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.

Keywords: Coal seam gas; Geostatistics; History matching; Mean square error; Realization; Sequential simulation.

Fig. 1

Location of study area with the coalbed methane wells. General geology of the Carbondale group where Seelyville coal is located and the boundaries and size of the modeled area are also shown.

Fig. 2

Measured bottom-hole pressure, gas, and water rate data of 5 of the studied wells between July 2007 and October 2013.

Fig. 3

Simulated gas and water production rates for all 100 simulations (light-colored lines) in comparison with the measured data for Hall-1. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 4

Results of the match between simulated rates and the measured ones using realization bundles 64 and 38 for gas rate and 72 and 81 for water rate, which gave minimum and maximum errors for Hall-1.

Fig. 5

Distribution of average history matching errors (based on gas rate) calculated using Eq. (2).

Fig. 6

Comparison of predicted gas and water rates using realization 69, E-type, and average of best realizations (BR in the legends) with the measured values for four of the wells.

Fig. 7

Histograms of initial gas-in-place and water within the model area.

Fig. 8

Maps of three different coal attributes for real 69, E-type, and average of best realizations (BR).

Fig. 9

Maps of pressure distributions in 2007 and 2013 in real 69, E-type, and average of best realizations.