Multi-Parametric Optimization of Multilateral Wells for Optimum Reservoir Contact

Abstract

As the oil and gas industry is continuously pushing boundaries of exploiting resources, it becomes more of a mandate to model and optimize forefront technologies. Multilateral wells are one example of a prevalent technology to maximize reservoir contact and return on investment. Optimum design and placement of this type of wells is significant. This work presents a multi-parametric optimization approach that optimizes the design of multilateral wells and maximizes the contact with highly productive hydrocarbon zones in the reservoir. Given a number of input parameters, the design and placement of multilateral wells is modeled using the Graph Theory principles and is optimized using Mixed Integer Programming (MIP) algorithms. The objective function is defined in this work as maximization function of the Total Contact with Sweetspots (TCS). At first, multiple main wellbores are optimized globally across the field and then several local optimizations are performed around each main wellbore to place the laterals. This optimization is subject to a number of input constraints, such as the maximum number of laterals, minimum spacing between wells, and maximum lateral length. Different sets of uncertainty parameters are generated using Latin-Hypercube Sampling (LHS) technique and used as input constraints in multiple well design realizations. In this work, the SPE10 benchmark model with 4 million grid cells and 10 existing producer wells was used. MIP was used in this work to optimize the initial geometry and placement of 20 new multilateral producers while LHS was used to fine-tune well configurations. Using TCS as the objective function in this multi-parametric optimization approach dramatically reduced the number of numerical simulation runs. The multi-parametric optimization generates multiple realizations with different sets of multilateral wells with different configurations. Numerical results from the benchmark model revealed the optimum solution with maximized hydrocarbon production. This resulted in a more practical approach to simultaneously optimize the placement of multilateral wells in large simulation models. In addition, the results reveal that the design, placement and performance of the new wells are highly sensitive to the sweetspot maps and reservoir heterogeneity. Using TCS as the objective function resulted in avoiding the excessive use of numerical simulation and cutting down the turnaround time for optimizing the design and placement of multilateral wells. In addition, the global and local optimizations used in this approach significantly simplified the mathematical formulation and avoided complex network modeling and optimization for multilateral wells.