Waterflood Surveillance in the Kuparuk River Unit With Computerized Pattern Analysis

Abstract

Summary This paper describes the computer-aided waterflood surveillance method used in the Kuparuk River field. The flood is monitored pattern by pattern, with production and injection allocations verified by comparison of measured field pressures with pressures calculated by material balance. The resulting detailed knowledge about waterflood progress has led to operational changes and workovers to optimize waterflood performance. Introduction The Kuparuk River Unit lies on the North Slope of Alaska about 40 miles [64 km] west of the Prudhoe Bay Unit. The Kuparuk reservoir contains some 4.4 billion bbl [700 × 10–6 m3] of original oil in place (OOIP). A combination of relatively low API gravity (24API [0.91 g/cm3]) and an initial undersaturated condition makes waterflooding this reservoir a necessity. To complicate matters, the Kuparuk is highly faulted in some areas and is divided into a lower body called the A Sand and an upper body called the C Sand, with the C Sand considerably more permeable. Because of the large reserve stakes and the geologic complexity of the Kuparuk a comprehensive waterflood surveillance effort is necessary. A pattern-by-pattern knowledge of performance is needed for waterflood optimization through workovers, infill drilling, and other operations. This knowledge is also important when selecting EOR candidate areas in the waterflood. Because of the large amount of data involved in any waterflood project, a computer can be helpful to engineers involved. A question arises about the actual amount of "help" the computer should give. The program can range from a simple production data base to a comprehensive full-field reservoir model that is used to track the waterflood. The more automated a computerized method is, the less work the engineers have to do. By being involved less, however, engineers do not become as familiar with the detailed flood performance and the program may not be as flexible as a program requiring more hands-on data manipulation. Also, a complex computer program tends to be a "black box" that only one or two people may understand fully. The computer program described here intentionally requires a great deal of manual manipulation by the engineers. The cornerstone of a sound surveillance effort, computerized or manual, is a good allocation of production and injection to every zone within each waterflood pattern. Allocation factors can be determined or estimated in a variety of ways from field data or reservoir parameters. Most of these methods have large uncertainties and do not track fluid allocations as they change over time with varying reservoir conditions. The method outlined in this paper uses a material-balance technique to backcalculate a pressure based on the production and injection allocated by pattern. These pressures, calculated monthly, are compared with actual field pressure data to check the production/injection allocation factors, which are adjusted manually after the fluids have been adequately allocated to each pattern and zone. Helpful output, such as pattern performance and conformance plots, are plotted by the computer. This leaves the interpretation to the engineers involved, getting them much closer to the problem. The waterflood surveillance program is used to look for anomalies in field performance, such as low volumetric efficiencies or poor injection/voidage ratios. More often than not, these anomalies turn up opportunities for waterflood improvement. While waterflood performance parameters could be calculated on the basis of a detailed history match of a simulation model, the main thrust of a model is to explore future scenarios with the model used as a predictive tool. Alternatively, the method described in this paper is intended to provide an in-depth look at waterflood performance to date without attempting to predict future performance. The method presented represents a simpler, lower-cost alternative to the use of simulation as a waterflood evaluation tool. Allocating Fluids by Material Balance Fig. 1 is a schematic of a typical Kuparuk waterflood pattern made up of two sands with differing flow characteristics. To monitor and optimize waterflood performance in each sand, factors are needed to allocate production and injection vertically between these two sands and areally among patterns. Separate sets of allocation factors are used for oil, gas, and water. Vertical allocation factors can be determined fairly accurately and at a moderate cost with spinner surveys in injection wells. The determination is much less accurate and very costly in producing wells, particularly after water breakthrough occurs. An additional problem is caused by the significant changes in well profile that can occur between spinner surveys. Historically, little or none of this type of data may have been taken in a mature field, hampering efforts to evaluate pattern-by-pattern flood performance. Various methods can be used to determine areal allocation factors. Injection-well tracer tests can aid in estimating areal allocations, but tracer tests are costly and give mostly qualitative results. The simplest method is to assume a homogeneous. constant-thickness reservoir and to base the factors geometrically on angle open to flow. Although simple to calculate, geometric allocation factors (even after adjustment for non-ideal conditions) may not be an accurate representation because of areal variations in reservoir properties, such as porosity and permeability. Reservoir discontinuities, such as truncations and faulting, can also vary the allocation factors. Areal allocation factors also need to be adjusted over time to match field performance as different patterns and sands pressure up and eventually reach breakthrough. The method described here handles fluid allocations by careful study of each pattern and uses a material-balance method to aid in correcting the allocation factors. Typical material-balance applications involve the use of measured pressures and production data to determine the original volume of oil in a reservoir. In this waterflood surveillance method, material balance is used in reverse. The of a pattern is determined volumetrically, then production and injection are allocated into a pattern. Next, material-balance equations are used to calculate what the pressure should be, given the known reservoir volume, PVT relationships, and allocated production and injection. The calculated pressures are then compared with measured values to check the allocation factors. The general form of the material-balance equation is ...............................(1) ...............................(2) ...............................(3) JPT P. 277^

Revisiting Reservoir Flood-Surveillance Methods Using Streamlines

“…Despite the simplicity and inherent assumptions of a surveillance model, it still can be a valuable reservoir-engineering tool. Chapman and Thompson (1989) note that a surveillance model provides an in-depth look at flood performance to date and is another way to interpret production data by relating injection to production.…”

“…Traditionally, the WAFs have been based on well-pattern geometry or were com-puted from simple 2D streamline models and usually assumed fixed through time. Field simulations routinely show that WAFs are neither fixed nor solely a function of well-pattern geometry and that there is substantial flow between well pairs outside of predefined patterns (Baker et al 2002;Chapman and Thompson 1989;Flanders and Bates 1987;Grinestaff and Caffery 2000). Additionally, for large, multiwell floods, pattern definition and WAF calculations are a time-consuming process.…”

Layered Waterflood Surveillance in a Mature Field: The Long Beach Unit

“…Layer gross production profiles in multi-well patterns can be estimated from the pattern injection profiles. 5. Layer oil and water splits in multi-well patterns can be estimated from gross production using the WOR vs. HPVI trend over time.…”

A pattern-based approach to waterflood performance prediction using knowledge management tools and classical reservoir engineering forecasting methods

“…Studies that are based on the fundamentals of Arps ' (1945) decline curves and experimental studies (Crawford, 1960) are followed by development of simple analytical tools throughout the years as a result of continuing interest (Higgins and Leighton, 1963;Higgins et al, 1966;Hiatt, 1968;Khan, 1971;Craig, 1973;Chapman and Thompson, 1989). With the advancement of computational technologies and power, numerical simulation has become a standard tool for waterflood performance prediction (Thomas and Driscoll, 1973;Tompang and Kelkar, 1988).…”