Reconciling flowback and production data: A novel history matching approach for liquid rich shale wells

Abstract The recent downturn in petroleum prices poses a challenge to the oil and gas industry and in particular to the development of shale assets. This unfavorable economic situation calls for improved reservoir characterization and modeling that are necessary for the optimization of well design and cost. Current rate transient analysis (RTA) techniques are well-suited for the reservoir characterization of dry shale gas wells. However, they have limited accuracy for the more profitable liquid rich shale (LRS) wells. For example, RTA does not accurately model three-phase flow of gas, water, and condensate that can occur in the reservoir. Further, application of RTA typically ignores post-fracturing water flowback data that can be analyzed for important information pertaining to hydraulic and secondary fractures. In this work, the reservoir of LRS wells is characterized by simulating and history matching the three-phase flow of early flowback and long-term production. In this work, characterization and history matching are performed by utilizing a novel procedure that includes an equation of state (EOS), a compositional reservoir simulator, and a multi-objective optimization (MOO) algorithm. In addition, the simulation approach incorporates various water trapping mechanisms that are necessary to reproduce the water production rate trends. The procedure minimizes the misfit between observed and simulated production data. It starts by first tuning the EOS to a surface recombined sample. Then, a triple-porosity (i.e., hydraulic fracture, secondary/natural fracture, and matrix porosities) simulation model is applied that utilizes the multiple interacting continua (MINC) method and accounts for permeability jail, pressure-dependent permeability, capillary pressure, and gravity segregation. Finally, an MOO algorithm is used, namely the fast nondominant sorting genetic algorithm (NSGA-II), to solve the misfit minimization problem. The introduced procedure has a number of advantages. One advantage is the simultaneous matching of flowback and production data which improves rock and fracture characterization. Another advantage is the utilization of MINC combined with logarithmic gridding that can model transient flow in a triple porosity system. Moreover, it can model different fracture shapes by adjusting a shape factor parameter. In addition to these, the use of MOO does not require predetermined weights unlike the aggregate function approach. In this article, detailed description of all the steps of the history matching workflow are presented, and its applicability demonstrated with a field example from the Montney Formation.

[1]  Robert A. Wattenbarger,et al.  Comparison of Empirical Decline Curve Methods for Shale Wells , 2012 .

[2]  Gudmundur S. Bodvarsson,et al.  A numerical dual‐porosity model with semianalytical treatment of fracture/matrix flow , 1993 .

[3]  Christopher R. Clarkson,et al.  Unconventional gas reservoir evaluation: What do we have to consider? , 2012 .

[4]  Stephen A. Holditch,et al.  Factors Affecting Water Blocking and Gas Flow From Hydraulically Fractured Gas Wells , 1979 .

[5]  C. Clarkson,et al.  Modeling Two-Phase Flowback of Multifractured Horizontal Wells Completed in Shale , 2013 .

[6]  Robert A. Wattenbarger,et al.  Bilinear Flow in Shale Gas Wells: Fact or Fiction? , 2014 .

[7]  J. E. Warren,et al.  The Behavior of Naturally Fractured Reservoirs , 1963 .

[8]  H. Kazemi,et al.  NUMERICAL SIMULATION OF WATER-OIL FLOW IN NATURALLY FRACTURED RESERVOIRS , 1976 .

[9]  Robert A. Wattenbarger,et al.  Triple-porosity Models: One Further Step Towards Capturing Fractured Reservoirs Heterogeneity , 2011 .

[10]  M. Honarpour,et al.  Imbibition and Water Blockage In Unconventional Reservoirs: Well-Management Implications During Flowback and Early Production , 2014 .

[11]  T. N. Narasimhan,et al.  A PRACTICAL METHOD FOR MODELING FLUID AND HEAT FLOW IN FRACTURED POROUS MEDIA , 1985 .

[12]  K. Aziz,et al.  Matrix-fracture transfer shape factors for dual-porosity simulators , 1995 .

[13]  Rasheed Olusehun Bello,et al.  Rate Transient Analysis in Shale Gas Reservoirs with Transient Linear Behavior , 2010 .

[14]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[15]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[16]  Akhil Datta-Gupta,et al.  Handling conflicting multiple objectives using Pareto-based evolutionary algorithm during history matching of reservoir performance , 2015 .

[17]  Robert A. Wattenbarger,et al.  Analysis and Verification of Dual Porosity and CBM Shape Factors , 2006 .

[18]  K. Shanley,et al.  Factors controlling prolific gas production from low-permeability sandstone reservoirs: Implications for resource assessment, prospect development, and risk analysis , 2004 .

[19]  Yueming Cheng,et al.  Impact of Water Dynamics in Fractures on the Performance of Hydraulically Fractured Wells in Gas Shale Reservoirs , 2010 .

[20]  Christopher R. Clarkson,et al.  History-matching and forecasting tight/shale gas condensate wells using combined analytical, semi-analytical, and empirical methods , 2014 .

[21]  O. A. Pedrosa.  Pressure Transient Response in Stress-Sensitive Formations , 1986 .

[22]  Robert A. Wattenbarger,et al.  Estimation of Effective-Fracture Volume Using Water-Flowback and Production Data for Shale-Gas Wells , 2014 .

[23]  Alfred Daniel Hill,et al.  Experimental and numerical studies of reduced fracture conductivity due to proppant embedment in the shale reservoir , 2015 .

[24]  R. Courant,et al.  On the Partial Difference Equations, of Mathematical Physics , 2015 .

[25]  Matthew T. Balhoff,et al.  Reservoir simulation and optimization of Huff-and-Puff operations in the Bakken Shale , 2015 .

[26]  Mukul M. Sharma,et al.  Impact of Liquid Loading in Hydraulic Fractures on Well Productivity , 2013 .

[27]  Christopher R. Clarkson,et al.  A Modified Approach For Modeling 2-Phase Flowback From Multi-Fractured Horizontal Shale Gas Wells , 2015 .

[28]  Randy L. Haupt,et al.  Practical Genetic Algorithms , 1998 .