Methods Used to Estimate Reservoir Pressure Performance: A Review

Main Article Content

Manar M. Amer
Dahlia A. Al-Obaidi

Abstract

Reservoir pressure plays a significant role in all reservoir and production engineering studies. It is crucial to characterize petroleum reservoirs: by detecting fluid movement, computing oil in place, and calculating the recovery factor. Knowledge of reservoir pressure is essential for predicting future production rates, optimizing well performance, or planning enhanced oil recovery strategies. However, applying the methods to investigate reservoir pressure performance is challenging because reservoirs are large, complex systems with irregular geometries in subsurface formations with numerous uncertainties and limited information about the reservoir's structure and behavior. Furthermore, many computational techniques, both numerical and analytical, have been utilized to examine reservoir pressure performance. This paper summarizes the concepts and applications of traditional and novel ways to investigate reservoir pressure changes over time. It provides a comprehensive review that assists the reader in recognizing and distinguishing between various techniques for obtaining an accurate description of reservoir pressure behavior during production, such as the reservoir simulation method, material balance equation approach, time-lapse seismic data, and modern artificial intelligence methods. Thus, the central concept of these procedures and a list of the authors' research are discussed.

Article Details

How to Cite
“Methods Used to Estimate Reservoir Pressure Performance: A Review” (2024) Journal of Engineering, 30(06), pp. 83–107. doi:10.31026/j.eng.2024.06.06.
Section
Articles
Author Biography

Dahlia A. Al-Obaidi, Department of Petroleum Engineering, College of Engineering, University of Baghdad

 

 

How to Cite

“Methods Used to Estimate Reservoir Pressure Performance: A Review” (2024) Journal of Engineering, 30(06), pp. 83–107. doi:10.31026/j.eng.2024.06.06.

Publication Dates

Received

2023-07-05

Accepted

2023-08-04

Published Online First

2024-06-01

References

Acharya, U.B., 1987. Effect of tarmat on reservoir behavior: reservoir simulation case studies. In Middle East Oil Show. OnePetro. Doi:10.2118/15690-MS.

Ali, A., and Guo, L., 2019. Neuro-adaptive learning approach for predicting production performance and pressure dynamics of gas condensation reservoir. IFAC-PapersOnLine, 52(29), pp. 122-127. Doi:10.1016/j.ifacol.2019.12.632.

Ali, A., and Guo, L., 2021. Data-driven based investigation of pressure dynamics in underground hydrocarbon reservoirs. Energy Reports, 7, pp. 104-110. Doi:10.1016/j.egyr.2021.02.036.

Ali, D.H., Al-Jawad, M.S., and Van Kirk, C.W., 2015, September. Modeling and history matching of a fractured reservoir in an Iraqi oil field. In SPE Reservoir Characterisation and Simulation Conference and Exhibition. OnePetro. Doi:10.2118/175553-MS.

Al-Mudhafar, W.J., Al-Obaidi, D.A., Saini, D., Wojtanowicz, A.K., and Al-Jawad, M.S., 2021. Feasibility of the Gas and Downhole Water Sink-Assisted Gravity Drainage (GDWS-AGD) process to enhance the recovery of oil in reservoirs with strong aquifer. Macromolecular Characterization of Hydrocarbons for Sustainable Future: Applications to Hydrocarbon Value Chain, pp. 91-106. Doi:10.1007/978-981-33-6133-1_7.

Al-Obaidi, D.A., and Al-Jawad, M.S., 2020. Numerical simulation of immiscible CO2-assisted gravity drainage process to enhance oil recovery. Iraqi Journal of Science, pp. 2004-2016. Doi:10.24996/ijs.2020.61.8.17.

Al-Obaidi, D.A., Wood, D.A., Al-Mudhafar, W.J., Wojtanowicz, A.A., and Merzoug, A., 2023. Development of a Multi-Completion Gas and Downhole Water Sink-Assisted Gravity Drainage (MC-DWS-AGD) to improve oil recovery and reduce water cut in reservoirs with strong water aquifers. In SPE Oklahoma City Oil and Gas Symposium. OnePetro. Doi:10.2118/213071-MS.

Asad, M.S., and Hamd-Allah, S.M., 2022. 3D geological modelling for Asmari reservoir in Abu Ghirab oil field. Iraqi Journal of Science, pp. 2582- 2597. Doi:10.24996/ijs.2022.63.6.24

Babasafari, A.A., Rezaei, S., Sambo, C., Bashir, Y., Ghosh, D.P., Salim, A.M.A., Yusoff, W.I.W., Kazemeini, S.H., and

Kordi, M., 2021. Practical workflows for monitoring saturation and pressure changes from 4D seismic data: A case study of Malay Basin. Journal of Applied Geophysics, 195, p.104472. Doi:10.1016/j.jappgeo.2021.104472.

Bagley, G., Saxby, I., McGarrity, J., Pearse, C., and Slater, C., 2004. 4D/time-lapse seismic: examples from the Foinaven, Schiehallion and Loyal fields, UKCS, West of Shetland. Geological Society, London, Memoirs, 29(1), pp. 297-302. Doi:10.1144/GSL.MEM.2004.029.01.27.

Baker, H.A., and Awad, A.S., 2017. Reservoir characterizations and reservoir performance of Mishrif Formation in Amara Oil Field. Journal of Engineering, 23(12), pp. 33-50. Doi:10.31026/j.eng.2017.12.03.

Cagle, T.O., 1990, September. Performance of secondary gas recovery operations: north Alazan H-21 reservoir. In SPE Annual Technical Conference and Exhibition. OnePetro. Doi:10.2118/20771-MS.

Chadwick, R.A., Williams, G.A., Williams, J.D.O., and Noy, D.J., 2012. Measuring pressure performance of a large saline aquifer during industrial-scale CO2 injection: The Utsira Sand, Norwegian North Sea. International Journal of Greenhouse Gas Control, 10, pp. 374-388. Doi:10.1016/j.ijggc.2012.06.022.

Galkin, V.I., Ponomareva, I.N., and Martyushev, D.A., 2021. Prediction of reservoir pressure and study of its behavior in the development of oil fields based on the construction of multilevel multidimensional probabilistic-statistical models. Gas, 2(2.53), pp. 17-5. Doi:10.18599/grs.2021.3.10.

Grana, D., and Mukerji, T., 2015. Bayesian inversion of time‐lapse seismic data for the estimation of static reservoir properties and dynamic property changes. Geophysical Prospecting, 63(3), pp. 637-655. Doi:10.1111/1365-2478.12203.

Grover, T., Moridis, G.J., and Holditch, S.A., 2008. Analysis of reservoir performance of the Messoyakha gas hydrate reservoir. In SPE Annual Technical Conference and Exhibition. OnePetro. Doi:10.2118/114375-MS.

Gyan, P.S., Xie, C., Brantson, E.T., and Atuahene, S., 2019. Computer modeling and simulation for undersaturated primary drive recovery mechanism. Advances in Mechanical Engineering, 11(5), p.1687814019841948. Doi:10.1177/1687814019841948.

Hashan, M., Jahan, L.N., Zaman, T.U., Elhaj, M., Imtiaz, S., and Hossain, M.E., 2018. Modelling of fluid flow in a petroleum reservoir using an engineering approach. In SPE Trinidad and Tobago Section Energy Resources Conference. OnePetro. Doi:10.2118/191153-MS.

Hoversten, G.M., Gritto, R., Washbourne, J., and Daley, T., 2003. Pressure and fluid saturation prediction in a multicomponent reservoir using combined seismic and electromagnetic imaging. Geophysics, 68(5), pp. 1580-1591. Doi:10.1190/1.1620632.

Idogun, I., Jeboda, O., Charles, D., and Ufomadu, H., 2015, August. Material balance modeling and performance prediction of a multi-tank reservoir. In SPE Nigeria Annual International Conference and Exhibition. OnePetro. Doi:10.2118/178344-MS.

Jassam, S.A., and Al-Fatlawi, O., 2023. Development of 3D geological model and analysis of the uncertainty in a tight oil reservoir in the Halfaya oil field. The Iraqi Geological Journal, pp. 128-142. Doi:10.46717/igj.56.1B.10ms-2023-2-18.

Jemeel, M.R., Lazium, S.A., and Hamdullah, S., 2020. The optimum reservoir performance of Nahr Umr/Ratawi oil field. Journal of Engineering, 26(2), pp. 42-56.

Doi:10.31026/j.eng.2020.02.04.

Koutsabeloulis, N., and Zhang, X., 2009. 3D reservoir geomechanical modeling in oil/gas field production. In SPE Saudi Arabia section technical symposium. OnePetro. Doi:10.2118/126095-MS.

Landa, J., Meadows, M., Thacher, C., Waddle, R., and Williams, N., 2015. Map-based estimation of reservoir pressure and saturation from 4D seismic with a data-driven procedure. In SPE Annual Technical Conference and Exhibition. Doi:10.2118/175100-MS.

Ma, E., Gheorghiu, S., Banagale, M., Dashti, L., Bond, D., Ibrahim, M., Ali, F., and Gurpinar, O., 2015. Reservoir simulation to support pressure maintenance projects in the Greater Burgan field, Kuwait. In SPE Middle East Oil & Gas Show and Conference. OnePetro. Doi:10.2118/172517-MS.

Majeed, A.J., Al-Rbeawi, S., 2022. The impact of the spatial and temporal variability of physical and petrophysical properties on conventional reservoir performance. Arab J Geosci 15, 1641. Doi:10.1007/s12517-022-10922-9

Mashallo, G.P., 2020. Application of material balance equation in prediction of reservoir performance. MSc. Thesis, University of Portsmouth.

Mattar, L., and Anderson, D., 2005. Dynamic material balance (oil or gas-in-place without shut-ins). In Canadian International Petroleum Conference. Doi:10.2118/06-11-TN.

Manzir, P.M., Beka, F.T., and Kadana, R.I., 2015. Predicting reservoir performance changes with time. International Journal For Reserarch In Emerging Science and Technology, 2(9), pp. 85-94.

Mohammed, M.A., Hamd-Allah, S., and Hameed, R., 2020. Diagnosing water problem for Asmari reservoir in Abu Ghirab oilfield using analytical and numerical approaches. Journal of Engineering, 26(4), pp. 94-110. Doi:10.31026/j.eng.2020.04.07.

Mohammed, S., Enty, G.S., and Amarfio, E.M., 2014. Determination of average reservoir pressure from constant-rate drawdown tests. In SPE Nigeria Annual International Conference and Exhibition. OnePetro. Doi:10.2118/172424-MS.

Mohammed, S.Q., and Almaqtri, A.M.G., 2022. Improving the future performance using simulation method by Eclipse software program, the Habban field (Block S2) case study. Tehama journal, (15), pp. 16-16.

Mohammed, W.J., Al-Jawad, M.S., and Al-Shamma, D.A., 2010, January. Reservoir flow simulation study for a sector in main pay-South Rumaila oil field. In SPE Oil and Gas India Conference and Exhibition. OnePetro. Doi:10.2118/126427-MS.

Orozco Ibarra, D.R., 2016. A new material balance methodology for quintuple porosity shale gas and shale condensate reservoirs. MSc. Thesis, Chemical and petroleum engineering department, University of Calgary, Calgary, Canada.

Reddy, K., Gupta, M., McClenaghan, R., Saikia, K., Mishra, S., Rao, C., Joysula, S., Kumar, A., and Shankar, V., 2013. Estimation of pore pressure and oil saturation changes in the reservoir using petro-elastic modeling and 4D AVO inversion attributes in the Ravva field. In SEG, Society of Exploration Geophysicists, pp. 4981-4985. Doi:10.1190/segam2013-1021.1.

Sallam, S., Ahmad, M., Nasr, M., and Gomari, S.R., 2015. Reservoir simulation for investigating the effect of reservoir pressure on oil recovery factor. International Journal of Advanced Research in Science, Engineering and Technology, 2(10), pp. 875-882.

Sapale, P., Bhadariya, V., Achari N, A.K., Paliwal, N., and Sreeharsha, V., 2019. Reservoir performance prediction using integrated production modelling (MBAL Software). International Journal of Recent Technology and Engineering (IJRTE), 8(4), pp. 1484-1489. Doi:10.35940/ijrte.D7627.118419.

Satter, A., and Iqbal, G.M., 2015. Reservoir engineering: the fundamentals, simulation, and management of conventional and unconventional recoveries. Gulf Professional Publishing.

Shamkhi, M.K., and Al-Jawad, M.S., 2021. Representative sector modeling and waterflooding performance in Rumaila oilfield. Iraqi Journal of Science, pp. 192-203. Doi:10.24996/ijs.2021.62.1.18.

Sibley, M.J., Bent, J.V., and Davis, D.W., 1997. Reservoir modeling and simulation of a Middle Eastern carbonate reservoir. SPE Reservoir Engineering, 12(02), pp. 75-81. Doi:10.2118/36540-PA.

Subbey, S., Christie, M., and Sambridge, M., 2004. Prediction under uncertainty in reservoir modeling. Journal of Petroleum Science and Engineering, 44(1-2), pp. 143-153. Doi:10.1016/j.petrol.2004.02.011.

Sylvester, O., and MO, O., 2015. Software for reservoir performance prediction. In SPE Nigeria Annual International Conference and Exhibition. Doi:10.2118/178288-MS.

Sylvester, O., Buduka, S., and Bibobra, I., 2015. Work flow for reservoir study and challenges. Paper presented at the SPE Nigeria Annual International Conference and Exhibition, Lagos, Nigeria. Doi:10.2118/178290-MS.

Tracy, G.W., 1955. Simplified form of the material balance equation. Transactions of the AIME, 204(01), pp. 243-246. Doi:10.2118/438-G.

Vaferi, B., Salimi, V., Baniani, D.D., Jahanmiri, A., and Khedri, S., 2012. Prediction of transient pressure response in the petroleum reservoirs using orthogonal collocation. Journal of Petroleum Science and Engineering, 98, pp. 156-163. Doi:10.1016/j.petrol.2012.04.023.

Walsh, M.P., 1995. A generalized approach to reservoir material balance calculations. Journal of Canadian Petroleum Technology, 34(1), pp. 55-63.

Widiyaningsih, I., Widiantoro, P.S., Suwardi, S., and Karimah, R.F.N., 2021. Reservoir performance analysis using material balance method in gas field. Journal of Petroleum and Geothermal Technology, 2(2), pp. 75-86. Doi:10.31315/jpgt.v2i2.5503.

Wigwe, M.E., Basit, M.I., Elldakli, F., Dambani, S., Mmuenu, R., and Soliman, M.Y., 2020. Comparative study of field development scenarios of the Elk City gas reservoir using reservoir simulation. In SPE Nigeria Annual International Conference and Exhibition. OnePetro. Doi:10.2118/203704-MS.

Yang, Y., Yu, J., Wang, Y., and Ma, C., 2021. Optimizing and accelerating history matching progress of numerical reservoir simulation by using material balance analysis. In MATEC Web of Conferences (Vol. 336, P. 01019). Doi:10.1051/matecconf/202133601019.

Zou, M., Wei, C., Li, L., Yang, Y., and Lei, B., 2013. Numerical simulation on the dynamic variation of reservoir pressure of typical coalbed methane single well and well net group—A case study on QN01 well in the southern Qinshui Basin, China. Energy exploration & exploitation, 31(2), pp. 249-265.

Similar Articles

You may also start an advanced similarity search for this article.