Optimized Reservoir Management With Mixed Linear Programming

Abstract

Summary Reservoir engineers face a large number of possible development plans for major offshore oil fields. Some of the factors affecting development include product-processing capacities, drilling-rig availability, extended use of existing platforms, enhanced-recovery options, and subsidence-avoidance requirements. Reservoir simulation is an accepted technique for estimating production from oil and gas fields. Each possible development scenario is simulated separately to obtain future oil- and gas-production estimates from wells included in the scenario. Simulation results are analyzed for each run, and the optimal plan is selected. Enumerating all the possible drilling schedules is a difficult task, and, even with clusters of advanced scientific workstations, the computational requirements of simulation can be overwhelming. This paper describes the application of linear mixed integer programming (MIP) to the combinatorial problem of generating the optimal redevelopment scenario for the Ekofisk field in the Norwegian sector of the North Sea. The MIP model determines the optimal redevelopment program and drilling schedule while maintaining operationally feasible product-processing requirements, drilling-rig use, and well/platform relationships. Optimality is based on the net present value (NPV) of the estimated production forecasts for the wells selected. Total recovered reserves is an alternative optimization criterion. Reservoir simulations generated the estimated production forecasts used in iterative runs of the optimized model. We discuss success of the approach and extensions. Introduction The field-redevelopment project presented enormous challenges to optimizing value while scheduling drilling of more than 60 new wells and timing permanent platform shutdowns within physical facilities constraints. In addition, because many of the wells would be completed in the same reservoir, the order of drilling affected the production rates from subsequent wells. The large number of possible development schedules and production profiles for each well indicated that a computer application would consider the possibilities and optimize the planning faster than more traditional methods. We chose linear programming because of its effectiveness in solving scheduling problems in other industries. Ekofisk Field History The Ekofisk field and surrounding Phillips Norway group fields, also referred to as the greater Ekofisk area fields, are in the southern part of the Norwegian sector of the North Sea. The Ekofisk field produces from a Maastrichtian/Danian chalk reservoir of the Tertiary/Cretaceous periods. The reservoir structure is an elliptical anticline, 6.6 miles long and 3.2 miles wide. The reservoir thickness is between 300 and 1,000 ft, with the top of the reservoir located at 9,500 ft subsea true vertical depth. The reservoir conditions include high pressure, high temperature, and high porosity with low rock-matrix permeability and extensive natural fracturing. Primary oil production from Ekofisk field began in 1971, with all gas reinjected. In 1977, gas sales began; however, gas injection continued for contractually obligated swing-gas volumes in excess of demand. Full-field secondary waterflood operations began in 1987.1 At year-end 1996, 65 producing wells, 36 water-injection wells, and three gas-injection wells were located on four production platforms and two water-injection platforms in the Ekofisk field. Processing, transportation, and accommodation facilities were located on eight additional platforms at the Ekofisk center. Seven surrounding Phillips Norway group fields produce from 77 producing wells located on nine production and processing platforms. Total production for Ekofisk and the surrounding fields at year-end 1996 was approximately 320,000 BOPD and 830 MMscf/D. Oil- and gas-separation and -transportation facilities are centrally located on the Ekofisk complex at Ekofisk field. The Ekofisk II Redevelopment Project is designed to replace the oil- and gas-production and -processing capabilities of the existing Ekofisk complex. This requirement grew out of the high-operating and -maintenance expenses associated with the existing facilities. Other factors of significance were the effects of seafloor subsidence and changing safety regulations. A significant aspect of the Ekofisk field has been reservoir compaction that resulted in seabed subsidence during the areal extent of the reservoir. After 25 years of production, the cumulative subsidence in the center of the field is more than 21 ft. The Ekofisk II Redevelopment Project addresses the economic, maintenance, and safety factors and maintains the economic viability of Ekofisk field and the surrounding Phillips Norway group fields.

Discussion on Integrating Monitoring Data into the Reservoir Management Process

“…Methodologies for reservoir management have been previously reported in numerous books and technical journal articles [1][2][3][4][5] .…”

Mathematical Programming Models and Local Search Algorithms for the Offshore Rig Scheduling Problem

“…Operations research publications applied to rig scheduling (Source: the author). Also in the 90's, Hasle et al (1996), Eagle (1996), Iyer et al (1998), Currie et al (1997a and Currie et al (1997b) issued the rigs scheduling problem. Hasle et al (1996) used the Constraint Reasoning (CR) method for the Well Activity Scheduling (WAS) problem, where resources such as drilling rigs and wireline cranes need to be appointed for several activities, mainly drilling, completation, perforation and logging.…”

“…Their model aimed to decide the number, location and capacities of the platforms, the number and location of wells, the schedules for well and platforms installations, the scheduling of drilling rigs on well platforms and the oil production rate over the time horizon. Last, Currie et al (1997a) and Currie et al (1997b) presented a simplified mixed integer programming (MIP) model for the redevelopment and reservoir management of wells, deciding the projects, wells and drilling rigs to be used in a yearly basis. After these papers were published, the interest in rig scheduling models and algorithms raised.…”

Data-Driven Joint Chance-Constrained Optimization for the Workover Rig Scheduling Problem

“…Furthermore, it had an efficient and easy user interface fully integrated with other systems from the company. Currie et al (1997a) and Currie et al (1997b) implemented a linear programming model for a Norwegian company to integrate and optimize the development decisions. Another resource planning system was developed by Horton and Dedigama (2006) for the Australian oil company Santos Ltd.…”