Geomechanical characterization of hydrocarbon reservoirs using seismic inversion and downhole measurements

The exploitation of hydrocarbon reservoirs requires innovative skills to allow better characterization of potential pay zones. Determining the reservoir's Geomechanical properties such as in-situ stresses and rock strengths is essential for effective reservoir management. It is now widely recognized that they are important for a wide range of objects, from the stability of the borehole to reservoir management and well stimulation issues like where oil producers and water injectors and should be placed. The pore pressure of the reservoir is also another important factor that can affect drilling and well design. When formations are under or over-pressurized, they can cause a lot of problems, like a loss of circulation, unstable boreholes, or a blowout while drilling. These problems can be very dangerous. This study aims to optimize drilling decisions and well planning by quantifying and predicting of geomechanical and petrophysical properties prior to drilling using innovative approaches involving seismic inversion and conventional downhole measurements. A geomechanical earth model is a numerical representation of the mechanical characteristics, strength, and in-situ stresses of rocks in the underground. Direct assessment of these parameters is typically impractical due to the high expense of testing or a lack of relevant data, especially in older wells. Despite the fact that they are quantifiable, they are restricted to a small and constrained region. To calculate these parameters and their fluctuations, surface seismic data were subjected to "elastic inversion," a geophysical procedure that recovers the earth's basic rock characteristics and is a vital element in the development of conventional and unconventional resources. Vertical and lateral variability of rock characteristics have a significant effect on production. Seismic surveys, in this sense, will continue to be a critical component of subsurface prediction of rock characteristics and will help identify their distribution inside the rock. To accomplish this, a hybrid inversion method based on Multi Attribute Analysis (MAA) and Deep Feed-forward Neural Networks (DFNNs) was used to estimate the spatial fluctuation of parameters in inter-well areas. Acoustic impedance (AI) models are generated from pre-stack seismic amplitude data using time domain seismic inversion with density log and velocity parameters. The results indicate that the suggested technique, which combines MAA and DFNNs, enables the optimization of vertical and lateral facies variabilities and the precise prediction of reservoir parameter variations.
The optimal data set for reservoir characterisation is multi-component, broad azimuth, extended offset seismic data. Generally, these information are unavailable. Additionally, it is not uncommon for a wide area to contain only a few wells, with little or scant microseismic, technical, or production data. This dissertation describes an approach and procedure for dealing with data scarcity in these situations. The natural variation in the geomechanical properties of the main reservoirs in the Wellington Oil Field in the south-central Kansas, as well as their stress state, rock mechanical properties, and the relationships with lithology, were investigated by employing a narrow-azimuth seismic volume and merging it with petrophysical logs, core data, and drilling mud reports. The incorporation of many datasets has demonstrated that knowing geomechanical parameters, increasing resolution, and being cognizant of stimulation mechanisms all contribute to the optimization of completions in the Wellington oil field. The methodologies developed in this study can be successfully applied to other areas in south central Kansas with the same burial history and similar geological settings, as well as to any other basin in the world with similar chert, clastic and carbonate reservoirs exhibit downward porosity decline, as shown in this study. The findings of this study show that there is a lot of unexploited potential in terms of efficiency, profitability, and performance.