Sequential Gaussian co-simulation of rate decline parameters of longwall gob gas ventholes
Highlights
► Geostatistical co-simulation results for gob gas ventholes (GGVs) were presented for decline parameters. ► GGVs located at lower elevations, i.e., at the bottom of valleys, tend to perform better in terms of their rate declines. ► Geostatistical simulation results were used to calculate drainage radii of GGVs using gas-in-place realizations. ► Calculated drainage radii were close to ones predicted by pressure transient tests.
Introduction
Drilling gob gas ventholes (GGVs) in longwall mining panels is a common technique to control methane emissions, allowing for the capture of methane within the overlying fractured strata before it enters the work environment during mining. The usual practice is to drill the GGVs prior to mining and locate a slotted casing in the zone that is expected to fracture (fractured zone). As mining advances under the venthole, the strata that surround the well deform and establish preferential pathways for the released methane, mostly from the coal seams within the fractured zone, to flow towards the ventholes [1]. The properties of fractured zones, mainly permeability, are determined through conventional pressure- and rate-transient well test analyses techniques that are used systematically and routinely for oil and gas [2], [3], [4], [5], [6]. Results showed that permeabilities of bedding plane separations can be as high as 150 Darcies, with average permeabilities (including fractures and intact formations) within the slotted casing interval of GGVs varying between 1 Darcy and 10 Darcies [7], [8], [9].
GGVs are equipped with exhausters on the surface to provide negative pressure to produce methane from highly permeable fractured zones with a rate and concentration depending on various additional factors besides permeability [10], [11]. The production life-span of GGVs may be long or short, depending on mining, borehole drilling, and location as well as operating conditions, but usually follows a declining trend with time [8] until the exhausters are shut down as a safety measure against explosion risk, when the methane concentration in the produced gas decreases to approximately 25%.
It is difficult to predict production performance of GGVs due to the involvement of multiple factors [10], [12]. In addition to complexities given in these studies, boreholes may deform under mining stresses and strata displacements [13], [14], [15], making production predictions even more difficult. Studies presented in [7], [12] do not take borehole stability issues into account while predicting GGV performance. However, Karacan [10] presented a sensitivity analysis of variables on total flow rate and methane percentage of gas produced from GGVs. The sensitivity analyses showed that, when considering the overall performance of GGVs for methane production rate, the most important variables were 1, whether or not face is advancing, 2, surface elevation of the venthole (above sea level), 3, overburden, 4, casing diameter, 5, distance of the venthole to the tailgate, and 6, distance of venthole to panel start.
Multiple factors studied in [10] and then improved in [11] were formulated as a multi-layer-perceptron (MLP) type neural network to predict GGV production performance. This module is part of MCP 2.0-Methane Control and Prediction software, v.2 [16] for prediction and sensitivity analyses purposes. Version 1.3 of this software is briefly discussed [17].
Despite the improvements for understanding the effects of various factors on GGV production and for predicting GGV performance, there are GGVs that perform much better or worse than expected in terms of methane production rate and production longevity. Although these unexpected production behaviors may be due to borehole stability issues, as mentioned before, they can also be related to spatial location of the borehole and how it interacts with other important production-influencing factors at that particular position. In other words, if there is a spatial correlation or stochastic dependency between borehole location, its rate transient, and other potentially influencing factors, the analyses should involve the geographical location of the boreholes, necessitating geostatistical methods.
Geostatistical methods, some of which are described in detail in [18], [19], [20], [21], [22], have wide applications in geology, environmental studies, mining research, and petroleum engineering [23], [24], [25], [26], [27], [28]. More recently, Olea et al. [29] have developed a formulation of a correlated variables methodology and co-simulation for assessment of gas resources in Woodford shale play, Arkoma basin, in eastern Oklahoma.
The aim of this paper is to explore the possibility of modeling the attributes of decline curve analyses (DCA) conducted on gob gas ventholes by taking into account borehole locations and potential correlations between surface elevations at the wellheads. Geostatistical stochastic co-simulation methods were used to map the distribution of decline curve attributes. In addition, cell-based DCA parameters were interpreted with the GIP in the fractured zone to estimate radii of drainage area of GGVs.
Section snippets
Site location and description of area in relation to correlations with gob gas venthole production
The longwall mining site studied in this paper is in the Pittsburgh coal, Monongahela Group, southwestern Pennsylvania. The Monongahela Group includes sandstone, siltstone, shale and commercial coal beds and occurs from the base of the Pittsburgh coal bed to the base of the Waynesburg coal bed. Thickness within the general study area ranges from 270 to 400 ft. The Pittsburgh coal seam is unusually continuous and covers more than 5000 square miles [30], making longwall mining technique highly
Production data and analyses methodology
Decline curve analysis is a rate transient test procedure used for analyzing declining production rates and forecasting future performance of wells. In this paper, Fekete's rate transient analysis (RTA) [35] software was used to analyze declining GGV production performances using both traditional decline approaches and Fetkovich type curves.
In decline curve analysis, it is implicitly assumed that the factors causing the historical decline continue unchanged during the forecast period. These
Surface elevation data and modeling of gas-in-place in fractured zone
The mining district modeled in this work (Fig. 1) hosted Pittsburgh seam panels 1250 ft wide initially (the first two panels), with wider panels (1450 ft) starting from the 3rd panel. Panel lengths were generally 12,000–13,000 ft in length. The dimensions of the area shown in Fig. 1 are 8624 ft in the y-direction (Northing) and 17,325 ft in the x-direction (Easting). In this district, overburden depths ranged between 700 and 1000 ft. This area was modeled in a 100×50 (Easting-Northing) Cartesian grid
Selection of primary variables
In this paper, the potential of geostatistics to model decline curve attributes of a limited number of GGVs is sought by utilizing the location of wells and by considering the correlation potential of DCA attributes with surface elevation of wells. For this purpose, surface elevation data shown in Fig. 4 are used as the secondary variable in co-simulations.
The next step was to identify which DCA parameters could be selected as primary variables of co-simulations. In order to determine these
Co-simulated realizations of primary variables using cell-based evaluation
Co-simulations using the MM1 approach were performed to generate 100 realizations for each of the primary variables. The realizations of DCA parameters co-simulated with surface elevation shown in Fig. 4 can be used in a variety of ways to improve the understanding of rate decline properties of GGVs drilled at different locations. One of the most useful applications of all 100 realizations from each of the parameters is to calculate local probability above certain thresholds. With this
Summary and conclusions
In this paper, geostatistical analysis was pursued using sequential Gaussian co-simulation to characterize decline curve analyses (DCA) of gob gas ventholes in combination with GIP in fractured zone and surface elevation. Surface elevation was selected as the secondary variable, while various attributes of DCA were treated as primary variables. GIP was also simulated with sequential Gaussian technique, used in conjunction with decline curve results to determine the drainage radii and production
Acknowledgments
We are grateful for the reviews as part of the internal approval process by our institutions: Gerrit Goodman on the NIOSH side, and Leslie F. Ruppert (USGS) and Michael Ed. Hohn (West Virginia Geological and Economic Survey) for the U.S. Geological Survey. We also thank Chris Garrity (U.S. Geological Survey) for providing the high-resolution surface elevation data.
Disclaimer: The findings and conclusions in this paper are those of the authors and do not necessarily represent the views of the
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