SPE Distinguished lecturer Anil Kumar states that industry wide injection wells are overlooked, neglected, and mismanaged. Historically, injection patterns have been described by neighboring producers. Production is allocated back to injectors using angle open to flow or volume and distance weighting methodology. These static allocation methods poorly represent the physics involved in determining flow paths in the reservoir between wells. Through the use of streamline simulation, Dynamic Injection Pattern Allocations accurately describe waterflood patterns through time. By quantifying the injector to producer relationship, inefficiencies in the waterflood are readily seen and the benefits of injection well work quantified. Waterflood injector rates can be set to maximize oil production, instead of maximizing water injection. This paper demonstrates how to use streamline simulation to proactively manage a waterflood. The paper focuses on three areas: 1) how streamline models require a different approach to simulation, 2) using streamlines to find inefficiencies in the waterflood and set injection targets, and 3) the benefits of a history matched model.

This case study of a complex multiple-Zone waterflood in Prudhoe Bay, Alaska, focuses on the basics of waterflood management that are often overlooked. Evaluation of injection well conformance, flood front behavior, and reservoir description, followed by improved injection well management resulted in dramatic improvements in waterflood performance. Assurance of waterflood performance requires an integrated approach and accurate performance predictions. The performance of the Northwest Fault Block (NWFB) waterflood was analyzed with traditional decline curves, Hall plots, and a thorough review of well histories to ascertain that it was under performing. A high-resolution 3-D streamline model proved to be the simulation of choice to answer regional and individual well performance questions for this complex waterflood. After achieving a history match for each of the 200+ wells, streamline modeling provided accurate results of vertical and area wide sweep inefficiencies that were later verified by horizontal drilling. Water cycling was quantified by streamline simulation and injection reduced by 30–40%, resulting in increased production and cost savings. Expansion of the waterflood into the gravity drainage area was stopped. Most importantly, bypassed oil is being developed through an aggressive injection well replacement program. These promising results were achieved by a multidisciplinary team that focused on understanding micro and macro scale reservoir mechanisms. This holistic understanding of mature waterflood behavior, was achieved with a thorough understanding of injection well behavior and streamline simulation at a detailed scale to match each wells performance. Introduction Prudhoe Bay, located on the North Slope of Alaska, is the largest oil field in North America. The field produces from the Permo-Triassic Ivishak formation, primarily made up of braided river deposits of sandstone and conglomerate. The reservoir has traditionally been divided up into four main Zones based on lithologic characteristics (Figure 1). Reservoir properties including shale frequency, differ between the Zones and across the field due to deposition and diagenesis. The field was found with up to 500 feet of saturated 27 degree API oil underlying a large gas cap. The down-dip peripheral areas of the field, where no gas cap existed, are produced using the waterflood and miscible gas injection process (Figure 2). The main area of the field is produced by gravity drainage with large scale gas cap cycling for vaporization of oil and condensate in the original and expanded gas cap.

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractThis paper describes the feasibility study of a large-scale miscible CO 2 -WAG (MWAG) injection scheme in the Gullfaks Field, offshore Norway. We describe the reservoir engineering workflow and simulation techniques, the predicted production and injection profiles, and the main infrastructure solutions under consideration.Compositional cross-section models and recently available streamline-tracer simulation techniques are employed to scale up from element models to a fast, full-field simulator with a high degree of flexibility. Figure 1: Gullfaks Field LocationThe starting point for the workflow is a set of black oil and streamline front tracking models, history matched on coarse and fine grids. A fast, finely gridded streamline model is used to identify the MWAG injection targets, define injection well locations and completion strategy. Fine gridded cross-sections are extracted and used in a compositional simulator to study and quantify the miscible displacement process. These are the used to derive scaling parameters used in a simple, ultra-fast streamline-tracer model, scaling the MWAG process up to field level. The streamline-tracer model interactively optimises solvent allocation and generates production predictions on a well-by-well basis. Water flood recovery and incremental IOR are predicted simultaneously in a single simulation run.In addition, the general economic limitations and example technical solutions for implementation of a CO 2 MWAG on the Gullfaks Field are briefly described.

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractIn September 2000 the Forties Field celebrated 25 years of production. Recovery up to this point is approximately 60% of the original oil in place. The field contains undersaturated oil and is being developed under waterflood. A screening study of Increased Oil Recovery (IOR) options highlighted CO 2 injection as technically feasible, suggesting an additional recovery of in the range 5-10% of the initial oil in place (STOIIP), subject to further work on the investment economics of the project and corporate sanction. This paper describes the study of the CO 2 injection scheme with a focus on the reservoir simulation workflow.Various techniques for evaluating the full field benefits of IOR schemes can be used including the Todd and Longstaff approach 1 or a coarsely gridded conventional compositional reservoir simulation model. The evaluation presented in this paper builds upon a technique that incorporates detailed conventional simulation results into a full field streamline front-tracking simulation. This method was originally developed by Arco and has been successfully applied in several Alaskan oil fields 2 .The technique captures the complex physics of the IOR process through fine scale, 3D, compositional, finite difference simulations of 'type' sections of the reservoir (the original method used 2D simulations). Results from these simulations are then used to calibrate 'recovery' curves that capture characteristics of oil mobilisation and returned solvent volumes as a function of gas injected. The calibrated curves representing gas injection response are then applied as tracers using streamline front tracking simulation to scale up to full field response.

A new type of grid used in the dynamic simulation model of the giant Troll Field is presented. The grid consists of a stratigraphic and a horizontal layered part. Hence, it is named hybrid grid. The horizontal part encompasses the thin oil zone and a small distance into the gas cap and water zone, the rest of the grid follows the stratigraphy. The reason for choosing such a grid for the Troll Field is the dipping stratigraphic layering, a thin oil column, and the large area that the field covers.Another factor is the long horizontal wells that drain the oil. The horizontal grid has a high vertical resolution of 2 m to accurately capture the thin oil column, and for satisfactorily representing horizontal wells close to the fluid contacts. Above and below the horizontal grid the layers follow the stratigraphy to represent the geology with a minimum number of grid blocks. The paper shows why the hybrid grid is superior compared to other grid types for a gas field with a thin oil column, and covering a large area. The paper also describes how to build a hybrid grid, and the work processes for up-scaling and history matching the model. The new Troll model has currently replaced the 15 simulation models that previously were used to represent dynamically the Troll Field.