Stable dispersions of magnetic nanoparticles that are already in use in biomedicine as image-enhancing agents, also have potential use in subsurface applications. Surface-coated nanoparticles are capable of flowing through micron-size pores across long distances in a reservoir with modest retention in rock. Tracing these contrast agents using the current electromagnetic tomography technology could potentially help track the flood-front in waterflood and EOR processes and characterize the reservoir. The electromagnetic (EM) tomography used in the petroleum industry today is based on the difference between the electrical conductivity of reservoir fluids as well as other subsurface entities. The magnetic nanoparticles that are considered in this study, however, change the magnetic permeability of the flooded region, which is a novel application of the existing EM tomography technology. As the first fundamental step, the magnetic permeability change in rock due to injecting magnetic nanoparticles is quantified as a function of particle and reservoir properties. Subsequently, a new formulation is devised to compute the sensitivity of magnetic measurements to magnetic permeability perturbations. The results are then compared with the sensitivity to conductivity perturbations to identify the application space of magnetic contrast agents. Using numerical simulations, the progress of magnetic nanoparticle bank is monitored in the reservoir through time-lapse magnetic tomography measurements that are expected. Initially, simple models for displacement of injection banks are assumed and the level of complexity is gradually increased to incorporate the realities of fluid flow in the reservoir. The fluid-flow behavior of the nanoparticles is dynamically integrated with time-lapse magnetic response. Since the nanoparticles could help illuminate the flow paths, they could be used to indirectly measure reservoir heterogeneities. Therefore, numerous case studies are demonstrated where reservoir heterogeneity could potentially be inferred. Finally, fundamental pore-scale models are developed as a first step towards the multiple fluid phases extension of the EM tomography application. Using magnetic nanoparticles to improve electromagnetic tomography provides several strategic advantages. One key advantage is that the magnetic nanoparticles provide high resolution measurements at very low frequencies where the conductivity contrast is hardly detectable and casing effect is manageable. In addition, the sensitivity of magnetic measurements at the early stages of the flood is significantly improved with magnetic nanoparticles. Moreover, the vertical resolution of magnetic measurements is significantly enhanced with magnetic nanoparticles present in the vicinity of source or receiver. The fact that the progress of the magnetic slug can be detected at very early stages of the flood, that the traveling slug’s vertical boundaries can be identified at low frequencies, that the reservoir heterogeneities could potentially be characterized, and that the magnetic nanoparticles can be sensed much before the actual arrival of the slug at the observer well, provides significant value of using magnetic contrast agents for reservoir illumination. / text

One of the most challenging tasks in the oil industry is the production of reliable reservoir forecast models. Because of different sources of uncertainties the numerical models employed are often only crude approximations of the reality. This problem is tackled by the conditioning of the model with production data through data assimilation. This process is known in the oil industry as history matching. Several recent advances are being used to improve history matching reliability, notably the use of time-lapse seismic data and automated history matching software tools. One of the most promising data assimilation techniques employed in the oil industry is the ensemble Kalman filter (EnKF) because its ability to deal with highly non-linear models, low computational cost and easy computational implementation when compared with other methods. A synthetic reservoir model was used in a history matching study designed to predict the peak production allowing decision makers to properly plan field development actions. If only production data is assimilated, a total of 12 years of historical data is required to properly characterize the production uncertainty and consequently the correct moment to take actions and decommission the field. However if time-lapse seismic data is available this conclusion can be reached 4 years in advance due to the additional fluid displacement information obtained with the seismic data. Production data provides geographically sparse data in contrast with seismic data which are sparse in time. Several types of seismic attributes were tested in this study. Poisson’s ratio proved to be the most sensitive attribute to fluid displacement. In practical applications, however the use of this attribute is usually avoided due to poor quality of the data. Seismic impedance tends to be more reliable. Finally, a new conceptual idea was proposed to obtain time-lapse information for a history matching study. The use of crosswell time-lapse seismic tomography to map velocities in the interwell region was demonstrated as a potential tool to ensure survey reproducibility and low acquisition cost when compared with full scale surface surveys. This approach relies on the higher velocity sensitivity to fluid displacement at higher frequencies. The velocity effects were modeled using the Biot velocity model. This method provided promising results leading to similar RRMS error reductions when compared with conventional history matched surface seismic data.