The variations of the electromagnetic fields caused by changes of the fluid distribution are measured in a wide range of distances from the well. The data are processed and interpreted through an integrated software platform that combines 3D and 4D geophysical data inversion with a machine-learning (ML) platform.

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SwissCognitiveThe complete paper clarifies the details of the ML work flow applied to electrical resistivity tomography (ERT) models using an example based on synthetic data.


An important question in well completions is how one may acquire data with sufficient accuracy for detecting the movements of the fluids in a wide range of distances in the space around the production well. One method that is applied in various Earth disciplines is time-lapse electrical resistivity. The operational effectiveness of ERT allows frequent acquisition of independent surveys and inversion of the data in a relatively short time. The final goal is to create dynamic models of the reservoir supporting important decisions in near-real-time regarding production and management operations. ML algorithms can support this decision-making process.


In a time-lapse ERT survey [often referred to as a direct-current (DC) time-lapse survey], electrodes are installed at fixed locations during monitoring. First, a base resistivity data set is collected. The inversion of this initial data set produces a base resistivity model to be used as a reference model. Then, one or more monitor surveys are repeated during monitoring. The same acquisition parameters applied in the base survey must be used for each monitor survey. The objective is to detect any small change in resistivity, from one survey to another, inside the investigated medium.

As a first approach, the eventual variations in resistivity can be retrieved through direct comparison between the different inverted resistivity models. A different approach is called difference inversion. Instead of inverting the base and monitor data sets separately, in difference inversion, the difference between the monitor and base data sets is inverted. In this way, all the coherent inversion artifacts may be canceled in the difference images resulting from this type of inversion.

Repeating the measurements many times (through multiple monitor surveys) in the same area and inverting the differences between consecutive data sets results in deep insight about relevant variations of physical properties linked with variations of the electric resistivity.


The Eni reservoir electromagnetic monitoring and fluid mapping system consists of an array of electrodes and coils (Fig. 1) installed along the production casing/liner. The electrodes are coupled electrically with the geological formations. A typical acquisition layout can include several hundred electrodes densely spaced (for instance, every 5–10 m) and deployed on many wells for long distances along the liner. This type of acquisition configuration allows characterization, after data inversion, of the resistivity space between the wells with relatively high resolution and in a wide range of distances. The electrodes work alternately as sources of electric currents (Electrodes A and B in Fig. 1) and as receivers of electric potentials (Electrodes M and N). The value of the measured electric potentials depends on the resistivity distribution of the medium investigated by the electric currents. Consequently, the inversion of the measured potentials allows retrieval of a multidimensional resistivity model in the space around the electrode array. This model is complementary to the other resistivity model retrieved through ERT tomography. Finally, the resistivity models are transformed into fluid-saturation models to obtain a real-time map of fluid distribution in the reservoir.

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The described system includes coils that generate and measure a controlled electromagnetic field in a wide range of frequencies.

Synthetic Tests

The geoelectric method has proved to be an effective approach for mapping fluid variations, using both surface and borehole measurements, because of its high sensitivity to the electrical resistivity changes associated with the different types of fluids (fresh water, brine, hydrocarbons). In the specific test described in the complete paper, the authors simulated a time-lapse DC tomography experiment addressed to hydrocarbon reservoir monitoring during production.

A significant change in conductivity was simulated in the reservoir zone and below it because of the water table approaching four horizontal wells. A DC cross-hole acquisition survey using a borehole layout deployed in four parallel horizontal wells located at a mutual constant distance of 250 m was simulated. Each horizontal well is a constant depth of 2340 m below the surface. In each well, 15 electrodes with a constant spacing of 25 m were deployed. […]


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