Infill well placement optimization in coal bed methane reservoirs using genetic algorithm

The unprecedented growth of coal bed methane drilling, expensive coal bed water treatment, and low gas rates urge the integration of petroleum engineering and optimization disciplines to meet production goals. An integrated framework is constructed to attain best-obtained optimal locations of infill wells in coal bed methane reservoirs. This framework consists of a flow simulator (ECLIPSE E100), an optimization method (genetic algorithm), and an economic objective function. The objective function is the net present value of the infill project based on an annual discount rate. Best obtained optimal well locations are attained using the integrated framework when net present value is maximized. In this study, a semi synthetic model is constructed based on the Tiffany unit coal bed data in the San Juan basin. The number of infill wells in reservoir resulting in peak production profit is selected as an optimum number of the infill drilling plan. Cost of water treatment and disposal is a key economical parameter which controls infill well locations across the reservoir. When cost of water treatment is low, infill wells are mostly located in virgin section of the reservoir where reservoir pressure is high and fracture porosity is low. Water content in fractures does not play a significant role on infill wells selection when water treatment and disposal is a cheap operation. When cost of water treatment is high, infill wells are mostly located on the transition section between virgin and depleted sections of the reservoir to minimize water production.