Optimizing Treatment Parameters for Enhanced Hydrocarbon Production by Hydraulic Fracturing

Abstract

Abstract A novel scheme is presented in this paper for hydraulic fracturing design, which integrates reservoir properties, operational limitations, fracture growth control requirements, reservoir production behaviour, and investment-return cash flow behaviour in deciding on the optimum values of various treatment parameters. The capability and robustness of the optimization scheme is demonstrated by applications to a tight gas reservoir for which various designs are obtained: maximum NPV design, maximum production design, a target production design, and a compromised design. Optimum designs are found to be different for different objective functions. It is demonstrated that maximization of NPV, or production, involves a high treatment cost, which can be minimized further by solving a combined objective function, but at the expense of some NPV or production. By tradeoff analysis between production/NPV and treatment cost, 44% of treatment cost saving is indicated at the expense of only a 12% sacrifice in production/NPV. Various other design issues are investigated by sensitivity analyses. Introduction At the design stage of a hydraulic fracturing job for a particular reservoir, the engineer must decide on a set of values for treatment parameters, such as viscosity of fracturing fluid, injection rate of the fluid, injection time, proppant type, proppant concentration, etc. A systematic and integrated procedure can aid the designer to perform this design task efficiently and to enforce a favourable hydraulic fracture geometry that meets various design objectives. In most hydraulic fracturing design works(1–4), maximization of net present value (NPV) is used as a design objective. The improvement in NPV is attempted by parametric sensitivity analysis systematically varying a number of treatment parameters and fracture length. Such a procedure is tedious and does not guarantee to achieve the "best possible" design because it often fails to explore all potential design scenarios and to address various operational factors (e.g., pump capacity, tubular strength, and pressure rating of surface equipment) and fracture growth control requirements. Furthermore, there may be benefits in considering other design objectives, such as maximizing production/NPV with minimum treatment costs or achieving a specific production target. Yang et al.(5) attempted procedural optimization of hydraulic fracturing considering only the injection rate, injection time, and proppant concentration as the free variables. The work, however, ignored operational factors and fracture growth control requirements. The three variables were optimized within their specified ranges to maximize NPV, systematically varying the fracture length with a fixed height. Thus, the work failed to exploit the full benefit of procedural optimization. Mohaghegh et al.(6) recently reported a hybrid Neuro-genetic approach for hydraulic fracture treatment design optimization based on historical data. Their approach avoids the problem of mathematical formulation and lacks the generality of a physics-based integrated approach. Mahrer(7) recently reviewed research work on hydraulic fracture geometry and treatment practice, and rightly concluded "optimizing treatment procedures based on in situ conditions is an area of technology that requires research and development." The current authors have therefore developed an optimization scheme to integrate the effects of various issues discussed above with the hydraulic fracturing design procedure.