New Method To Estimate Surface-Separator Optimum Operating Pressures
The significance of setting optimal surface separation pressures cannot be overemphasized in surface-separation design for the purpose of maximizing the surface liquid production from the wellstream feed. Usually, classical pressure-volume-temperature (PVT) analysis of reservoir fluids provides one or several separator tests through which the optimum separator pressures are estimated. In case separator tests are not available, or the limited numbers of separator tests are not adequate to determine the optimum separator pressures, empirical correlations are applied to estimate the optimum separator pressures. The empirical correlations, however, have several disadvantages that limit their practical applications.
In this study, we approached the problem with a rigorous method with a theoretical basis. According to the gas/liquid equilibrium calculation, the optimum separator pressures were determined. Comparisons of our results with experimental data indicated that the proposed method can simulate the separator tests very well. Because the method has a theoretical basis and does not require existing two-stage or multiple-stage separator-test data as in the application of empirical correlations, it potentially has wide applications in practice for a variety of conditions and yields a more optimal separation scheme than the empirical correlations. Furthermore, the method is independent of reservoir fluid. In the event that separator tests are available from fluid analysis, our method can be used as a quality-control tool. Because the setting for optimal separation pressures vary as the composition of the wellstream changes during the field life, our method provides a quick and low-computational-cost approach to estimate optimum separator pressures corresponding to different compositions.
Corrosion Will Occur, Whether You Like It or Not
Just like Houston’s summer heat, corrosion of metal surfaces will occur—whether you like it or not. To help you better understand corrosion, these papers describe using water surveys in a production/injection plant, testing the effectiveness of mitigation, and data evaluation using machine learning.
Machine Learning Enhances Evaluation of Oil and Gas Assets
Using machine learning (ML), image recognition, and object detection, the use of ML on algorithms to recognize objects and describe their condition were investigated—offering new possibilities for performing inspection and data gathering to evaluate the technical condition of oil and gas assets.
Water Survey Assesses Souring and Corrosion at Production/Injection Plant
At a water treatment plant for an onshore oil field in northern Germany, formaldehyde injection was started in 2015 as a biocide. The goal of this study was to understand the chemical parameters and microbial distribution in the water system and whether formaldehyde injection was effective.
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03 October 2019
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03 October 2019