An Integrated Reservoir Properties Characterization And Modelling

This dissertation aims at improving the prediction of reservoir properties from seismic amplitude by integrating geo-statistical modeling principle, extended elastic impedance and rock physics models. This general objective contains four specific goals: 1) To identify the predictable spatial trends of reservoir properties using geo-statistical modeling frame work:2) To link properties with seismic velocities using extended elastic impedance inversion:3) To link reservoir properties with seismic velocities through rock physics models and 4) To improve the understanding of seismically derived signatures of reservoir properties, depositional processes as well as digenetic process in the west African deep water environment.In order to achieve these aims, this study attempted to resolve two primary problems as follows;1. How possible can reservoir properties be derived away from well control using the concept of extended elastic impedance inversion? And what do we see when compared with geo-statistically derived reservoir properties?2. How can we resolve the problem of under-determined system of equations when converting seismic properties to reservoir rock properties and vice-versa?In order to resolve the first problem, Geo-statistics was applied on plutonio field so as to predict the trend of reservoir properties, this was carried out for the purpose of having a bases for comparison with a subsequent model to be developed using extended elastic impedance. The geostatistical modeling process began with petrophysical analysis thereby generating a cut off criteria for reservoir properties, then followed by a Structural model that integrates the stratigraphic framework and fault system of the reservoirs, sedimentary microfacies (or sand bodies) modelling on the basis of drilling and logging data, isochronous stratigraphic correlation unit (as the framework), dynamic integration of parameters and characteristics of channel sand bodies as well as analysis of variation function. It went ahead to apply facies models as basis for reservoir property distribution by determining a rational model on the basis of stochastic simulation.Again, deterministic seismic inversion and extended elastic impedance (EEI) were applied on the same area so as to obtain quantitative estimates of reservoir properties. To achieve this, the optimum EEI angles corresponding to reservoir properties were analyzed using well logs data, together with a lithology indicator. Pre-stack seismic data were simultaneously inverted into density, acoustic and gradient impedances cubes, through model based inversion algorithm. The last two broadband inverted volumes were projected to the Chi angles corresponding to petrophysical indicators, resulting to two broadband EEI volumes. At Well location, the EEI versus petrophysical parameters linear trends were then used to transform EEI volumes into quantitative porosity and shale volumes based on a specific lithology. In order to obtain the reservoir facies distribution, a background EEI was established based on an identified minimum energy angle, thereby enabling the mapping of reservoir facies from quantitative porosity, shale content and background EEI cubes. A comparison was made between the properties generated through EEI method with the initial geo-statistical method based properties, which shows that EEI can be applied for the purpose of mapping favorable zones that may suggest possible future drilling locations.The second problem was resolved through rock physics modeling and diagnostics. During reservoir characterization process, incorporating lithology and fluid mixtures in rock physics models so as to estimate rock properties is a challenging task because it leads to an underdetermined system of rock physics equations, which necessitate the application of complex mathematics. The possibility of overcoming this difficulty can be achieved when the numbers of unknowns in the equation are reduced. This thesis made its contribution by making use of an additional petrophysical link, the one between porosity and clay content which is common on a dispersed shale environment. To achieve its goal, firstly, a rock physics model was established, and then the reservoir was delineated through a combination of P-wave impedance and poisson’s ratio. In the reservoir, total porosity and clay content were inverted based on P-wave impedance by applying the rock physics model that relates P-wave impedance to porosity and clay content, alongside the established petrophysical link between total porosity and clay content. The result was found to be consistent on the well log scale. Uniquely, a good match was obtained when the methodology was repeated on an up-scaled well log data, thereby presenting its behavior on the seismic scale.