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.
