Inclusion of Well Schedule and Project Life in Well Placement Optimization

Abstract

Abstract Well placement optimization plays an essential role in reservoir management. When wells are placed in optimum positions in the reservoir and operated at optimum rates or bottomhole pressures, the financial return on investment is expected to be high. In a highly heterogeneous reservoir, placing the wells in appropriate positions, although very challenging, can improve the profitability of investment significantly. Also, operating the wells under appropriate controls can enhance the viability of the project. One challenge with well placement, however, is how to simultaneously optimize well locations, well controls, well types, well schedules and project life cycle. Many works addressing the issue of well placement optimization often fix the project life and also assume that the wells were drilled at the beginning of the project. It is well known that all wells cannot be drilled all at the beginning of the project due to manpower and facility constraints, even if that scenario is the optimal choice for high net present value of the project. In this work, we present an optimization framework that simultaneously finds the optimal number of wells, their types, locations, controls, well schedules and the optimal project life cycle. The method is an extension of our previous work in which all the above except well schedules and project life cycle have been determined using global optimization strategy. We included an additional variable that represents the project life in the list of optimization parameters and one variable per well to represent the fractional time at which the well is to be drilled, completed and put on production or injection. This fractional time is a fraction of the total project life and each well has its own fractional time. This means that wells will not be drilled all at the same time during the project life. To ensure that too many wells are not scheduled for drilling/commissioning during the same year, we adopted the penalty approach to enforce a set of inequality constraints that ensure that the number of wells drilled and put on operation in any particular year is not more than a predetermined number. Differential Evolution was used as the global optimizer to solve the problem. Results show that the method is able to yield high net present value corresponding to the estimated set of parameters including well schedules and project life cycle.