Predicting Flow Profile of Horizontal Well by Downhole Pressure and Distributed-Temperature Data for Waterdrive Reservoir

Abstract

Summary Downhole pressure and temperature data are important information that helps us understand the bottomhole flow condition. Today, the data are readily available from permanent monitoring systems such as downhole gauges or fiber-optic sensors. In a previous study, we showed that using temperature and pressure data, water entry along a horizontal wellbore can be detected by a semianalytical model. Flow in the wellbore is well-defined, but flow in the reservoir is described by a single-phase, 1D model. The assumptions limited application of the model to mostly a single-phase condition. In this paper, we present an improved model that is more flexible. We use a streamline-simulation method to solve the flow problem in the reservoir for fast tracking of reservoir flow. We developed a transient, 3D, multiphase reservoir thermal model to calculate reservoir temperature. We integrated the reservoir flow model and thermal model with a horizontal-well temperature model to predict the pressure and temperature distribution in a horizontal-well system. We apply the model to a synthetic example. The example is an infinite waterdrive case. The results of simulation show that the temperature features in a horizontal well can detect the location and amount of water breakthrough successfully. Meanwhile, even the pressure trend does not reflect the water entrance as clearly as the temperature curve, the capability of which to indentify the reservoir permeability distribution is very helpful in temperature calculation. We apply the model to a field case: a horizontal well in the Sincor field for heavy-oil production. The results showed that we can successfully identify where and how much water enters the horizontal well in this field example. We use an inversion method to interpret the pressure and temperature data to obtain a flow-rate profile along horizontal wells. The inversion method is the traditional Markov Chain Monte Carlo (MCMC) method. This stochastic method searches for the possible solution in the parameter space and uses the Metropolis-Hastings algorithm to judge the acceptance. We discuss how to reduce the parameters to make the inversion method work more efficiently according to the downhole pressure and temperature data.