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Real-time mapping reservoir fluids distribution during hydrocarbon production, or during injection operation, represents a crucial issue and a big challenge at the same time. In this article, we present a new approach based on single-well and cross-well electric measurements. We use electrodes permanently installed on the well casing and electrically insulated from it. We tested our approach through a two-steps workflow. In the first step, we performed forward and inverse modelling on realistic production scenarios. In the second step, we acquired, processed and inverted real data acquired in laboratory, where we tested small-scale scenarios of hydrocarbon production. We acquired and inverted DC (Direct Current) data. Our objective was to reconstruct the variations of the 3D distribution of electric resistivity during the various phases of oil production. The retrieved models reproduced properly the experimental movements of fluids observed in our lab measurements. Finally, modelling and inversion of both synthetic and real data confirm that cross-hole DC method allows mapping reservoir fluid variations even in case of predominant metallic components of the well completion.

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In this paper, we introduce a new technology permanently installed on the well completion and addressed to a real time reservoir fluid mapping through time-lapse electric/electromagnetic tomography while producing and/or injecting. Our technology consists of electrodes and coils installed on the casing/liner in the borehole/reservoir section of the well. We measure the variations of the electromagnetic fields caused by changes of the fluid distribution in a wide range of distances from the well, from few meters up to hundreds meters. The data acquired by our technology are processed and interpreted through an integrated software platform that combines 3D and 4D geophysical data inversion with a Machine Learning platform equipped with a complete suite of classification/prediction algorithms. Every time new data are acquired, they are fully integrated with the previous database, and used for decreasing the level of uncertainty about the dynamic model of the reservoir. In order to clarify the potential impact of such system on reservoir management, we apply this methodology to a synthetic data set. We discuss a simulation of a scenario where the waterfront approaches the wells during oil production. The goal of our test is to show how to combine our technology with Machine Learning to make robust predictions about the water table variations around the production wells.

One of the main challenges in the oil and gas industry is mapping hydrocarbons and other fluids in the reservoir in real-time during production or injection. In facts, knowing how the distribution of hydrocarbons and of water changes over time and space, represents a fundamental requirement for optimizing the management of the reservoir and for maximizing the recovery factor. Having these goals in mind, in this paper we present the basic concepts and the architecture of the E-REMM system (Eni-Reservoir Electro-Magnetic Mapping) developed by Eni S.p.A. This is a borehole electromagnetic methodology, fully integrated with surface electromagnetic methods, aimed at providing real-time mapping of the reservoir fluids distribution during production or injection. The E-REMM system uses electrodes and coils installed permanently in the well completion in order to perform a ‘quasi-continuous’ EM survey. The data are transmitted to the surface and transformed into resistivity models. These are interpreted in terms of fluid distribution in the reservoir, using empirical relationships between resistivity and fluid saturation. This approach is able to provide important benefits in terms of production optimization and reservoir characterization, consequently delivering significant technical and economic advantages. The main advantages expected from the E-REMM System include: optimization of production management, thanks to a better mapping of the fluids in the reservoir; maximization of the hydrocarbons Recovery Factor, thanks to a better planning of the recovery strategy; reduction of operational expenditures (OPEX), such as the reduction of water shut-off intervention and the reduction of the disposal costs for the produced water. Furthermore, the application of the E-REMM system allows reducing the Capital expenditures (CAPEX), thanks to a "modular" approach to the asset development strategy. Finally, combining E-REMM with surface layouts can allow identifying bypassed hydrocarbons accumulations, supporting the processes of near-field exploration and in-field appraisal.