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Prudhoe Bay Oil Production Optimization: Using Virtual intelligence Techniques, Stage One: Neural Model Building

Field data from the Prudhoe Bay oil field in Alaska was used to develop a neural network model of the cross-country gas transit pipeline network between the production separation facilities and central gas compression plant. The trained model was extensively tested and verified using 30% of the data that was not used during the training process. The results show good accuracy in reproducing the actual rates and pressures at the separation facilities and at the gas compression plant. The correlation coefficient for rate and pressure were 0.997 and 0.998 respectively. This is the first phase in the development of a tool to maximize total field oil production by optimizing the gas discharge rates and pressures at the separation facilities. The second phase, development of a state-of-the-art genetic algorithm to perform the optimization, is currently being tested and will be the subject of a future paper. Background Prudhoe Bay has approximately 800 producing wells flowing to eight remote, three-phase separation facilities (flow stations and gathering centers). High-pressure gas is discharged from these facilities into a cross–country pipeline system flowing to a central compression plant. Figure 1 illustrates the gas transit network between the separation facilities and the inlet to the compression plant. Fuel gas supply (at the flowstations and gathering centers) and artificial lift gas supply for the lift gas compressors at GC1 are taken off the gas transit line upstream of the compression plant. This reduces the feed gas rate and pressure at the inlet to the compression plant. Gas feeding the central compression plant is processed to produce natural gas liquids and miscible injectant. Residue gas from the process is compressed further for reinjection into the reservoir to provide pressure support. Business Motivation Ambient temperature has a dominant effect on compressor efficiency and hence total gas handling capacity and subsequent oil production. Figure 2 illustrates the range of daily average temperatures from 1990-2000, and the actual daily average for 2001 and 2002. Observed temperature variations during a 24-hour period can be as high as 40 degrees Fahrenheit (F). Figure 3 is a curve fit to total shipped gas rate to the compression plant versus ambient temperature for 2001. A significant reduction in gas handling capacity is observed at ambient temperatures above 0F. Individual well GOR ranges between 800 scf/stb and 35,000 scf/stb, with the lower GOR wells in the water-flood area of the field and higher GORs in the gravity drainage area. Gas compression capacity is the major bottleneck to production at Prudhoe Bay and typically field oil rate will be maximized by preferentially producing the lowest GOR wells. As the ambient temperature increases from 0 and 40F, the maximum (or “marginal”) GOR in the field decreases from approximately 35,000 to 28,000 scf/stb. A temperature swing from 0 to 40F in one day equates to an approximate oil volume reduction of 40,000 bbls, or 1000 bopd per F rise in temperature. The reduction in achievable oil rate, per degree Fahrenheit increase in temperature, increases with ambient temperature. This is due in part to the increase in slope of the curve of shipped gas versus temperature, and also to the reduction in limiting or “marginal” GOR as gas capacity decreases. At 2 MOHAGHEGH, HUTCHINS & SISK SPE 77659 higher temperatures, larger gas cuts are required at lower GORs to stay within compression limits. As ambient temperature rises, the inlet pressure at the compression plant increases. High inlet pressure can create backpressures at the separation facilities that will cause flaring. This is not environmentally acceptable and is avoided by cutting gas production. However, sometimes, ambient temperatures change so rapidly that significant gas cuts are necessary to avoid flaring. A similar problem occurs if a compressor experiences unexpected mechanical failure. The ability to optimize the facilities in response to ambient temperature swings, compressor failures or planned maintenance is a major business driver for this project. Proactive management of gas production also reduces unnecessary emissions. To maximize total oil rate under a variety of field conditions it is first necessary to understand the relationship between the inlet gas rate and pressure at the central compression plant, and the gas rates and discharge pressures into the gas transit line system at each of the separation facilities. This paper describes the development of a series of neural network models to describe these relationships. Field oil rate will be impacted by the manner in which gas is distributed between facilities. A state-of-the-art genetic algorithm based optimization tool (subject of a future paper) has been developed based on the neural network models described in this paper. The goal of the optimization tool is to determine the gas discharge rates and pressures at each separation facility that will maximize field oil rate at a given ambient temperature, using curves of oil versus gas at each facility (based on GOR sorts). These curves are developed by iteration with a recently developed model of the surface piping network from wellhead to gathering center / flowstation for Prudhoe Bay (Reference 1). Gas capacity constraints start to affect oil production at about 0 deg F, with increasing impact as the temperature increases. The estimated benefit of this tool for optimizing oil rate during temperature swings and equipment maintenance is 1-2 MBOPD for 75% of the year. Introduction Attempts were made to develop a deterministic model of the gas transit system using commercial pipeline modeling software. However, it was extremely difficult to obtain sufficient historical data to validate the model. Development of a neural network model was undertaken to determine if this approach would provide a robust description of the observed gas rates and pressures with less stringent data requirements. For this initial test it was assumed that there was negligible hysteresis in the system. Initial results were very encouraging, suggesting that this is a valid approach, albeit limited to the data range used to train the model. Methodology In this section the methodology of the approach used in this paper is presented. The methodology has been divided into two sections.