Evaluation of the representative elementary volume (REV) of a fractured geothermal sandstone reservoir

Abstract

The objective of this study is to evaluate a representative elementary volume (REV) in a fractured geothermal sandstone reservoir in Germany using the discrete fracture network (DFN) model approach. Due to the lack of in situ data from the deep geothermal reservoir, field measurements of outcrop reservoir analogues were conducted to get a quantitative description of the DFN. Field measurements reveal that the geometry of the DFN is largely influenced by the lithological layering (e.g. relationship between joint spacing and bed thickness). Thus, DFN models have to be completed individually for each layer since each bed is distinct compared to its neighbouring beds. Various DFNs were generated using statistical-derived fracture input data. The two-dimensional universal distinct element code UDEC was used for fracture flow simulations. In order to determine the minimum REV, the equivalent average conductivity tensors were obtained with increasing block size. Plots of conductivity tensor coefficients versus block size exhibit only minor variation of the hydraulic properties with increasing block size. Hence, an REV size of 10 m 9 10 m could be determined for all studied stochastically derived discrete fracture networks.

Predictability of properties of a fractured geothermal reservoir: the opportunities and limitations of an outcrop analogue study

“…It is thus inherent that the quality of predictions made on reservoir properties largely depends not only on the distance between exploration wells in the reservoir, but also on the heterogeneity of the latter (e.g. Müller et al 2010;Fitch et al 2015). This holds particularly true for fluvial sedimentary rock sequences that are characterized by frequent changes of rock properties, both laterally and vertically (Morad et al 2010).…”

Integrated Research as Key to the Development of a Sustainable Geothermal Energy Technology

“…[26,30,37] In this context, Zeeb et al [38] introduced the new toolbox called FraNEP to statistically analyze natural fracture data and to enable transformation into more consistentartificial DFNs by using customized fracture network generators. [26] Whether these DFNs are representative of ar eservoiro rn ot depends strongly on the Figure 6.…”

“…Examples representI)aphase field model (PFM) of fracture sealing; [20] II) as ynthetic model [21] of atypical self-affine,rough fracture surface (fractal dimension = 2.2 [22] ); III) alocal cubic law visualization of typicalf low channelsbased on aperture distributions; [23] IV) amedical X-ray computed tomography (CT) scan of af racturedc ore sample; [24] V) lineament interpretationsb ased on remote sensing; [25] and VI) arandom stochasticD FN model. [26] Fracture or DFN studies provide improved permeability predictions of single fractures(analytical, [27] numerical [24,28] )and fracturedg eothermalreservoirs (analytical, [29] numerical [24,25,30] availability of outcrops and subsurface data;this is often provided, for example,b yf ield measurements,t errestrial laser scanning, [39] and remote sensing [40] (Figure6V), as well as aa pplied samplingm ethod, such as scanline or window sampling. [38] In conclusion, the evaluation of fluid flow in fractured reservoirs is am atter of scales,r angingf rom single fracture scales (mmt oc ms cale,m echanically and chemically induced geometries)t ot he field scale (cm to km scale,t ransferability of DFN geometries), the interactions of which have to be better understood to provide more reliable geothermal reservoir models.…”

A new methodology to reduce uncertainty of global attributes in naturally fractured reservoirs

“…Fractures of intermediate scales (fracture swarms) are difficult to model, as they are too numerous to model explicitly and too large to form an equivalent medium at simulator scale Bourbiaux (2010). The definition of the REV for fractured reservoirs is also difficult as highlighted and discussed by several authors (Warren and Root, 1963;Long et al, 1982;Gilman, 2003;Müller et al, 2010;Kuchuk et al, 2015). The size of the REV must be larger than the heterogeneity size and smaller than the macroscopic length-scale, for instance, well spacing (Royer et al, 2002;Gilman, 2003).…”