Waterflood Management: A Case Study of the Northwest Fault Block Area of Prudhoe Bay, Alaska, Using Streamline Simulation and Traditional Waterflood Analysis

Abstract

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.

Multiscale Mimetic Solvers for Efficient Streamline Simulation of Fractured Reservoirs

“…Streamline simulation has obvious speed advantages on large models and is steadily gaining acceptance as a key technology for processing large geological models as well as the more standard waterflooding and history-matching applications (Lolomari et al 2000;Grinestaff and Caffrey 2000;Kretz et al 2004;Moreno et al 2004;Cheng et al 2004). To be able to simulate flow efficiently on increasingly detailed geological models, it is vital that the performance of the flux-field computation required by the streamline algorithm be improved.…”

Revisiting Reservoir Flood-Surveillance Methods Using Streamlines

“…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.…”

Optimal Waterflood Management Using Rate Control

“…Also, an increased cost is associated with water recycling and handling. One approach to counteract the impact of heterogeneity and improve waterflood sweep efficiency is optimal rate allocation to the injectors and producers (Asheim 1988;Sudaryanto and Yortsos 2001;Brouwer et al 2001;Brouwer and Jansen 2004;Grinestaff 1999;Grinestaff and Caffrey 2000). Through optimal rate control, we can manage the propagation of the flood front, delay water breakthrough at the producers, and also increase the recovery efficiency.…”

“…They didn't use a formal optimization procedure, but instead they used streamlineflow visualization, allocation calculations, and the rapid turn around of simulation runs to guide the engineer. They utilized allocation factors (Grinestaff and Caffrey 2000) as their primary criterion for optimal sweep. Thiele and Batycky (2006) provide a different measure of flood efficiency.…”

“…, Distinguished Lecturer (1999-2000, and Distinguished Author (2000) and was selected as an Outstanding Technical Editor in 1996. He has also received the Cedric K. Ferguson Certificate (2000,2006) and the AIME Rossitter W. Raymond Award (1992).…”