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

Abstract

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. Introduction Horizontal wells have been widely used to enhance production by increasing wellbore contact with reservoir. Meanwhile, the local inflow rates of different phases along a horizontal well may vary because of reservoir heterogeneity. When unwanted phases are produced, downhole monitoring and control system can be deployed to optimize the well performance. Successful detection of water entry location and its flow rate will help the well performance improvement.

Flow Control Optimisation to Maximise the Accuracy of Multi-phase Flow Rate Allocation

“…Luo et al (2014) adopted the latter framework but proposed two methods to calculate the optimal variances of the process noise in Markov jump model that were chosen manually previously. Li and Zhu (2009) interpreted P/T data to obtain a flow rate profile along horizontal wells using the traditional Markov Chain Monte Carlo method.…”

Diagnosis of Multi-Stage Fracture Stimulation in Horizontal Wells by Downhole Temperature Measurements

“…But these software packages use a simple analytical correlation to calculate the temperature change in the reservoir from pressure drawdown instead of a rigorous reservoir thermal model. In the past several numerical simulations were developed to identify water/gas entry zones in vertical or horizontal wells (Pinzon et al 2007; Yoshioka et al 2007; Li and Zhu, 2009). Many investigators also tried to regress upon flow rates and layer permeabilities by using coupled reservoir and wellbore models (Sui et al 2008; Duru and Horne, 2008;Li and Zhu, 2009;Ramazanov et al 2010).…”

Determining multilayer formation properties from transient temperature and pressure measurements in gas wells with commingled zones

“…Their models can be used for steady-state for the whole well depth or transient flow over the non reservoir intervals of the well. Li andZhu, 2009 replenished Yoshioka's model (2007) to a 3D, fully transient wellbore/reservoir model and investigated the water conning problem for infinite water-drive reservoir. In Li's work, streamline simulation technique was used in forward modeling to speed up forward model simulation.…”