6 | Rock typing of the Kimmeridgian - -Tithonian Reef Complex unit
6.8 Petrophysical rock types
Petrophysical rock types (PRT) are categories of reservoir property values, which should (1) be representative of zones of different reservoir quality, according to property measurements in core samples, (2) have a geological meaning (3) be distinguishable on logs so that petro-physical properties from sample measurements can be upscaled and distributed along wells, and (4) be able to be distributed at the inter-well scale, in order to understand the (geo-logical) factors controlling the geometry of reservoir bodies. Because DdfRTs defined above could not been properly represented by ERTs (condition 3) unfulfilled), simplified PRTs have been newly defined (Figure 6.30).
The definition and distribution of these new PRTs can be considered as a training image for reservoir modelling. When plotting porosity-permeability values measured in plug samples with this new PRTs attribution, clusters of reservoir properties are observed (Figure 6.31), which are close to those defined with DdRTs. Factors controlling the formation and distri-bution of the pore network(s) have been investigated and are now understood. The PRTs can be propagated along wells according to specific (range of) log values, whose relation to rock types defined in cores, at smaller scale, have been properly pinpointed. The lateral distribution of these PRTs has been first constrained by geometrical arrangement observed in outcrops. It shows several meters thick coral and microbial bioherms drowned in 2 to 5
6.8. Petrophysical rock types 175
CTabLandaizeTidalitesComplexe récifal
PRT5: microΦ ≈10% (+residual moldic and vugs?), few fractures
PRT1: marls, no reservoir PRT2: marly limestone, no reservoir
PRT4: limestone, Φ≈5%, (potentially fractured) PRT3: limestone, tight, (potentially fractured)
PRT6: microΦ ≈10%, (+residual moldic and vugs?), densely fractured
PRT7: large fault/fracture zone PRT8: dolomite, intercrystalline Φ ≈12%, or intensively fractured
Figure 6.30: Petrophysical Rock Types (PRT) represented on 2D cross-section.
km-large patches of peri-reef deposits (Meyer, 2000a ; Chatelain and Grosjean, 2004, this study) in the particularly heterogeneous Calcaires récifaux unit. The geological trends high-lighted (ESE mainly) influence the sedimentary evolution through time, the expansion of diagenetic impacts, and potentially permeable structural features. Therefore, based on these PRTs and the geological constraints, a first attempt on static reservoir modelling could be considered.
6.8.1 Comments on uncertainties and upscaling
Uncertainties have been highlighted at different steps of the present rock typing workflow, mainly those affecting the data. Therefore, the workflow has been designed to optimize the available dataset, but also to ensure the integration of future data, which could enhance rock types predictability in terms of reservoir properties.
The data reliability and upscaling process are an important source of uncertainties, as com-monly reported in rock typing studies (Borgomano et al., 2013 ; Kleipool et al., 2015 ; Lallier et al., 2016 ; Bisdom et al., 2017). First, the core material is smaller than expected, and core samples representativity has been questioned. To date, this issue has not been totally solved, because of the great heterogeneity of the studied carbonate reservoir, and the overall lack of subsurface data. However, it has been addressed with a meticulous review of the literature on outcrop studies of the Reef Complex unit, in order to integrate complementary outcrop samples, and better define the sequence sedimentary framework within the basin. Indeed, better constraining sedimentary sequences can be of great support to evaluate lateral
het-176 Chapter 6. Rock typing of the Kimmeridgian - Tithonian Reef Complex unit
CTabLandaizeTidalitesComplexe récifal
PRT5: microΦ ≈10% (+residual moldic and vugs?), few fractures
PRT1: marls, no reservoir
PRT2: marly limestone, no reservoir PRT4: limestone, Φ≈5%, (potentially fractured) PRT3: limestone, tight, (potentially fractured)
PRT6: microΦ ≈10%, (+residual moldic and vugs?), densely fractured
PRT7: large fault/fracture zone PRT8: dolomite, intercrystalline Φ ≈12%, or intensively fractured
Figure 6.31: Porosity-permeability measurements, with Petrophysical Rock Types (PRT) having been attributed to each sample.
erogeneities at the inter-well scale and the distribution of sedimentary bodies (Borgomano et al., 2008).
Then, the different log devices used and the poor quality of log signals have resulted in a highly heterogeneous log dataset, which has limited the well log interpretation to simple calculations and qualitative comparisons of log curves. Nevertheless, reservoir properties have been tentatively upscaled, but the number of petrophysical rock types had to be reduced, in order to honour better the data rather than concepts. Consequently, resulting rock types and predicted reservoir properties correspond to a pessimistic scenario of the reservoir quality.
But they have to be considered as a first attempt according to the available dataset, as long as no larger effective matrix pore network has been verified, and fracture effect on permeability quantatively assessed.
In order to improve confidence in upscaling matrix petrophysical properties from the well-defined micro- and cm-scale up to several meters, the following analyses are strongly rec-ommended. CT-scan image analysis on core sections would help deciphering if vugs and larger moulds evidenced in reef-related facies could be connected at a larger scale than the plug dimension (Youssef et al., 2007). This method should be especially applied on peri-reef deposits, in which the occurrence of larger, poorly sorted grains could likely result in various pore types, and a more complex pore network than micropores only (Lucia, 1995 ; Borgomano et al., 2013). Image log and NMR log could also be used to upscale rock types based on cores and core samples, the latter logs can be related to exclusive attributes recognizable along wells, such as textural, mineralogical or pore characteristics. These complementary analyses would help to characterize better sedimentary-diagenetic influence on reservoir properties, and the distribution of potentially enhanced matrix properties.
Complementary investigations on fracture attributes are also required, in order to quantify
6.9. The South German Molasse Basin: a geothermal reservoir analogue? 177 better the permeability in potentially enhanced fractured zones. Outcrop analyses on stress orientation and fault mineralization in the GGB are actually being processed (Cardello et al., in prep.), which should be completed by focussed characterisation of fractures, and their density in the Reef Complex. It would greatly improve our knowledge of such unit, since fracture networks often appear at sub-seismic scale, and are not fully represented on plug nor on log data (Bisdom et al., 2017). This would provide another step towards the definition of flow units, which are related to permeability assessment at the reservoir scale. Considering the available material, these flow units could not be confidently defined. However, the geological constraints influencing flow units, and the dual permeability behaviour have been addressed. These are first key parameters to assess the permeability anisotropy, which have to be completed with dynamic parameters, only provided confidently by flow tests in wells.
Improving the reliability of the rock type definition with additional data and analyses would confirm the present rock type definition. It would allow the validation of their susceptibility to predict reservoir properties in space, also through mathematical approach (Borgomano et al., 2013 ; Makhloufi, 2013 ; Skalinski and Kenter, 2015).