In-well flow measurement remains as one of the most difficult tasks in the oil and gas industry, mainly due to the challenging conditions of the downhole environment. When made successfully, however, it plays a major role in monitoring and optimizing well performance, especially for the wells equipped with advanced completion devices. The increasing demand for in-well flow measurement is also driven by other factors including zonal production allocation in multizone completions as well as reliable commingled production, reduction of surface well tests and facilities, and detection of production anomalies.
This paper provides a closer look at one of the state-of-art in-well flow measurement technologies: optical, strain-based, phase flow rate measurements via turbulent structure velocity and sound speed of the turbulent flow. It is an introduction to how this flow measurement technology works and how it is applied to different flow applications from single-phase injectors to multiphase producers. Specific field examples representing different flow applications are also referenced to published material. The strong and weak points of the technology are explored, and in the process, an operation envelope is produced for the use of this technology. The system response to the presence of advanced completion devices are also discussed, guidelines are given, and recommendations are made based on field and lab tests.
Understanding a technology's strong and weak points before implementation is essential to ensuring that informed and successful decisions can be made concerning its use for a given application. This process is often mutually beneficial to both operators and equipment manufacturers since collaborations can lead to advancement of technology and, as a result, provide even more reliable solutions.
The use of the phrase "intelligent well?? was not common a decade ago. The reason is hidden in its definition. Because we are engineers and not linguists, our definition can be based on what we would like to achieve in using that phrase: An intelligent well should typically have sensors downhole to measure flow properties including pressure (P), temperature (T), flow rates, phase information such as gas volume fraction (GVF), water-in-liquid ratio (WLR), and perhaps more. It should also be possible to control and optimize the well production by means of adjustable chokes on the surface and in the well, so that an even flow distribution can be achieved particularly in multizone applications with the end result being more efficient production and longer lifetime of the well. Most of these complex, state-of-art devices were not available not too long ago. This has changed thanks to the technological advancements, and today we can manage wells more "intelligently??.
An efficient two-stage algebraic multiscale solver (TAMS) that converges to the fine-scale solution is described. The first (global) stage is a multiscale solution obtained algebraically for the given fine-scale problem. In the second stage, a local preconditioner, such as the Block ILU (BILU) or the Additive Schwarz (AS) method, is used. Spectral analysis shows that the multiscale solution step captures the low-frequency parts of the error spectrum quite well, while the local preconditioner represents the high-frequency components accurately. Combining the two stages in an iterative scheme results in efficient treatment of all the error components associated with the fine-scale problem. TAMS is shown to converge to the reference fine-scale solution. Moreover, the eigenvalues of the TAMS iteration matrix show significant clustering, which is favorable for Krylov-based methods. Accurate solution of the nonlinear saturation equations (i.e., transport problem) requires having locally conservative velocity fields. TAMS guarantees local mass conservation by concluding the iterations with a multiscale finite-volume step. We demonstrate the performance of TAMS using several test cases with strong permeability heterogeneity and large-grid aspect ratios. Different choices in the TAMS algorithm are investigated, including the Galerkin and finite-volume restriction operators, as well as the BILU and AS preconditioners for the second stage. TAMS for the elliptic flow problem is comparable to state-of-the-art algebraic multigrid methods, which are in wide use. Moreover, the computational time of TAMS grows nearly linearly with problem size.
Recent advances in multiscale methods have shown great promise in modeling multiphase flow in highly detailed heterogeneous domains. Existing multiscale methods, however, solve for the flow field (pressure and total velocity) only. Once the fine-scale flow field is reconstructed, the saturation equations are solved on the fine scale. With the efficiency in dealing with the flow equations greatly improved by multiscale formulations, solving the saturation equations on the fine scale becomes the relatively more expensive part. In this paper, we describe an adaptive multiscale finite-volume (MSFV) formulation for nonlinear transport (saturation) equations. A general algebraic multiscale formulation consistent with the operator-based framework proposed by Zhou and Tchelepi (SPE Journal, June 2008, pages 267-273) is presented. Thus, the flow and transport equations are solved in a unified multiscale framework. Two types of multiscale operators--namely, restriction and prolongation--are used to construct the multiscale saturation solution. The restriction operator is defined as the sum of the fine-scale transport equations in a coarse gridblock. Three adaptive prolongation operators are defined according to the local saturation history at a particular coarse block. The three operators have different computational complexities, and they are used adaptively in the course of a simulation run. When properly used, they yield excellent computational efficiency while preserving accuracy. This adaptive multiscale formulation has been tested using several challenging problems with strong heterogeneity, large buoyancy effects, and changes in the well operating conditions (e.g., switching injectors and producers during simulation). The results demonstrate that adaptive multiscale transport calculations are in excellent agreement with fine-scale reference solutions, but at a much lower computational cost.