approach of inter-well connectivity modeling, in Proc. SPE Asia Pacific Oil & Gas Conf. and Exhibition, Perth, Austria, 2016.

DOI

[10]

M. Mirzayev and J. L. Jensen, Interwell connectivity analysis in a low-permeability formation using a modified capacitance model with application to the east pembina field, cardium formation, alberta, Oil Gas Sci. Technol.- Rev. IFP Energ. Nouv., vol. 74, p. 26, 2019.

DOI Google Scholar

[11]

X. D. Chen, D. M. Zhang, L. Z. Wang, N. Jia, Z. J. Kang, Z. Yun, and S. Y. Hu, Design automation for interwell connectivity estimation in petroleum cyber-physical systems, IEEE Trans. Comput.-Aided Design Integr. Circuits Syst., vol. 36, no. 2, pp. 255-264, 2017.

DOI Google Scholar

[12]

S. Liu, L. Cao, H. He, T. Yang, and D. S. Zhou, An improved interwell connectivity model to obtain interwell connectivity information by using complex well data, Simulation, .

DOI Google Scholar

[13]

W. Wang, J. Yao, Y. Li, and A. M. Lv, Research on carbonate reservoir interwell connectivity based on a modified diffusivity filter model, Open Phys., vol. 15, no. 1, p. 34, 2017.

DOI Google Scholar

[14]

R. C. Eberhart and Y. H. Shi, Particle swarm optimization: Developments, applications and resources, in Proc. 2001 Congress on Evolutionary Computation, Seoul, Republic of Korea, 2002.

[15]

A. V. Dorogush, V. Ershov, and A. Gulin, Catboost: Gradient boosting with categorical features support, arXiv preprint arXiv: 1810.11363, 2018.

Google Scholar

[16]

M. Bennasar, Y. Hicks, and R. Setchi, Feature selection using joint mutual information maximization, Expert Systems with Applications, vol. 42, no. 22, pp. 8520-8532, 2015.

DOI Google Scholar

About this article

Publication history

Acknowledgements

Rights and permissions

Publication history

Received: 13 August 2020

Accepted: 14 September 2020

Published: 09 June 2021

Issue date: December 2021

Copyright

Acknowledgements

This work was supported by the Ministry of Industry and Information Technology’s 2018 Big Data Industry Development Pilot Demonstration Project "Demonstration Project of Oil and Gas Exploration and Development Innovation and Efficiency Enhancement Based on the Application of Big Data" (Letter of the Ministry of Industry and Information Technology [2018] No. 339), the Ministry of Industry and Information Technology Demonstration Project Supporting Project "Petroleum Exploration and Development Big Data and Artificial Intelligence Key Technology" (No. 2018D-5010-16), the Innovation Project of PetroChina Science and Technology Research Institute Co., Ltd. "Exploration and Research on Predicting the Remaining Oil Saturation of Each Layer under the Condition of Co-Injection by Applying Big Data Deep Learning Method" (No. 2017ycq02), the National Key R&D Program (No. 2018YFE0116700), and the Shandong Provincial Natural Science Foundation (No. ZR2019MF049, Parallel Data-Driven Fault Prediction under Online-Offline Combined Cloud Computing Environment).

Rights and permissions

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).