Downhole and near wellbore reservoir state inference through automated inverse wellbore flow modeling

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. Based on the extent of repetition and overlap between similar states as they transition over several time steps, the confidence of the inverse model increases, thus narrowing down the likely domain and trajectory of operation and boosting the probability of this narrowed zone. The knowledge of downhole and near wellbore reservoir zone is essential for better modeling, understanding of the wells and decision making in the oilfield. This knowledge may be obtained through well testing but involves physical intervention that can involve expense and production loss. It is also less common to have such well tests being performed at a daily, weekly or even monthly basis so timely information is generally not available. This invention provides a mechanism to have a live update of such information without any physical intervention.