Abstract: A method to estimate the likely downhole conditions in the wellbore and reservoir by Inverse modeling well flow simulation history matched with field sensor data. The invention presents a method for automating sensor data processing through cleaning, transformation, and identification of stable states. This process is crucial for the selection of data to be simulated and matched without human review. The matched simulations are subjected to a state-space model in order to assign a probability to a given unknown state. This probability is updated at each time step. As the well undergoes transition over time including decline, the drift of the likely state of operation is orchestrated to allow physically constrained movement to a proximate space.

Abstract: A methodology for providing set-point recommendations in an automated manner to optimize the operation of a well-producing fluid, by establishing a live synergy between physics-based simulation and real-time field data, through the employment of machine learning models. The machine learning models serve two distinct purposes in this approach: 1. Accelerate emulation of the numerical physics-based simulation to enable real-time solutions 2. Provide a probabilistic estimate of the unknown operating conditions of a well and updating the estimate based on the response to the set-point changes made, thus improving with each iteration.

Abstract: A method to estimate the likely downhole conditions in the wellbore and reservoir by Inverse modeling well flow simulation history matched with field sensor data. The invention presents a method for automating sensor data processing through cleaning, transformation, and identification of stable states. This process is crucial for the selection of data to be simulated and matched without human review. The matched simulations are subjected to a state-space model in order to assign a probability to a given unknown state. This probability is updated at each time step. As the well undergoes transition over time including decline, the drift of the likely state of operation is orchestrated to allow physically constrained movement to a proximate space.

Abstract: A method to estimate the likely downhole conditions in the wellbore and reservoir by Inverse modeling well flow simulation history matched with field sensor data. The invention presents a method for automating sensor data processing through cleaning, transformation, and identification of stable states. This process is crucial for the selection of data to be simulated and matched without human review. The matched simulations are subjected to a state-space model in order to assign a probability to a given unknown state. This probability is updated at each time step. As the well undergoes transition over time including decline, the drift of the likely state of operation is orchestrated to allow physically constrained movement to a proximate space.