Predicting Flow Profile of Horizontal Well by Downhole Pressure and DTS Data for Water-Drive Reservoir

摘要

Downhole pressure and temperature data are important information to help us understand the bottom-hole flow condition. The data today are readily available from permanent monitoring systems such as downhole gauges or fiber optic sensors. In previous study we have showed that using temperature and pressure data, water entry along a horizontal wellbore can be detected by a semi-analytical model. Flow in the wellbore is well-defined but flow in the reservoir is described by a single phase, one-dimensional model. The assumptions limited application of the model for mostly a single-phase condition In this paper, we present an improved model that is more flexible. We use streamline simulation method to solve the flow problem in the reservoir for fast track of reservoir flow. We developed a transient, three-dimensional, 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 water-drive case. The results of simulation show that the temperature features in a horizontal well can successfully detect the location and amount of water breakthrough. Meanwhile, even the pressure trend does not reflect the water entrance as clear as temperature curve, its value of easily 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 entering the horizontal well in this field example. We use an inversion method to interpret the pressure and temperature data to obtain flow rate profile along horizontal wells. The inversion method is the traditional Markov Chain Monte Carlo (MCMC) method. This stochastic method searches the possible solution in the parameter space and use 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.