ogst
Home All issues Volume 75 (2020) Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles, 75 (2020) 84 References
Open Access
Issue
Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles
Volume 75, 2020
Article Number 84
Number of page(s) 16
DOI https://doi.org/10.2516/ogst/2020077
Published online 25 November 2020
  • Chen G., Zhao F., Wang J., Zheng H., Yan Y., Wang A., Li J., Hu Y. (2020) Regionalized multiple-point stochastic geological modeling: A case from braided delta sedimentary reservoirs in Qaidam Basin, NW China, Petrol. Explor. Develop. 42, 638–645. [Google Scholar]
  • Wu S., Zhang Y., Ringas J.E. (2006) Reservoir stochastic modeling constrained by quantitative geological conceptual patterns, Pet. Sci. 3, 28–33. [Google Scholar]
  • Cai J., Xu K., Zhu Y., Hu F., Li L. (2020) Prediction and analysis of net ecosystem carbon exchange based on gradient boosting regression and random forest, Appl. Energy 262, 114566. [CrossRef] [Google Scholar]
  • Hao M., Wang D., Deng C., He Z., Zhang J., Xue D., Ling X. (2006) 3D geological modeling and visualization of above-ground and under-ground integration-taking the Unicorn Island in Tianfu new area as an example, Earth Sci. Inf. 12, 465–474. [CrossRef] [Google Scholar]
  • Li S., Zhang C. (2010) Ranking realizations of stochastic modeling based on reservoir static geological parameters, Acta Pet. Sin. 31, 445–448. [Google Scholar]
  • Yang L., Hyde D., Grujic O., Scheidt C., Caers J. (2019) Assessing and visualizing uncertainty of 3D geological surfaces using level sets with stochastic motion, Comput. Geosci. 122, 54–67. [CrossRef] [Google Scholar]
  • Rezapour A., Ortega A., Sahimi M. (2019) Upscaling of geological models of oil reservoirs with unstructured grids using lifting-based graph wavelet transforms, Transp. Porous Media 127, 661–684. [CrossRef] [Google Scholar]
  • Ebong E.D., Akpan A.E., Ekwok S.E. (2020) Stochastic modelling of spatial variability of petrophysical properties in parts of the Niger Delta Basin, southern Nigeria, J. Pet. Explor. Prod. Technol. 10, 569–585. [CrossRef] [Google Scholar]
  • Chang Y., Bouzarkouna Z., Devegowda D. (2015) Multi-objective optimization for rapid and robust optimal oilfield development under geological uncertainty, Comput. Geosci. 19, 933–950. [CrossRef] [Google Scholar]
  • Schiozer D.J., de Souza dos Santos A.A., de Graça Santos S.M., von Hohendorff Filho J.C. (2019) Model-based decision analysis applied to petroleum field development and management, Oil Gas Sci. Technol. - Rev. IFP Energies nouvelles 74, 46. [CrossRef] [Google Scholar]
  • El Azzab D., Ghfir Y., Miftah A. (2019) Geological interpretation of the rifian foreland gravity anomalies and 3D modeling of their Hercynian granites (Northeastern Morocco), J. Afr. Earth Sci. 150, 584–594. [CrossRef] [Google Scholar]
  • Evren P.C., Mark L., Vitaliy O., Jeremie G., Mark J. (2018) Monte Carlo simulation for uncertainty estimation on structural data in implicit 3-D geological modeling, a guide for disturbance distribution selection and parameterization, Solid Earth 9, 385–402. [CrossRef] [Google Scholar]
  • Wang Y., Li T., Chen Y., Ma G. (2019) Numerical analysis of heat mining and geological carbon sequestration in supercritical CO2 circulating enhanced geothermal systems inlayed with complex discrete fracture networks, Energy 173, 92–108. [CrossRef] [Google Scholar]
  • Cao W., Shi J.Q., Si G., Durucan S., Korre A. (2018) Numerical modelling of microseismicity associated with longwall coal mining, Int. J. Coal Geol. 193, 30–45. [CrossRef] [Google Scholar]
  • Wang H., Wellmann J.F., Li Z., Wang X., Liang R. (2016) A segmentation approach for stochastic geological modeling using hidden Markov random fields, Math. Geosci. 49, 1–33. [Google Scholar]
  • Cao B., Luo X., Zhang L., Lei Y., Zhou J. (2020) Petrofacies prediction and 3-D geological model in tight gas sandstone reservoirs by integration of well logs and geostatistical modelling, Mar. Pet. Geol. 114, 104202. [CrossRef] [Google Scholar]
  • Liu K., Yin D., Sun Y. (2020) The mathematical model of stress sensitivities on tight reservoirs of different sedimentary rocks and its application, J. Pet. Sci. Eng. 193, 107372. [CrossRef] [Google Scholar]
  • Lillah M., Boisvert J.B. (2013) Stochastic distance based geological boundary modeling with curvilinear features, Math. Geosci. 45, 651–665. [CrossRef] [Google Scholar]
  • Burki M., Abu-Khadra A. (2019) Sequence stratigraphic approaches for reservoir modeling, Arshad area, Sirt Basin, Libya, J. Afr. Earth Sci. 151, 1–8. [CrossRef] [Google Scholar]
  • Qadri S.M.T., Islam M.A., Shalaby M.R. (2019) Three-dimensional petrophysical modelling and volumetric analysis to model the reservoir potential of the Kupe field, Taranaki basin, New Zealand, Nat. Resour. Res. 28, 369–392. [CrossRef] [Google Scholar]
  • Demyanov V., Backhouse L., Christie M. (2015) Geological feature selection in reservoir modelling and history matching with Multiple Kernel Learning, Comput. Geosci. 85, 16–25. [CrossRef] [Google Scholar]
  • Mohaghegh S.D., Gruic O., Zargari S., Dahaghi A., Bromhal G. (2012) Top-down, intelligent reservoir modelling of oil and gas producing shale reservoirs: Case studies, Int. J. Oil Gas Coal Technol. 5, 3–28. [CrossRef] [Google Scholar]
  • Li J., Yan K., Ren H., Sun Z. (2020) Detailed quantitative description of fluvial reservoirs: A case study of L6–3 Layer of Sandgroup 6 in the second member of Shahejie formation, Shengtuo Oilfield China, Adv. Geo-energy Res. 4, 43–53. [CrossRef] [Google Scholar]
  • Bouzarkouna Z., Ding D., Auger A. (2012) Well placement optimization with the covariance matrix adaptation evolution strategy and meta-models, Comput. Geosci. 16, 75–92. [CrossRef] [Google Scholar]
  • Grana D., Paparozzi E., Mancini S., Tarchiani C. (2013) Seismic driven probabilistic classification of reservoir facies for static reservoir modelling: A case history in the Barents Sea, Geophys. Prospect. 61, 613–629. [CrossRef] [Google Scholar]
  • Junling F., Fengde Z., Zhonghua T. (2017) Discrete fracture network modelling in a naturally fractured carbonate reservoir in the Jingbei oilfield, China, Energies 10, 183. [CrossRef] [Google Scholar]
  • Falivene O., Arbués P., Howell J., Munoz J.A., Fernandez O., Maezo M. (2006) Hierarchical geocellular facies modelling of a turbidite reservoir analogue from the Eocene of the Ainsa basin, NE Spain, Mar. Pet. Geol. 23, 679–701. [CrossRef] [Google Scholar]
  • Cabello P., Falivene O., Lopez-Blanco M., Howell-John A., Pau A., Emilio R. (2011) An outcrop-based comparison of facies modelling strategies in fan-delta reservoir analogues from the Eocene Sant Llorenc del Munt fan-delta (NE Spain), Pet. Geosci. 17, 65–90. [CrossRef] [Google Scholar]
  • Wang J., Zhang J., Xie J., Ding F. (2014) Initial gas full – component simulation experiment of Ban-876 underground gas storage, J. Nat. Gas Sci. Eng. 18, 131–136. [CrossRef] [Google Scholar]
  • Naji H., Hakimi M., Khalil M., Sharief F. (2010) Stratigraphy, deposition, and structural framework of the cretaceous (review) and 3D geological model of the lower cretaceous reservoirs, Masila oil field, Yemen, Arab. J. Geosci. 3, 221–248. [CrossRef] [Google Scholar]
  • Soleimani M., Shokri B. (2016) Intrinsic geological model generation for chromite pods in the Sabzevar ophiolite complex, NE Iran, Ore Geol. Rev. 78, 138–150. [CrossRef] [Google Scholar]
  • Liu H., Chen S., Hou M., He L. (2020) Improved inverse distance weighting method application considering spatial autocorrelation in 3D geological modeling, Earth Sci. Inf. 13, 619–632. [CrossRef] [Google Scholar]
  • Milicich S.D., Pearson-Grant S.C., Alcaraz S., White P., Tschritter C. (2018) 3D geological modelling of the Taupo Volcanic Zone as a foundation for a geothermal reservoir model, New Zealand, J. Geol. Geophys. 61, 79–95. [CrossRef] [Google Scholar]
  • Jung H., Jo H., Kim S., Lee K., Choe J. (2018) Geological model sampling using PCA-assisted support vector machine for reliable channel reservoir characterization, J. Pet. Sci. Eng. 167, 396–405. [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]
  • Hazarika P., Yadav A., Roy S. (2017) Influence of permeability in modeling of reservoir triggered seismicity in Koyna region, western India, J. Geol. Soc. India 90, 728–732. [CrossRef] [Google Scholar]
  • Chen L., Yang Z., Liu H. (2017) Sensitivity analysis for the total nitrogen pollution of the Danjiangkou reservoir based on a 3-D water quality model, Front. Earth Sci. 11, 609–619. [CrossRef] [Google Scholar]
  • Wang S., Li Z., Wang S., Han X. (2018) Well pattern optimization based on StoSAG algorithm, Adv. Geo-energy Res. 2, 103–112. [CrossRef] [Google Scholar]
  • Wang X., Wan L., Jiang Z., Liu R., Wang X., Tang W., Gao Y., Liu S., Xu W. (2017) Controlling factors and accumulation model of hydrocarbon reservoirs in the Upper Cretaceous Yogou formation, Koulele area, Termit basin, Niger, J. Earth Sci. 28, 1126–1134. [CrossRef] [Google Scholar]
  • Wang J., Zhang J., Xie J. (2018) Determination of the microstructure of a lithologic Interface using the delayed response characteristics of HorizontalWell gamma ray logging curves: A case study of the Daqingzijing oilfield, Songliao basin, northeast China, Arab. J. Sci. Eng. 43, 6653–6664. [CrossRef] [Google Scholar]
  • Yuan B., Zhang Z., Clarkson C. (2019) Improved distance-of-investigation model for rate-transient analysis in a heterogeneous unconventional reservoir with nonstatic properties, SPE J. 24, 2362–2377. [CrossRef] [Google Scholar]
  • Wang H., Wellmann J.F., Li Z., Wang X., Liang R. (2017) A segmentation approach for stochastic geological modeling using hidden Markov random fields, Math. Geosci. 49, 145–177. [CrossRef] [Google Scholar]
  • Gengxin C., Fan Z., Jiangong W., Hongjun Z., Yaozu Y., Aiping W., Jiyong L., Yunpeng H. (2015) Regionalized multiple-point stochastic geological modeling: A case from braided delta sedimentary reservoirs in Qaidam basin, NW China, Petrol. Explor. Develop. 42, 697–704. [CrossRef] [Google Scholar]
  • Zhang S., Liu Z., Shi A., Wang X. (2019) Development of accurate well models for numerical reservoir simulation, Adv. Geo-Energy Res. 3, 250–257. [CrossRef] [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.