Stochastic reservoir simulation for the modeling of uncertainty in coal seam degasification

Abstract

Coal seam degasification improves coal mine safety by reducing the gas content of coal seams and also by generating added value as an energy source. Coal seam reservoir simulation is one of the most effective ways to help with these two main objectives. As in all modeling and simulation studies, how the reservoir is defined and whether observed productions can be predicted are important considerations. Using geostatistical realizations as spatial maps of different coal reservoir properties is a more realistic approach than assuming uniform properties across the field. In fact, this approach can help with simultaneous history matching of multiple wellbores to enhance the confidence in spatial models of different coal properties that are pertinent to degasification. The problem that still remains is the uncertainty in geostatistical simulations originating from the partial sampling of the seam that does not properly reflect the stochastic nature of coal property realizations. Stochastic simulations and using individual realizations, rather than E-type, make evaluation of uncertainty possible. This work is an advancement over Karacan et al. (2014) in the sense of assessing uncertainty that stems from geostatistical maps. In this work, we batched 100 individual realizations of 10 coal properties that were randomly generated to create 100 bundles and used them in 100 separate coal seam reservoir simulations for simultaneous history matching. We then evaluated the history matching errors for each bundle and defined the single set of realizations that would minimize the error for all wells. We further compared the errors with those of E-type and the average realization of the best matches. Unlike in Karacan et al. (2014), which used E-type maps and average of quantile maps, using these 100 bundles created 100 different history match results from separate simulations, and distributions of results for in-place gas quantity, for example, from which uncertainty in coal property realizations could be evaluated. The study helped to determine the realization bundle that consisted of the spatial maps of coal properties, which resulted in minimum error. In addition, it was shown that both E-type and the average of realizations that gave the best match for invidual approximated the same properties resonably. Moreover, the determined realization bundle showed that the study field initially had 151.5 million m3 (cubic meter) of gas and 1.04 million m3 water in the coal, corresponding to Q90 of the entire range of probability for gas and close to Q75 for water. In 2013, in-place fluid amounts decreased to 138.9 million m3 and 0.997 million m3 for gas and water, respectively.

Numerical Simulation Research on Well Pattern Optimization in High–Dip Angle Coal Seams: A Case of Baiyanghe Block

“…We used the CMG-GEM simulator to build the numerical model and generated the optimization scenarios of development. The GEM is a three-dimensional compositional simulator capable of simulating the sorption, diffusion, dual-porosity, and singlepermeability flowing of CBM in coal, which is widely used in CBM development engineering studies (Karacan and Olea, 2015;Zhang et al, 2020).…”

Estimating permeability in shales and other heterogeneous porous media: Deterministic vs. stochastic investigations

“…For instance, Karacan et al implemented sequential Gaussian simulation (SGSIM) and sequential Gaussian co-simulation (co-SGSIM) techniques to generate stochastic realizations of coal. 48 We implemented a previously reported stochastic MC sampling technique, to generate our 2D pore networks, 27,49,50 and we also used a stochastic method (Method III) to simulate the fluid transport of the gas particles (KMC) through the generated pore networks. The time step used for the KMC sampling was set at 100 ns (see Appendix A; supplementary data for further details).…”

Stochastic Modeling for Estimating Coalbed Methane Resources

“…For instance, the sequential Gaussian simulation (SGS) and ordinary Kriging methods were adopted by Beretta et al, while the semivariogram method was used by Pardo-Igúzquiza et al and Saikia et al In addition, Olea et al combined several geostatistical approaches to quantify the uncertainty in the coal thickness distribution . By combining stochastic geological modeling and history matching, Karacan and Olea selected the most probable realization for determining the coal thickness …”

“…8 By combining stochastic geological modeling and history matching, Karacan and Olea selected the most probable realization for determining the coal thickness. 9 The coal density distribution is mainly affected by the coal density cutoff value setting. According to the CBM industry, the standard value is 2.0 g/cm 3 .…”