Abstract: Systems and methods for operating wells of a field using a multi-objective genetic algorithm are disclosed. In one embodiment, a method of operating a plurality of wells within a field includes determining an oil rate for each well of the plurality of wells by a multi-objective genetic algorithm. The multi-objective genetic algorithm is defined by a multi-objective fitness function including a first objective function that meets a target oil rate for the field and a second objective function that maximizes bottom-hole reservoir pressure, maximizes a distance of the wells to a crest line of the field, and minimizes a water cut of the field. The multi-objective genetic algorithm outputs the oil rate for each well that satisfies the multi-objective fitness function. The method further includes operating the plurality of wells at the oil rate for each well.

Abstract: Systems and methods for operating wells of a field using a multi-objective genetic algorithm are disclosed. In one embodiment, a method of operating a plurality of wells within a field includes determining an oil rate for each well of the plurality of wells by a multi-objective genetic algorithm. The multi-objective genetic algorithm is defined by a multi-objective fitness function including a first objective function that meets a target oil rate for the field and a second objective function that maximizes bottom-hole reservoir pressure, maximizes a distance of the wells to a crest line of the field, and minimizes a water cut of the field. The multi-objective genetic algorithm outputs the oil rate for each well that satisfies the multi-objective fitness function. The method further includes operating the plurality of wells at the oil rate for each well.

Abstract: Information associated with a plurality of constraints for well placement is received. For each of the plurality of constraints, information associated with the constraint is converted into a binary matrix. An overall constraint matrix is determined by convolving the binary matrices associated with the plurality of constraints. Feasible regions are determined based on the overall constraint matrix. Optimal well locations are determined within the feasible regions. The optimal well locations and a number of the optimal wells are output.

Abstract: Information associated with a plurality of constraints for well placement is received. For each of the plurality of constraints, information associated with the constraint is converted into a binary matrix. An overall constraint matrix is determined by convolving the binary matrices associated with the plurality of constraints. Feasible regions are determined based on the overall constraint matrix. Optimal well locations are determined within the feasible regions. The optimal well locations and a number of the optimal wells are output.