An overview of coalbed methane (CBM) reservoir characteristics, its unique production mechanisms, and the influence of geomechanical processes on these production mechanisms are discussed from a reservoir engineering point of view. Models have been developed to predict changes in cleat porosity and permeability with in situ conditions (stress, pressure, gas sorption, and temperature). Explicit-sequential coupling simulations are conducted for conventional CBM depletion production. The study shows that during production, coal matrix shrinkage due to methane desorption results in an increase of permeability within coal seams in most regions close to the producer even though the mean effective stresses increase. The predicted production rate and cumulative production from explicit-sequential coupling simulations are higher than that from conventional simulations. The developed models also allow coalbed permeability anisotropy to be considered. Introduction Worldwide coalbed methane (CBM) reserves have been estimated at 84 ~ 262 trillion m3 (2,980 ~ 9,260 trillion ft3)(1). The majority of these CBM reserves are mainly located in Russia (17 ~ 113 trillion m3), Canada (6 ~ 76 trillion m3), China (30 ~ 35 trillion M3), Australia (8 ~ 14 trillion m3), and USA (11 trillion m3)(1). In the United States, CBM accounted for 10% of dry gas reserves and 8% of dry gas production in 2003(2). In other countries, such as China, Canada, and Australia, CBM projects are attracting more and more attention by resource companies. The production methods of CBM include conventional pressure depletion production and enhanced coalbed methane (ECBM) recovery. At present, CBM is mainly recovered by the former method. In ECBM, gases such as N2, CO2, or flue gas are injected to displace methane and maintain coalbed pressure. This recovery method is still in its infancy with only two field-scale ECBM projects (one injected N2 and the other injected CO2)(3), and one singlewell pilot project(4) worldwide. Productivity evaluation and prediction are important steps in the development of CBM reservoirs. Because gas storage mechanisms in coal seams (mainly adsorbing on the walls of pores) are different from that in conventional gas reservoirs (compressed in pores), conventional reservoir simulators generally do a poor job in predicting CBM production. Over the past decade, many models have been developed to characterize CBM production processes (5–7). Commercial simulators for CBM production can be categorized into two types: modified conventional black oil simulators and modified compositional simulators. With the recognition of the stress dependency of coal permeability and porosity and shrinkage/swelling of the coal matrix due to desorption/adsorption, some simulators have been modified to accommodate these characteristics(3). However, in these simulators the influence of in situ stresses is simplified with an analytic model or a monotonic relation between the permeability ratio and pressure changes. Durucan et al. developed a finite element model to simulate the in situ stress changes near wellbores and coupled the stress changes with fluid flow simulation by characterizing dynamic changes in permeability(8).
A new approach is presented for interpreting low level Langmuir probe measurements in terms of physical plasma parameters such as density or temperature. Instead of relying on analytic expressions as in most analyses, the method uses regressions combined with a suitably prepared solution library consisting of precomputed probe characteristics for selected plasma parameters. In machine learning language, this amounts to generating a training data set, constructing and training a model, and validating it over a domain of physical parameters of interest. This study aims at establishing the feasibility and limits of the method by using synthetic data sets that can be generated quickly from analytic approximations. The ultimate goal is to use this approach with model training on data sets constructed with detailed kinetic simulations capable of accounting for more physical processes, and more realistic geometry, than are possible with analytic models.
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