This study developed new concepts and interpretative methods for mapping reservoir heterogeneity and connectivity of a fault controlled Wilcox clastic reservoir in Texas, USA. The application of high-resolution seismic enhancement in this study allows for better delineation of subsurface geologic features, detailed mapping of reservoir heterogeneities and more accurate identification of depositional, structural, and stratigraphic characteristics that control reservoir connectivity and fluid flow. Seismic enhancement in this study pertains to amplitude preserving neural network implementation of the Volterra integral equation of the first kind from a plane-wave solution of poro-viscoelasticity (Sun, et al., 2003). This enhancement amounts to an advanced spiked deconvolution of post-stack seismic data that broadened the dominant seismic frequency from 16Hz for the conventional seismic to 65Hz for the enhanced seismic. Bed resolution is improved from 175ft to 45ft and fault offset resolution is improved from 80ft to 20ft. High-resolution seismic interpretation was validated through synthetic seismograms, stratigraphic surface comparisons, and most importantly using a comprehensive model-based knowledge of regional tectonics and depositional environments. Stratigraphic features that were not resolvable in conventional seismic data can now be interpreted using the enhanced seismic data. An Upper Wilcox reservoir was identified as a transgressive sheet sand overlaying a progradational deltaic seismic facies. An Upper Middle Wilcox reservoir was identified as a probable lobate gravity flow, and a Middle Wilcox reservoir was identified as a transgressive sheet sand with over and underlying progradational deltaic seismic facies. Geobody extraction from seismic inversion volumes delineates reservoir compartments and flow units. Reservoir connectivity analysis performed on the Middle Wilcox reservoir determined the probable drainage area for a producing well by comparing estimates of compartmentalized hydrocarbon volumes with production information. The methodology developed could help extract connected geobodies defined by sand, porosity, permeability, and hydrocarbon indicators, to map in detail the internal structure of produced reservoir and to locate new development prospects. Enhanced seismic may thus enable us to find bypassed hydrocarbons and to provide better methods for improving recovery in the studied and other mature fields.

Recent efforts to optimize oil recovery from Green River reservoirs, Uinta Basin, have stimulated the need for better understanding of the reservoir connectivity at the scale of the operational unit. This study focuses on Green River reservoirs in the Wells Draw study area where oil production response to implemented waterflood is poor and a better understanding of the reservoir connectivity is required to enhance future secondary oil recovery. Correlating the sand bodies between well locations in the area remains difficult at 40-acre well spacing. Thus, interwell connectivity of the reservoirs is uncertain. Understanding the reservoir connectivity in the Wells Draw study area requires integration of all static and dynamic data for generation of probabilistic models of the reservoir at the interwell locations. The objective of this study is two-fold. The first objective was to determine reservoir connectivity at the interwell scale in the Wells Draw study area. To achieve this goal, I used well log and perforation data in the Wells Draw study area to produce probabilistic models of net-porosity for four producing intervals: (1) Castle Peak, (2) Lower Douglas Creek, (3) Upper Douglas Creek, and (4) Garden Gulch. The second objective was to find readily applicable methods for determining interwell connectivity. To achieve this goal, I used sandstone net thickness and perforation data to evaluate interwell connectivity in the Wells Draw study area. This evaluation was done to: (1) assess and visualize connectivity, (2) provide an assessment of connectivity for validating / calibrating percolation and capacitance based methods, and (3) determine flow barriers for simulation. The probabilistic models encompass the four producing intervals with a gross thickness of 1,900 ft and enable simulation assessments of different development strategies for optimization of oil recovery in the Wells Draw study area. The method developed for determining interwell connectivity in Wells Draw study area is reliable and suited to the four producing intervals. Also, this study shows that the percolation based method is reliable for determining interwell connectivity in the four producing intervals.

Recent efforts to optimize oil recovery from Green River reservoirs, Uinta Basin, have stimulated the need for better understanding of the reservoir connectivity at the scale of the operational unit. This study focuses on Green River reservoirs in the Wells Draw study area where oil production response to implemented waterflood is poor and a better understanding of the reservoir connectivity is required to enhance future secondary oil recovery. Correlating the sand bodies between well locations in the area remains difficult at 40-acre well spacing. Thus, interwell connectivity of the reservoirs is uncertain. Understanding the reservoir connectivity in the Wells Draw study area requires integration of all static and dynamic data for generation of probabilistic models of the reservoir at the interwell locations. The objective of this study is two-fold. The first objective was to determine reservoir connectivity at the interwell scale in the Wells Draw study area. To achieve this goal, I used well log and perforation data in the Wells Draw study area to produce probabilistic models of net-porosity for four producing intervals: (1) Castle Peak, (2) Lower Douglas Creek, (3) Upper Douglas Creek, and (4) Garden Gulch. The second objective was to find readily applicable methods for determining interwell connectivity. To achieve this goal, I used sandstone net thickness and perforation data to evaluate interwell connectivity in the Wells Draw study area. This evaluation was done to: (1) assess and visualize connectivity, (2) provide an assessment of connectivity for validating / calibrating percolation and capacitance based methods, and (3) determine flow barriers for simulation. The probabilistic models encompass the four producing intervals with a gross thickness of 1,900 ft and enable simulation assessments of different development strategies for optimization of oil recovery in the Wells Draw study area. The method developed for determining interwell connectivity in Wells Draw study area is reliable and suited to the four producing intervals. Also, this study shows that the percolation based method is reliable for determining interwell connectivity in the four producing intervals.