Thermally enhanced gas recovery and infill well placement optimization in coalbed methane reservoirs.

Abstract

The aim of this thesis is to investigate innovate approaches that can help to improve methane recovery and production rate from coalbed methane (CBM) reservoirs. The results of two following subjects are presented and discussed. First, thermally enhanced gas recovery from gassy coalbeds is introduced. Second, an integrated reservoir simulation-optimization framework is developed and employed to optimize infill well locations across coalbed reservoirs. When coalbed methane and geothermal activities coexist in the same field, coalbeds can be thermally treated prior to the gas production using available underground geothermal resources. Feasibility of this method is investigated both using methane sorption tests on Australian coal samples at different temperatures and also reservoir simulation. The impact of temperature elevation on methane sorption and diffusion in coal is investigated by running sorption experiments on two the Australian coal samples using a manometric adsorption apparatus. Experiments are performed to indicate that how the difference between original reservoir pressure and critical desorption pressure is decreased at elevated reservoir temperatures. Lower pressure gradient is required to extract methane from coalbed when it is thermally treated prior to gas production. Following the experimental study, the feasibility of thermally enhanced gas production from coalbeds is studied by coupling of coalbed methane and thermal simulators. The coalbed methane simulator of Computer Group Modelling (CMG) and the thermal simulator of CMG known as STARS are loosely coupled to study the effect of temperature elevation on total gas and water production. Both gas rate and ultimate gas recovery from the reservoir are increased by thermal operation. In the second part of this thesis, an integrated reservoir simulation-optimization framework is developed to intelligently obtain locations of new infill wells in a way to maximize profitability of the infill plan. This framework consists of a reservoir flow simulator (Eclipse E100), an optimization method (genetic algorithm), and an economic objective function. The objective function in this framework is to maximize discounted net cash flow of infill project. The importance of optimization is magnified when cost of water treatment is increased. When optimization approach is compared with standard five spot pattern well arrangements, the impact of water treatment cost is observed. When cost of water treatment is high, there is a large difference between the profit of the infill project calculated using the optimization approach and the standard five spot pattern. Simulation results indicate that at higher cost of water treatment, infill wells are preferably located either on the front of the water depletion zone or close to existing wells. On the other hand, when water treatment cost is low, infill wells are located in virgin sections of the coalbed where both gas content and cleat water saturation are high.