Optimizing Pigging Frequency For Dewaxing Of Crude Oil Pipelines

Abstract

Wax deposition is a frequently encountered issue during the transportation of waxy crude oils. The wax deposit can reduce the effective area for oil flow. If not handled properly, the wax deposit can be too thick and hard and makes it impossible to be removed by pigging. So it is necessary to understand the nature of the wax deposits and study the deposition process. Hence, the flow assurance in the crude oil pipelines is very important due to the precipitation of the solid phase of wax on the pipe wall creating blockages and reduces or stops production. There are several mitigation methods were used in the different oil fields around the world to reduce wax deposition such as inhibitors and thermal insulation in addition to pigging operation. For this case study and to improve the transporting conditions, the crude is mixed with diesel to dissolve wax and to enhance flow, many theoretical and experimental studies have been conducted to understand the physics of wax deposition and to predict the deposit growth rate and thickness. The models are based on heat and mass transfer mechanisms in the bulk flow as well as the internal diffusion mechanism. This work describes the underlining wax models implemented in OLGA, depending on the field data of the study. OLGA software was used to simulate the wax deposition process (location and thickness) to predict the wax deposition tendencies and gives the recommended optimum pigging frequency. Steady State Operation for non-pigging and pigging operation at three different flowrates were applied to predict liquid/water hold-up and to check water slugging and pigging characteristics has been included. Different scenarios for Wax deposition and pigging frequency issues at three different flowrates has been implemented and created with respect to weather (summer and winter), including studying the effect of changing ambient temperature to match the actual wax thickness and quantities as per wax received at pig receiver trap as well as to determine an optimal pigging frequency. The findings, the model prediction results prove that the wax is distributed in a short, localized accumulation along the first quarter of pipeline.