Variogram Identification Aided by a Structural Framework for Improved Geometric Modeling of Faulted Reservoirs: Jeffara Basin, Southeastern Tunisia

A Constrained Geostatistical Approach for Efficient Multilevel Aquifer System Characterization

“…A thorough stratigraphic and structural framework reconstruction of the study area was accomplished in order to highlight the existing relations between the aquifer system units [4,[13][14].…”

“…1) were acquired for a seismic structural study of the area [13]. The locations and lineaments of the faults interpreted from seismic data were drawn on the geological map to build the fault network on a large scale (Fig.…”

“…1). Seismic and petroleum well data were examined and interpreted in previous works [13] on the study area to map the prominent fault network.…”

“…The basic tool for spatial correlation analysis is the experimental variogram function [16][17][18][19], which characterizes the spatial structure of the regionalized variable ܼሺ‫ݔ‬ሻ: To be used in the kriging interpolations, the experimental variogram requires fitting by a theoretical definite positive function, taking into account the context of the local and regional geology and the number and the spatial distribution of the available data [13,4,[19][20][21][22][23][24]. The theoretical model of the spatial variability is then used to estimate the random variables' "depth" ("Jurassic Horizon", "Cenomanian Horizon", "Turonian Horizon" and the "Senonian Horizon") at unsampled locations.…”

“…1 and 8); thus, it was difficult to choose the parameters required for calculating the experimental variograms. Many attempts were made to select the best values of the lag distance, tolerance on distance, tolerance on angle direction and maximum distance limit, which are required to identify a reliable spatial correlation function [13,[25][26][27]. Fig.…”