A Physics-Based Data-Driven Model for History Matching, Prediction, and Characterization of Unconventional Reservoirs
- Yanbin Zhang (Chevron Energy Technology Company) | Jincong He (Chevron Energy Technology Company) | Changdong Yang (Chevron Energy Technology Company) | Jiang Xie (Chevron Energy Technology Company) | Robert Fitzmorris (Chevron Energy Technology Company) | Xian-Huan Wen (Chevron Energy Technology Company)
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- August 2018
- Document Type
- Journal Paper
- 1,105 - 1,125
- 2018.Society of Petroleum Engineers
- History Matching, Unconventional Reservoir, Data Driven, Simulation
- 10 in the last 30 days
- 560 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 5.00|
|SPE Non-Member Price:||USD 35.00|
We developed a physics-based data-driven model for history matching, prediction, and characterization of unconventional reservoirs. It uses 1D numerical simulation to approximate 3D problems. The 1D simulation is formulated in a dimensionless space by introducing a new diffusive diagnostic function (DDF). For radial and linear flow, the DDF is shown analytically to be a straight line with a positive or zero slope. Without any assumption of flow regime, the DDF can be obtained in a data-driven manner by means of history matching using the ensemble smoother with multiple data assimilation (ES-MDA). The history-matched ensemble of DDFs offers diagnostic characteristics and probabilistic predictions for unconventional reservoirs.
|File Size||2 MB||Number of Pages||21|
Acuña, J. A. 2016. Analytical Pressure and Rate Transient Models for Analysis of Complex Fracture Networks in Tight Reservoirs. Presented at the Unconventional Resources Technology Conference, San Antonio, Texas, 1–3 August. URTEC-2429710-MS.
Alharthy, N., Teklu, T., Kazemi, H. et al. 2016. Compositional Rate Transient Analysis in Liquid Rich Shale Reservoirs. Presented at the SPE Annual Technical Conference and Exhibition, Dubai, 26–28 September. SPE-181699-MS. https://doi.org/10.2118/181699-MS.
Arps, J. J. 1945. Analysis of Decline Curves. Trans. AIME 160 (1): 228–247. SPE-945228-G. https://doi.org/10.2118/945228-G.
Blasingame, T. A., McCray, T. L., and Lee, W. J. 1991. Decline Curve Analysis for Variable Pressure Drop/Variable Flowrate Systems. Presented at the SPE Gas Technology Symposium, Houston, 22–24 April. SPE-21513-MS. https://doi.org/10.2118/21513-MS.
Bourdet, D., Ayoub, J. A., and Pirard, Y. M. 1989. Use of Pressure Derivative in Well-Test Interpretation. SPE Form Eval 4 (2): 293–302. SPE-12777-PA. https://doi.org/10.2118/12777-PA.
Brown, M., Ozkan, E., Raghavan, R. et al. 2011. Practical Solutions for Pressure-Transient Responses of Fractured Horizontal Wells in Unconventional Shale. SPE Res Eval & Eng 14 (6): 663–676. SPE-125043-PA. https://doi.org/10.2118/125043-PA.
Bui, K. and Akkutlu, I. Y. 2015. Nanopore Wall Effect on Surface Tension of Methane. J. Mol. Phys. 113 (22): 3506–3513. https://doi.org/10.1080/00268976.2015.1037369.
Cipolla, C. L., Fitzpatrick, T., Williams, M. J. et al. 2011. Seismic-to-Simulation for Unconventional Reservoir Development. Presented at the SPE Reservoir Characterisation and Simulation Conference and Exhibition, Abu Dhabi, 9–11 October. SPE-146876-MS. https://doi.org/10.2118/146876-MS.
Cui, J., Yang, C., Zhu, D. et al. 2016. Fracture Diagnosis in Multiple-Stage-Stimulated Horizontal Well by Temperature Measurements with Fast Marching Method. SPE J. 21 (6): 2289–2300. SPE-174880-PA. https://doi.org/10.2118/174880-PA.
Datta-Gupta, A., Xie, J., Gupta, N. et al. 2011. Radius of Investigation and its Generalization to Unconventional Reservoirs. J Pet Technol 63 (7): 52–55. SPE-0711-0052-JPT. https://doi.org/10.2118/0711-0052-JPT.
Du, S., Liang, B., and Lin, Y. 2017. Field Study: Embedded Discrete Fracture Modeling With Artificial Intelligence in Permian Basin for Shale Formation. Presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, 9–11 October. SPE-187202-MS. https://doi.org/10.2118/187202-MS.
Du, S., Yoshida, N., Liang, B. et al. 2016. Application of Multi-Segment Well Approach: Dynamic Modeling of Hydraulic Fractures. J. Nat. Gas Sci. Eng. 34 (August): 886–897. https://doi.org/10.1016/j.jngse.2016.07.028.
Emerick, A. A. and Reynolds A. C. 2013. Ensemble Smoother With Multiple Data Assimilations. Comput. Geosci. 55 (June): 3–15. https://doi.org/10.1016/j.cageo.2012.03.011.
Fetkovich, M. J. 1980. Decline Curve Analysis Using Type Curves. J Pet Technol 32 (6): 1065–1077. SPE-4629-PA. https://doi.org/10.2118/4629-PA.
Fetkovich, M. J., Vienot, M. E., Bradley, M. D. et al. 1987. Decline Curve Analysis Using Type Curves: Case Histories. SPE Form Eval 2 (4): 637–656. SPE-13169-PA. https://doi.org/10.2118/13169-PA.
Fujita, Y., Datta-Gupta, A., and King, M. J. 2016. A Comprehensive Reservoir Simulator for Unconventional Reservoirs That is Based on the Fast Marching Method and Diffusive Time of Flight. SPE J. 21 (6): 2276–2288. SPE-173269-PA. https://doi.org/10.2118/173269-PA.
Javadpour, F. 2009. Nanopores and Apparent Permeability of Gas Flow in Mudrocks (Shales and Siltstone). J Can Pet Technol 48 (8): 16–21. PETSOC-09-08-16-DA. https://doi.org/10.2118/09-08-16-DA.
Jia, P., Cheng, L., Huang, S. et al. 2015. A Semi-Analytical Model for Production Simulation of Complex Fracture Network in Unconventional Reservoirs. Presented at the SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition, Nusa Dua, Bali, Indonesia, 20–22 October. SPE-176227-MS. https://doi.org/10.2118/176227-MS.
Kabir, C. S. and Lake, L. W. 2011. An Analytical Approach to Estimating EUR in Unconventional Reservoirs. Presented at the North American Unconventional Gas Conference and Exhibition, The Woodlands, Texas, 14–16 June. SPE-144311-MS. https://doi.org/10.2118/144311-MS.
Le, D. H., Emerick, A. A., and Reynolds, A. C. 2016. An Adaptive Ensemble Smoother With Multiple Data Assimilation for Assisted History Matching. SPE J. 21 (6): 2195–2207. SPE-173214-PA. https://doi.org/10.2118/173214-PA.
Lee, S. H., Jensen, C. L., and Lunati, I. 2015. A Statistical Dual-Tube Model to Analyze Gas Production From Shale Formations. Presented at the SPE Reservoir Simulation Symposium, Houston, 23–25 February. SPE-173260-MS. https://doi.org/10.2118/173260-MS.
Li, L. and Lee, S. H. 2008. Efficient Field-Scale Simulation of Black Oil in a Naturally Fractured Reservoir Through Discrete Fracture Networks and Homogenized Media. SPE J. 11 (4): 750–758. SPE-103901-PA. https://doi.org/10.2118/103901-PA.
Mi, L., Jiang, H., Tang, L. et al. 2016. A Utility Discrete Fracture Network Model for Field-Scale Simulation of Naturally Fractured Shale Reservoirs. Presented at SPE Argentina Exploration and Production of Unconventional Resources Symposium, Buenos Aires, 1–3 June. SPE-180968-MS. https://doi.org/10.2118/180968-MS.
Nobakht, M. and Mattar, L. 2012. Analyzing Production Data From Unconventional Gas Reservoirs With Linear Flow and Apparent Skin. J Can Pet Technol 51 (1): 52–59. SPE-137454-PA. https://doi.org/10.2118/137454-PA.
Sharma, A. and Lee, W. J. 2016. Improved Workflow for EUR Prediction in Unconventional Reservoirs. Presented at the Unconventional Resources Technology Conference, San Antonio, Texas, 1–3 August. URTEC-2444280-MS.
Shojaei, H. and Tajer, E. S. 2013. Analytical Solution of Transient Multiphase Flow to a Horizontal Well With Multiple Hydraulic Fractures. Presented at the SPE Eastern Regional Meeting, Pittsburgh, Pennsylvania, 20–22 August. SPE-165703-MS. https://doi.org/10.2118/165703-MS.
Siripatrachai, N., Ertekin, T., and Johns, R. T. 2016. Compositional Simulation of Hydraulically Fractured Tight Formation Considering the Effect of Capillary Pressure on Phase Behavior. SPE J. 22 (4): 1046–1063. SPE-179660-PA. https://doi.org/10.2118/179660-PA.
Stalgorova, K. and Mattar, L. 2013. Analytical Model for Unconventional Multifractured Composite Systems. SPE Res Eval & Eng 16 (3): 246–256. SPE-162516-PA. https://doi.org/10.2118/162516-PA.
Wang, Z., Li, C., and King, M. J. 2017. Validation and Extension of Asymptotic Solutions of Diffusivity Equation and Their Applications to Synthetic Cases. Presented at the SPE Reservoir Simulation Conference, Montgomery, Texas, 20–22 February. SPE-182716-MS. https://doi.org/10.2118/182716-MS.
Xie, J., Yang, C., Gupta, N. et al. 2015. Depth of Investigation and Depletion in Unconventional Reservoirs With Fast-Marching Methods. SPE J. 20 (4): 831–841. SPE-154532-PA. https://doi.org/10.2118/154532-PA.
Xue, X., Yang, C., Sharma, V. K. et al. 2016. Reservoir and Fracture Flow Characterization Using a Novel W(s) Formulation. Presented at the Unconventional Resources Technology Conference, San Antonio, Texas, 1–3 August. URTEC-2440083-MS.
Yang, C., Sharma, V. K., Datta-Gupta, A. et al. 2015. A Novel Approach for Production Transient Analysis of Shale Gas/Oil Reservoirs. Presented at the Unconventional Resources Technology Conference, San Antonio, Texas, 20–22 July. URTEC-2176280-MS. https://doi.org/10.15530/URTEC-2015-2176280.
Zhang, Y., Bansal, N., Fujita, Y. et al. 2016. From Streamlines to Fast Marching: Rapid Simulation and Performance Assessment of Shale-Gas Reservoirs by Use of Diffusive Time of Flight as a Spatial Coordinate. SPE J. 21 (5): 1883–1898. SPE-168997-PA. https://doi.org/10.2118/168997-PA.
Zhou, W., Banerjee, R., Poe, B. D. et al. 2014. Semi-Analytical Production Simulation of Complex Hydraulic Fracture Networks. SPE J. 19 (1): 6–18. SPE-157367-PA. https://doi.org/10.2118/157367-PA.