Gas Lift Nodal-Analysis Model Provides Economical Approach to Optimization

Abstract

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 171345, “Gas Lift Nodal-Analysis Model—Economical Optimization Approach,” by Mustafa Al Lawati, Occidental Petroleum, prepared for the 2014 SPE Artificial Lift Conference and Exhibition—North America, Houston, 6–8 October. The paper has not been peer reviewed. Field X consists of 17 scattered clusters of more than 100 oil wells producing from different reservoirs. Rapid expansion of the network has made the lifting network extremely busy and has resulted in many lowlifting- gas-rate ends. An easy solution is to increase lifting-gas use, but this is expensive. A more economical option is to restructure the gas lift surface pipelines, on the basis of proper engineering, by rerouting the surface network to reduce branching and enhance lifting-gas pressures and rates while maintaining the same level of lifting-gas use. Introduction The main gas lift 4-in. line connects Clusters A through C with the compressor. The main line is branched with 2-in. lines to every well in the cluster to deliver the required gas for lifting. As the number of wells increased dramatically, more low-rate and -pressure branches of the 2-in. lines were built. This was the main reason behind the loss of lifting efficiency. An increase of water cut in these clusters was another reason for loss of vertical-lift efficiency. Considering the operational challenges of the field, the easy yet expensive solution was to increase the volumes of lifting gas, knowing that 30 MMscf/D of gas already was used for lifting. A more-practical and -economical approach was, first, to build a numerical optimization model that could identify the low-pressure and -rate ends in the gas lift network on the basis of nodal analysis. The next step was to modify the gas lift network by rerouting the surface pipelines to help reduce any network branching and enhance lifting-gas pressures and rates accordingly across the network while maintaining the same volume of gas being used. Focusing on the three clusters— A, B, and C—pipeline rerouting and restructuring was implemented to debottleneck constraints at the surface network and to deliver lifting gas to high-productivity- index (PI) wells at optimum rates to optimize gas lift injection depth. Figs. 1 through 3 show the modifications made to these clusters. The main line is a 4-in. gas lift line, and the branched lines are all 2-in. lines. In Cluster A, a new 2-in. line was extended to the high-PI wells. The pipeline is represented in Fig. 1 by a dashed line. This allows for the delivery of higher lifting-gas rates, which helped optimize vertical-lift performance (VLP) of the wells by lifting from a deeper injection point while maintaining the same rate of lifting-gas use. The method of extending a new 2-in. line to high-PI wells was implemented again in Clusters B and C (Figs. 2 and 3, respectively). Methodology Pressure/Volume/Temperature (PVT) Characterization. The fluid PVT model is the first component of the integrated-nodal- analysis model. In this case, a surface fluid sample of Reservoir X from Cluster A was collected from the separator and tested in the laboratory. Later, the sample was modeled and characterized to evaluate the pressure-drop regime the fluid experiences along the production system, from the reservoir to the production separator. The characterization process includes the following: mass- balance calculation, compositional analysis, and entering the PVT laboratory data for matching to the equation of state. Reservoir Material-Balance Model. The reservoir material-balance model is the second component in the integrated model. To perform nodal analysis throughout the gas lift system, defining the production profile and static pressure of the tank (reservoir) from which the gas lift wells in Cluster A are producing is essential because defining an exact reservoir pressure is the starting point of any nodal analysis.