Novel method for the rapid evaluation of pressure depletion in tight oil reservoirs
Keywords:
Tight oil reservoirs, depletion-driven development, reservoir pressure depletion assessment, surrogate models linear regressionAbstract
Tight oil reservoirs hold immense development potential but are characterized by challenging reservoir properties, severe heterogeneity, and extremely low permeability and porosity. Massive hydraulic fracturing of horizontal wells is applied to achieve sustainable production in these reservoirs. The swift assessment of pressure depletion in tight reservoirs is essential for their successful and cost-effective development. Traditional pressure testing methods necessitate well shutdown, impacting subsequent production, while numerical simulation methods demand significant computational resources and expertise from technical personnel. To identify the sensitivity parameters influencing the reservoir pressure drop, this study uses a Plackett-Burman design and variance analysis. Using numerical simulations, variance analysis and multi-linear regression, we formulate evaluation indices and surrogate models for individual well depletion. The method’s reliability is validated through multiple experiments along with testing data. Our rapid evaluation method accurately assesses pressure depletion in typical well groups, with a fitting rate exceeding 85%. In regions where the pressure maintenance is below 80%, indicating severe reservoir depletion, enhanced oil recovery treatments, e.g., gas or water injection, are applied based on the evaluation results. The proposed method for evaluating individual well pressure depletions provides crucial guidance for realizing the efficient development of tight oil reservoirs.
Document Type: Short communication
Cited as: Ding, C., Chen, J., Yang, G., Bao, R., Dou, Y., Song, K. Novel method for the rapid evaluation of pressure depletion in tight oil reservoirs. Advances in Geo-Energy Research, 2024, 11(1): 74-80. https://doi.org/10.46690/ager.2024.01.07
ReferencesAdeosun, T., Adabanija, M., Akinpelu, F. Modeling and simulation of reservoir pressure associated with emulsions transport near wellbore for enhanced oil recovery. World Scientific News, 2020, 144: 427-449.
Al-Tailji, W. H., Shah, K., Davidson, B. M. The application and misapplication of 100-mesh sand in multi-fractured horizontal wells in low-permeability reservoirs. Paper SPE 179163 Presented at the SPE Hydraulic Fracturing Technology Conference, The Woodlands, Texas, USA, 9-11 February, 2016.
Beres, D. L., Hawkins, D. M. Plackett–burman technique for sensitivity analysis of many-parametered models. Ecological Modelling, 2001, 141(1): 171-183.
Cheng, Z., Wang, T., Bai, Z., et al. A numerical simulation investigation on low permeability reservoirs air flooding: Oxidation reaction models and factors. Geoenergy Science and Engineering, 2023, 223: 211506.
Chi, J., Zhang, X. Numerical simulation study of CO2 immiscible flooding in ultra-low permeability reservoir. Environmental Technology & Innovation, 2021, 23: 101612.
Fanchi, J. R. Principles of Applied Reservoir Simulation. Amsterdam, Netherlands, Elsevier, 2005.
Hu, W., Wei, Y., Bao, J. Development of the theory and technology for low permeability reservoirs in china. Petroleum Exploration and Development, 2018, 45(4): 685-697.
Ji, B., Fang, J. An overview of efficient development practices at low permeability sandstone reservoirs in china. Energy Geoscience, 2023, 4(3): 100179.
Kang, W., Zhou, B., Issakhov, M., et al. Advances in enhanced oil recovery technologies for low permeability reservoirs. Petroleum Science, 2022, 19(4): 1622-1640.
Lesan, A., Shams, R., Pourafshary, P., et al. Application of the modified capacitance-resistance method to model reservoir pressure maintenance. Results in Engineering, 2023, 19: 101231.
Li, E., Yang, W., Bai, G., et al. The application of reservoir pressure monitoring in oilfield development and production. Chemical Engineering & Equipment 2018, 12: 149-150.
Li, N., Xie, H., Hu, J., et al. A critical review of the experimental and theoretical research on cyclic hydraulic fracturing for geothermal reservoir stimulation. Geomechanics and Geophysics for Geo-Energy and Geo-Resources, 2022, 8: 1-19.
Li, Y., Huang, Y., Ma, H., et al. Study on the development options of tight sandstone oil reservoirs and their influencing factors. Frontiers in Energy Research, 2022, 10: 1007224.
Liang, H., Liu, G., Zou, J., et al. Research on calculation model of bottom of the well pressure based on machine learning. Future Generation Computer Systems, 2021, 124: 80-90.
Liu, S., Agarwal, R., Sun, B., et al. Numerical simulation and optimization of injection rates and wells placement for carbon dioxide enhanced gas recovery using a genetic algorithm. Journal of Cleaner Production, 2021, 280: 124512.
Peaceman, D. W. Fundamentals of Numerical Reservoir Simulation. Amsterdam, Netherlands, Elsevier, 2000.
Plackett, R. L., Burman, J. P. The design of optimum multifactorial experiments. Biometrika, 1946, 33(4): 305-325.
Ponomareva, I. N., Martyushev, D. A., Govindarajan, S. K. A new approach to predict the formation pressure using multiple regression analysis: Case study from sukharev oil field reservoir–russia. Journal of King Saud University-Engineering Sciences, 2022, in press, https://doi.org/10.1016/j.jksues.2022.03.005.
Ranjith, P. G., Zhang, C., Zhang, Z. Experimental study of fracturing behaviour in ultralow permeability formations: A comparison between CO2 and water fracturing. Engineering Fracture Mechanics, 2019, 217: 106541.
Ren, L., Lin, R., Zhao, J., et al. Simultaneous hydraulic fracturing of ultra-low permeability sandstone reservoirs in china: Mechanism and its field test. Journal of Central South University, 2015, 22(4): 1427-1436.
Scerbacova, A., Mukhina, E., Bakulin, D., et al. Water-and surfactant-based huff-n-puff injection into unconventional liquid hydrocarbon reservoirs: Experimental and modeling study. Energy & Fuels, 2023, 37(15): 11067-11082.
Shao, Z., Xiong, W., Gao, S., et al. Experimental study on depletion development mechanism in low permeability gas reservoir. Advanced Materials Research, 2015, 1079: 464-468.
Tang, H., Fu, P., Jo, H., et al. Deep learning-accelerated 3d carbon storage reservoir pressure forecasting based on data assimilation using surface displacement from insar. International Journal of Greenhouse Gas Control, 2022, 120: 103765.
Wang, D., Yin, D., Gong, X. Numerical simulation of microemulsion flooding in low-permeability reservoir. Journal of Chemistry, 2019, 2019: 5021473.
Zhou, Y., Ju, S., Lyu, Q., et al. Numerical simulation of two-phase flow in glutenite reservoirs for optimized deployment in horizontal wells. Fluid Dynamics & Materials Processing, 2023, 19(1): 245-259.
