For some petroleum fields, optimization of production operations can be a major factor for increasing production rates and reducing production costs. While for single wells or other small systems simple nodal analysis can be adequate, large complex systems demand a much more sophisticated approach. Many mature fields are produced by gas-lift under multiple constraints imposed by the field handling capacity of the system. In this paper we present an optimization technique for allocating production rates and lift-gas rates to wells of large fields subject to multiple flow rate and pressure constraints. The well rate and lift-gas rate allocation problem has been addressed in the literature1–13. However, existing methods are either inefficient or make significant simplifications. This often leads to suboptimal operations. This paper proposes a new formulation of the problem that is able to handle flow interactions among wells and can be applied to a variety of problems of varying complexities. We show that the proper formulation of the optimization problem is important in the practical use of modern optimization techniques. Once formulated, the optimization problem is solved by a sequential quadratic programming algorithm. Our results show that the procedure developed in this paper is capable of handling complex oil production problems. Introduction In petroleum fields, hydrocarbon production is often constrained by reservoir conditions, deliverability of the pipeline network, fluid handling capacity of surface facilities, safety and economic considerations, or a combination of these considerations. While production can be controlled by adjusting well production rates, allocating lift-gas rates, and in some fields, by switching well connections from one flowline to another flowline, implementation of these controls in an optimal manner is not easy. The objective of dynamic production optimization is to find the best operational settings at a given time, subject to all constraints, to achieve certain operational goals. These goals can vary from field to field and with time. Typically one may wish to maximize daily oil rates or minimize production costs. Various aspects of production optimization have been addressed in the literature. For example, several researchers1–5 have studied the problem of allocating limited amount of available gas to specified wells for continuous gas-lift. Fang and Lo6 proposed a linear programming technique to allocate lift-gas and well rates subject to multiple flow rate constraints. Barnes et al.7 developed an optimization technique for a portion of the Prudhoe Bay field in Alaska. This model maximizes oil production while minimizing the need for gas processing. Several papers8–10 have reported results for the production optimization of the Kuparuk River field in Alaska. The techniques published so far1–10 either addressed only a part of the optimization problem of interest to us or made significant simplifications during the optimization process. In most commercial reservoir simulators11,12, flow rate constraints on facilities are handled sequentially by ad hoc rules. In addition, gas-lift optimization is done separately from the allocation of well rates. Because of the nonlinear nature of the optimization problem and complex interactions, results from such procedures can be unsatisfactory. In a companion paper Wang et al.13 presented a procedure for the simultaneous optimization of well rates, lift-gas rates, and well connections subject to multiple pressure, flow rate, and velocity constraints. While this approach was successful, it was limited in its ability to handle flow interactions among wells when allocating well rates and lift-gas rates. Here we extend the work of Wang et al.13 and propose a new formulation for the problem of simultaneously optimizing the allocation of well rates and lift-gas rates. The optimization problem is solved by a sequential quadratic programming algorithm, which is a derivative-based nonlinear optimization algorithm. The proposed method is tested on several examples. Results show that the method is capable of handling flow interactions among wells and can be applied to a variety of problems of varying complexities and sizes.

We have developed a new constrained optimization approach to the coarsening of 3D reservoir models for flow simulation. The optimization maximally preserves a statistical measure of the heterogeneity of a fine scale model. Constraints arise from the reservoir fluids, well locations, pay/non-pay juxtaposition, and large scale reservoir structure and stratigraphy. The approach has been validated for a number of oil and gas projects, where flow simulation through the coarsened model is shown to provide an excellent approximation to high resolution calculations performed in the original model. The optimal layer coarsening is related to the analyses of Li and Beckner (2000), Li, Cullick and Lake (1995), and Testerman (1962). It differs by utilizing a more accurate measure of reservoir heterogeneity and by being based on recursive sequential coarsening, instead of sequential refinement. Recursive coarsening is shown to be significantly faster than refinement: the cost of the calculation scales as (NX·NY·NZ) instead of (NX·NY·NZ)[2]. The more accurate measure of reservoir heterogeneity is very important; it provides a more conservative estimate of the optimal number of layers than the analysis of Li et.al.. The latter is shown to be too aggressive and does not preserve important aspects of the reservoir heterogeneity. Our approach also differs from the global methods of Stern (1999) and Durlofsky (1994). It does not require the calculation of a global pressure solution and it does not require the imposition of large scale flow fields, which may bias the analysis, Fincham (2004). Instead, global flow calculations are retained only to validate the reservoir coarsening. Our approach can generate highly unstructured, variable resolution, computational grids. The layering scheme for these grids follows from the statistical analysis of the reservoir heterogeneity. Locally variable resolution follows from the constraints (reservoir structure, faults, well locations, fluids, pay/non-pay juxtaposition). Our reservoir simulator has been modified to allow a fine scale model to be initialized and further coarsened at run time. This has many advantages in that it provides both simplified and powerful workflows, which allow engineers and geoscientists to work with identical shared models. Introduction The development of (coarsened) reservoir simulation models from high resolution geologic models remains an active field of research [1–7]. In the current study we will report upon our success in the use of coarsening algorithms to determine a ‘best’ reservoir simulation grid obtained by grouping fine scale geologic model cells into effective simulation cells. Our results differ from previous studies in that we have found a statistical analysis of the static properties of the model that appears to identify the best grid for dynamic reservoir simulation. Coarsening beyond the degree indicated by our analysis discards too much of the underlying heterogeneity. It will overly homogenize the properties on the simulation grid. Finer models will, of course, retain at least as much reservoir heterogeneity, but are more costly. Our analysis uses a statistical technique for layer grouping and a constrained approach for areal gridding. The resulting composite corner point grid (CCPG) has many of the advantages of unstructured PEBI grids in that they can follow major features of the geologic description [8]. Compared to PEBI grids, they have the advantage of exact alignment of the simulation cells with the geologic model, which will minimize property upscaling errors, and the practical advantage that they may be utilized without the development of new simulation pre and post processing applications. This approach also moves us closer to having an Earth Model shared between the reservoir engineer and the reservoir geologist: the 3D geologic model will provide the grid on which the high resolution initialization of the simulation model will be calculated.

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Although various gas-lift optimization algorithms have been proposed in literature, few of them is suitable for long-term reservoir development studies, which require the gas-lift optimizer to be highly efficient, flexible and powerful enough to handle complicated fluid flows and operational constraints, and have low impact on simulator convergences. This paper investigated methods to address these important issues. The gas-lift optimization problem considered in this paper is to maximize the daily hydrocarbon production by selecting optimally the well production and lift gas rates subject to pressure and rate constraints in nodes of the surface pipeline network and to the amount of lift gas available. The problem is regarded as a well management problem in a commercial reservoir simulator capable of simulating multiphase compositional fluid flow in reservoirs, well tubing strings, surface pipeline network systems, and separation facilities. The problem is solved in selected iterations of a reservoir simulation time step. This paper proposed a method for the described gas-lift optimization problem and investigated its performance against multiple existing methods. Case studies showed that the new method is capable of producing high quality results while requires less CPU time for optimization and has smaller impact on reservoir simulator convergence. This paper also applied the concept of multiobjective optimization to smooth the rate oscillation between adjacent iterations by sacrificing a certain amount of oil production. In certain cases, this method reduces the simulation time significantly. Introduction When oil field matures, the hydrocarbon production is often assisted by continuous lift gas injection and constrained by the gas and/or liquid handling capacities of surface facilities. The optimal allocation of production rates and lift gas rates subject to reservoir deliverability and surface facility capacities can have a big impact on facility design and other capital investment decisions and should be captured in long term reservoir studies. Compared to real time production optimization, the optimal rate allocation in long-term reservoir simulation studies poses unique problems: the rate allocation optimizer has to be highly efficient and have low impact on simulation convergence while is capable of generating quality results. The stated problem has been addressed in different ways in existing literature. Fang and Lo1 proposed a linear programming technique to allocate lift gas rates and production streams subject to multiple flow rate constraints. The method was implemented in a reservoir simulator and proved to be efficient in several field studies. Based on Fang and Lo's work, Wang et al.2 developed a procedure to optimally allocate the production rate, lift gas rate, and well connections to surface pipeline systems simultaneously. The optimization procedure is invoked at the Newton-iteration level of a commercial reservoir simulator. Hepgular et al.3 coupled a separate commercial surface pipeline network optimizer with a commercial reservoir simulator through an iterative procedure. The surface network optimizer employees a Sequential Quadratic Programming (SQP) optimization algorithm and has the ability to perform general operation and design optimizations. Davidson and Beckner4 presented an integrated facility and reservoir model in which the rate allocation problem is solved in the facility model using SQP methods. They also presented a detailed procedure on how to handle infeasible conditions. Fang and Lo's1 method is simple and efficient. However, it ignores the pressure interactions among wells through common flow lines and may result unsatisfying results. Hepgular et al.3 and Davidson and Beckner's4 methods relied on powerful facility network optimizers that require significant effort to develop. This paper bridges this gap by presenting a simple yet robust and efficient rate allocation optimization procedure. In addition, this paper applied the concept of multiobjective optimization5 to minimize the impact of lift gas rate oscillation on simulator convergence. This method proved to be successful in certain cases.

This work deals with the problem of estimating reservoir permeability and porosity distribution from multiple sources, including production data and well logging data. The work focuses on integrating long-term resistivity data into the parameter estimation problem, investigating its resolution power both in the depth direction and in area. The resistivity logging tool considered in this project is a new tool proposed for permanent installation. The tool is installed in the cement around the well when the well is completed, and is capable of recording the long-term resistivity variation around the wellbore. In this work, the Poisson equation with mixed boundary condition was used to model the infinite potential field around the resistivity logging tool. The behavior of the reservoir was modeled with a standard three-dimensional, two-phase, blackoil model. The resistivity response simulator was integrated into the flow simulator through Archie's law. The Gauss-Newton algorithm was used to solve the inverse problem. This algorithm requires the calculation of the derivatives of the observation data with respect to the unknown parameters. These derivatives are called the sensitivity coefficients. By running several simple inverse problems, it was concluded that the resistivity data has high resolution power in the depth direction and is capable of sensing the areal heterogeneity.