Polymer flooding is one of the most widely used chemical enhanced oil recovery methods due to its simplicity and low cost. To achieve high oil recoveries, large quantities of polymer solution is often injected through a small wellbore. Sometimes, the economic success of the project is only feasible when injection rate is high for high viscosity solution. However, injection of viscous polymer solutions has been a concern for the field application of polymer flooding. The pressure increase in polymer injectors can be attributed to (1) formation of an oil bank, (2) polymer rheology (shear-thickening behavior at near well-bore), and (3) plugging of the reservoir pores by insoluble polymer molecules or suspended particles in the water. In this paper, we propose a new model to history match field injection rate/pressure data. The pertinent equations for deep-bed filtration and external cake build-up in radial coordinate were coupled to the viscoelastic polymer rheology to capture important mechanisms. We selected radial coordinate in order to minimize the velocity/shear rate errors due to gridblock size in Cartesian coordinate. We used filtration theory and successfully history matched the field data. We performed systematic simulations and studied the impact of adsorption (retention), shear thickening, deep bed filtration, and external cake formation to explain the well injectivity behavior of polymer. The simulation results indicate that the gradual increase in bottomhole pressure during early times is attributed to the shear thickening rheology at high velocities experienced by viscoelastic HPAM polymers around the wellbore and the permeability reduction due to polymer adsorption and internal filtration of undissolved polymer. However, the linear impedance during external cake growth is responsible for the sharper increase in injection pressure at the later times. The proposed model can be used to calculate the injectivity of the polymer injection wells, understand the contribution of different phenomena on the pressure rise in the wells, locate the plugging or damage that may be caused by polymer, and accordingly design the chemical stimulation if necessary.
History matching a field's performance to understand the reservoir behavior and characterize its static and dynamic properties has been a key activity for reservoir engineers for a long time. Recently, significant effort has been made to devise techniques that allow this process to be automated. These techniques suffer with a number of limitations and often yield History Match (HM) solution points in the uncertainty space that carry certain bias inherent to the algorithm used. Common limitations include:Techniques more often failing to yield any realization with acceptable HM quality at the well levels,Large number of iterations and simulation runs required to minimize the HM errors to acceptable values and,Significant volume of information generated during this iterative process, which becomes unmanageable to interpret and reconcile. Consequently, the assisted HM unfortunately becomes just an iterative process of minimizing the objective function, and the main goal of understanding the reservoir performance gets diluted. When these techniques are applied for Enhanced Oil Recovery (EOR) studies, the list of uncertainty parameters becomes very extensive due to the addition of parameters defining the EOR process itself. As a result, the number of scenarios increases in addition simulations tend to become slower due to incorporation of EOR module and model requirements for space and time resolution to simulate physio-chemical phenomena. Traditional HM techniques then become cumbersome which might lead to inadequate characterization of the potential upside and downside scenarios. This in turn could adversely impact business decisions involving huge capital investments for any field (re-)development opportunities. This paper will discuss a technique that evolved during the HM exercise for an EOR pilot area in the Middle East. The methodology preserves the idea of Assisted History Match (AHM) to generate multiple HM realizations, however, the solution points are found much quicker eliminating large number of iterations, thereby minimizing the computational expense. The devised methodology is based on the combination of Design of Experiment (DoE) based Stochastic Uncertainty Management (SUM) workflow with the gradient based calculation (Adjoint) approach to find multiple HM realizations. First, a complete stochastic uncertainty management workflow is applied sampling the entire uncertainty space and multiple realizations are screened ensuring enough variability in parameters and acceptable HM error. The gradient based calculations are then applied on each of these selected realizations to further minimize the HM error. The Adjoint method yields final set of improved HM realizations with different starting points in the solution space that were obtained via DoE. This helps in two ways – firstly, it covers the entire uncertainty space for better characterization of upside and downside cases and secondly, provides a focused view of the reservoir characteristics. Additionally, the paper would also offer insights gained from the HM exercise into water coning behavior in a viscous oil reservoir and illustrate the reservoir parameters and their significance that need to be addressed to adequately capture the coning phenomenon.
This paper describes key factors related to intelligent horizontal well completion systems and surveillance activities for a polymer field trial within a sandstone reservoir in the South of Oman. The existing field predominantly comprising of horizontal producer wells drilled and located at the crest of the reservoir to ensure optimum oil production rates via artificial lift techniques. Many wells have encountered early water breakthrough, resulting in large volumes of un-swept oil. Improved sweep efficiency, hence improved oil recovery is expected by polymer flooding using a horizontal well approach [1]. The polymer field trial location consists of: 4 horizontal producers each completed with wire wrap screens, blanks and external zonal isolation packers within the reservoir section for segmentation and isolation. Each producer has a downhole gauge for real-time pressure and temperature monitoring. Three horizontal smart injectors each consisting of 4 zones completed with 7 inch pre-drilled liners, blanks and external zonal isolation packers across the reservoir section for segmentation and zonal isolation. These injectors are internally completed with intelligent completion systems with remote access and control whereby each of the 4 zones consists of a mechanical retrievable packer for zonal isolation, on-off intelligent flow control valve with hydraulic multi-drop module system for conformance control, quartz pressure-temperature gauge, double ended pump down DTS system for real-time monitoring and internally lined GRE tubulars in order to prevent polymer degradation. Two horizontal observation wells each completed with GRE casings and predrilled liner joints for logging along with downhole gauges for real-time pressure-temperature monitoring. One vertical observation well completed with GRE casing and carbon steel casing below the oil water contact for surveillance purposes. A detailed surveillance plan for the current producers, injectors and observation wells is of utmost importance for pre and post injection data gathering in order to successfully evaluate key subsurface risks and uncertainties associated with the polymer flood technique. The field trial has been designed and executed with an optimum approach to ensure continuous real-time surveillance. This is facilitated by remote access and control thereby minimizing well interventions for surveillance activities for the duration of the trial.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.