ogst
Home All issues Volume 76 (2021) Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles, 76 (2021) 82 References
Open Access
Issue
Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles
Volume 76, 2021
Article Number 82
Number of page(s) 21
DOI https://doi.org/10.2516/ogst/2021061
Published online 24 December 2021
  • Hu W. R. (2008) Necessity and feasibility of PetroChina mature field redevelopment, Pet. Explor. Dev. 35, 1, 1–5. [CrossRef] [Google Scholar]
  • Al-Qenae A., Chetri H., Kumar P.R., Orjuela J. (2018) Tracking the performance of strategically significant EOR Pilot: Zooming into inter-well connectivity, in: Abu Dhabi International Petroleum Exhibition & Conference, OnePetro [Google Scholar]
  • Chen P., Selveindran A., Kumar C., Saloma Y., Bose S., Balasubramanian S., Thakur G. (2019) CO2-EOR and carbon storage in Indian oilfields: from laboratory study to pilot design, in: SPE Western Regional Meeting, OnePetro. [Google Scholar]
  • Teletzke G.F., Wattenbarger R.C., Wilkinson J.R. (2010) Enhanced oil recovery pilot testing best practices, SPE Reserv. Evaluation Eng. 13, 01, 143–154. [CrossRef] [Google Scholar]
  • Liu Z.X., Liang Y., Wang Q., Guo Y.J., Gao M., Wang Z.B., Liu W.L. (2020) Status and progress of worldwide EOR field applications, J. Pet. Sci. Eng. 193, 107449. [CrossRef] [Google Scholar]
  • Babadagli T. (2020) Philosophy of EOR, J. Pet. Sci. Eng. 188, 106930. [CrossRef] [Google Scholar]
  • Sandoval J.R., Pérez H., Maya G., Castro R., Muñoz E., Colmenares K., León J., Sánchez F., Villadiego D., Manrique E., Romero J., Izadi M. (2010) Dina Cretáceos Field chemical EOR: from screening to pilot design, in: SPE Latin American and Caribbean Petroleum Engineering Conference, OnePetro. [Google Scholar]
  • Chen X., Feng Q., Wu X., Zhao G. (2016) A pilot numerical simulation case study for chemical EOR feasibility evaluation, J. Pet. Explor. Prod. Technol. 6, 2, 297–307. [CrossRef] [Google Scholar]
  • Alfarge D., Wei M., Bai B., Alsaba M. (2018) Lessons learned from IOR pilots in Bakken formation by using numerical simulation, J. Pet. Sci. Eng. 171, 1–15. [CrossRef] [Google Scholar]
  • Taqi F., Ahmad K., Garcia J.G., Zhang I., Zijlstra E., Ayyad H., Sullivan M. (2019) Interference pressure transient test for permeability anisotropy evaluation in shallow unconsolidated reservoir undergoing EOR polymer flood pilot, in: SPE Kuwait Oil & Gas Show and Conference, OnePetro. [Google Scholar]
  • Chai C.F., Adamson G., Lo S.W., Agarwal B., Ritom S., Du K., Azizan N. (2011) St. Joseph Chemical EOR Pilot – a key de-risking step prior to offshore ASP full field implementation, in: SPE Enhanced Oil Recovery Conference, OnePetro. [Google Scholar]
  • Ozen O., Wahlheim T.A., Attia T., Barrios L., Bin Majid M.N., Wilkinson J. (2014) Dukhan field CO2 injection EOR pilot: Reservoir modeling and planning, in: International Petroleum Technology Conference, OnePetro. [Google Scholar]
  • Ali H.A., Musa T.A., Doroudi A. (2015) Chemical enhanced oil recovery pilot design for Heglig Main Field-Sudan, in: SPE Saudi Arabia Section Annual Technical Symposium and Exhibition, OnePetro. [Google Scholar]
  • Saniez J., VandeBeuque S., Ekpenyong D.E., Bastos N., Wantong P., Salley B., Al-Yafei A. (2012) State of the art of geoscience and reservoir integrated study for EOR CO2 Pilot Implementation: example of a Giant Carbonate Reservoir of Arabian Gulf UAE, in: Abu Dhabi International Petroleum Conference and Exhibition, OnePetro. [Google Scholar]
  • Al-Dhuwaihi A.S., Abdullah M.B., Tiwari S., Al-Murayri M.T., Al-Mayyan H., Shahin G.T., Shukla S. (2017) Fit-for-purpose chemical EOR ASP modeling strategy to guide pilot development decisions for a giant reservoir in North Kuwait, in: SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition, OnePetro. [Google Scholar]
  • Sharma S., Kamal D., Al-Maraghi E., AlMahrooqi S., Winkler M. (2016) Miscible gas EOR pilot design decisions driven by linking EOR performance parameters to uncertainties – a Kuwait Field Example, in: SPE EOR Conference at Oil and Gas West Asia, OnePetro. [Google Scholar]
  • Prasad D., Pandey A., Kumar M.S., Koduru N. (2014) Pilot to full-field polymer application in one of the largest onshore field in India, in: SPE Improved Oil Recovery Symposium, OnePetro. [Google Scholar]
  • Da Cruz P.S., Horne R.N., Deutsch C.V. (2004) The quality map: a tool for reservoir uncertainty quantification and decision making. SPE Reserv. Evaluation Eng. 7, 01, 6–14. [CrossRef] [Google Scholar]
  • Martini R.F., Schiozer D.J., Nakajima L. (2005) Use of quality maps in reservoir management, J. Braz. Soc. Mech. Sci. Eng. 27, 4, 463–468. [CrossRef] [Google Scholar]
  • da Cruz Schaefer B., Sampaio M.A. (2020) Efficient workflow for optimizing intelligent well completion using production parameters in real-time, Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles 75, 69. [Google Scholar]
  • James G., Witten D., Hastie T., Tibshirani R. (2017) An introduction to statistical learning with applications in R, Springer. [Google Scholar]
  • Gorunescu F. (2011) Data mining: concepts, models and techniques, Vol. 12, Springer Science & Business Media. [MathSciNet] [Google Scholar]
  • Mohaghegh S.D. (2020) Subsurface analytics: contribution of artificial intelligence and machine learning to reservoir engineering, reservoir modeling, and reservoir management, Pet. Explor. Dev. 47, 2, 225–228. [CrossRef] [Google Scholar]
  • Hu J.I.A., Lihui D.E.N.G. (2018) Water flooding flowing area identification for oil reservoirs based on the method of streamline clustering artificial intelligence, Pet. Explor. Dev. 45, 2, 328–335. [CrossRef] [Google Scholar]
  • Vaseghi F., Ahmadi M., Sharifi M., Vanhoucke M. (2021) Generalized Multi-Scale Stochastic Reservoir Opportunity Index for enhanced well placement optimization under uncertainty in green and brownfields, Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles 76, 41. [Google Scholar]
  • Borgelt C. (2013) Objective functions for fuzzy clustering, Comput. Intell. Intell. Data Anal. 3–16. [Google Scholar]
  • Xie X.L., Beni G. (1991) A validity measure for fuzzy clustering, IEEE Trans. Pattern Anal. Mach. Intell. 13, 8, 841–847. [CrossRef] [Google Scholar]
  • Subbalakshmi C., Krishna G.R., Rao S.K.M., Rao P.V. (2015) A method to find optimum number of clusters based on fuzzy silhouette on dynamic data set, Proc. Comp. Sci. 46, 346–353. [CrossRef] [Google Scholar]
  • Shannon C.E. (1948) A mathematical theory of communication, Bell Syst. Tech. J. 27, 3, 379–423. [Google Scholar]
  • Abellan A., Noetinger B. (2010) Optimizing subsurface field data acquisition using information theory, Math. Geosci. 42, 6, 603–630. [Google Scholar]
  • Zhang Y., Li P., Wang Y., Ma P., Su X. (2013) Multiattribute decision making based on entropy under interval-valued intuitionistic fuzzy environment, Math Prob. Eng. 2013, 526871. [Google Scholar]
  • Chaudhry A. (2004) Oil well testing handbook, Elsevier. [Google Scholar]
  • Pyrcz M.J., Deutsch C.V. (2014) Geostatistical reservoir modeling, Oxford University Press. [Google Scholar]
  • Saaty T.L., Peniwati K. (2013) Group decision making: drawing out and reconciling differences, RWS Publications. [Google Scholar]
  • Samad A.M., Hifni N.A., Ghazali R., Hashim K.A., Disa N.M., Mahmud S. (2012) A study on school location suitability using AHP in GIS approach, in: 2012 IEEE 8th International Colloquium on Signal Processing and its Applications, IEEE, pp. 393–399. [Google Scholar]
  • Li Q., Zhang J., Deng B., Chang J., Li H., Liu S., Xu X. (2011) Grey decision-making theory in the optimization of strata series recombination programs of high water-cut oilfields, Pet. Explor. Dev. 38, 4, 463–469. [CrossRef] [Google Scholar]
  • Cables E., Lamata M.T., Verdegay J.L. (2016) RIM-reference ideal method in multicriteria decision making, Inform. Sci. 337, 1–10. [CrossRef] [Google Scholar]
  • Maschio C., Schiozer D.J. (2019) Integration of geostatistical realizations in data assimilation and reduction of uncertainty process using genetic algorithm combined with multi-start simulated annealing, Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles 74, 73. [Google Scholar]
  • Alinezhad A., Amini A. (2011) Sensitivity analysis of TOPSIS technique: the results of change in the weight of one attribute on the final ranking of alternatives, J. Optim. Indus. Eng. 4, 7, 23–28. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.