Data Driven Approach to Infer Inter-well Connectivity among Production Wells in an Oil Synthetic Reservoir

Document Type : Research Paper

Authors

School of Chemical Engineering, Oil and gas, Iran University of Science and Technology, Tehran, Iran

Abstract

Optimal reservoir management, modeling and production, depend on understanding the connectivity between production wells in an oil reservoir. Using numerical simulation and a reservoir permeability map can clear this feature, however, it is a time-consuming method and the presence of uncertainty in the input data of simulators leads to employ other methods to understand the feature. Having new sensors that are permanently placed in the wellbore, large amounts of production data (production rate and bottom hole pressure) are available. In recent years, intelligent data-driven techniques have been used to work with the large amount of data obtained from production wells. This paper uses a data-driven approach based on detecting important production well events during its production life. Important well events are divided into three categories: (1) increasing, (2) decreasing, and (3) no flow (shut in the well by operator). These events are identified using derivative and slope of the production figures and some limiting factors. Afterwards, the algorithm has to find the related events between the production wells, and based on the importance of the events, the inter-well connectivity among production wells is determined which it is shown as a connectivity map. A synthetic reservoir was first developed in the Eclipse simulation software, and the three high-permeability streaks were considered between three well pairs. Next, its production data were used as the data-driven model’s input, which it is programmed in MATLAB software, in order to identify the three high-permeability streaks via the data-driven model.
 

Keywords


[1]. Chen X, Zhang D, Wang L, Jia N, Kang Z, Zhang Y, Hu S (2016) Design automation for interwell connectivity estimation in petroleum cyber-physical systems, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2, 36: 255-264.##
[2]. Cao M, Shang F (2013) Study on inferring interwell connectivity of injection-production system based on decision tree, 2013 10th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), 1010-1014. ##
[3]. Wang Y. (2018) Inferring interwell connectivity by statistical diagnostic tools and signal processing methods, M.Sc thesis, University of Oklahoma, USA. ##
[4]. Tian C, Horne R (2016) Inferring interwell connectivity using production data, SPE Annual Technical Conference and Exhibition, OnePetro. ##
[5]. Heffer K, Fox R, McGill C, Koutsabeloulis N (1997) Novel techniques show links between reservoir flow directionality, earth stress, fault structure and geomechanical changes in mature waterfloods, SPE Journal, 2, 2:91-98. ##
[6]. Soeriawinata T, Kelkar M (1999) Reservoir management using production data, SPE mid-continent operations symposium, OnePetro. ##
[7]. Albertoni A, Lake L (2003) Inferring interwell connectivity only from well-rate fluctuations in waterfloods, SPE Reservoir Evaluation and Engineering, 1, 6: 6-16. ##
[8]. Yousef A, Gentil P, Jensen J, Lake L (2005) A capacitance model to infer interwell connectivity from production and injection rate fluctuations, SPE Annual Technical Conference and Exhibition, SPE Reservoir Evaluation and Engineering, 9, 06: 630-646. ##
[9]. Sayarpour M, Kabir C, Lake L (2009) Field applications of capacitance-resistance models in waterfloods, SPE Reservoir Evaluation and engineering, 6, 12: 853-864. ##
[10]. Ballin PR, Solano R, Hird KB, Volz RF (2002) New reservoir dynamic connectivity measurement for efficient well placement strategy analysis under depletion, SPE Annual Technical Conference and Exhibition. ##
[11]. Panda MN, Chopra AK (1998) An integrated approach to estimate well interactions, SPE India Oil and Gas Conference and Exhibition, OnePetro. ##
[12]. Artun E (2017) Characterizing interwell connectivity in waterflooded reservoirs using data-driven and reduced-physics models: a comparative study, Neural Computing and Applications, 7, 28: 1729-1743. ##
[13]. Killough J E (1995) Ninth SPE comparative solution project: a reexamination of black-oil simulation, SPE Reservoir Simulation Symposium. ##