Systems and methods for electromagnetic waterfront surveillance in a vicinity of an oil well

A real-time monitoring system to detect and spatially-position a waterfront in a reservoir comprising:

• a surface-to-borehole electromagnetic (“SBEM”) acquisition system comprising a plurality of electric dipoles formed on the surface, the SBEM acquisition system operable to inject currents having different frequencies into the earth and measure the resulting vertical electrical fields associated with the different frequency currents;
• a data processing system operable to process the received measured vertical electric fields to enhance a signal-to-noise ratio of the measured vertical electric fields;
• a machine learning data analysis system operable to detect patterns within the measured vertical electric fields and predict movement of the waterfront within the reservoir comprises a deep learning system operable to receive:
• the measured vertical electric field measurements taken at different points in time, the vertical electric field measurements arranged in an EM data hypercube for point in time, and
• a reservoir model for each point in time, and
• wherein the machine learning data analysis system is operable to predict movement of the waterfront in the reservoir based on predicted patterns using regressions between the reservoir models and the EM data hypercubes;
• and
• an alarm and control system operable to signal an alarm when a position of the waterfront is predicted to be a predetermined distance from the well.

A computer program product encoded on a non-transitory medium, the product comprising computer readable instructions for causing one or more processors to perform operations comprising:

• generating a plurality of electrical signals with a surface electrical source, each of the signals having a different corresponding frequency;
• injecting currents corresponding to the plurality of generated signals into the earth within a predefined distance from a well extending into the reservoir with a surface dipole;
• detecting vertical components of electric fields generated by the injected currents at locations in the reservoir, the vertical components of the electric fields corresponding to bulk formation resistivity changes present in the reservoir that represent the waterfront;
• detecting a location of the waterfront within the reservoir based on the received vertical components of the electric fields;
• and
• analyzing the detected vertical components of the electric fields taken on at least two different points in time using machine learning to predict a rate of movement of the waterfront within the reservoir, wherein the machine learning comprises a deep learning system;
• generating a separate reservoir model for each point in time with a reservoir simulator;
• converting the separate reservoir models into respective resistivity models;
• generating simulated electromagnetic (EM) responses to a simulated waterfront contained within the resistivity models through EM data modeling;
• forming EM data hypercubes with the simulated EM responses for each point in time;
• and
• applying the deep learning system to the separate resistivity models and the EM data hypercubes, which form a machine learning knowledge-base.