A numerical Python model of gas liquid flow in petroleum wells

Abstract

The primary objective of this thesis is to develop a Python script for predicting reservoir production in petroleum wells, based on an existing MATLAB script. This transition is motivated by Python’s cost-effectiveness and open-source nature, offering a free alternative to the commercially licensed MATLAB. There are three codes: one main code that has been translated from the MATLAB script, and two supplementary scripts that are built on the main code. The study investigates the interplay between key parameters, such as well- head pressure, gas fraction, mixture velocity, and boiling pressures, utilizing three distinct Python scripts. The main script iteratively evaluates varying wellhead pressures, while two supplementary scripts explore the effects of boiling pressure on reservoir production and the relationship between gas velocity and pressure gradient. This thesis comprises three key aspects:

• The first aspect of the thesis focuses on validating the Python model against theo- retical expectations for single-phase flow conditions. Results demonstrate the model’s accuracy, highlighting its capacity to simulate realistic fluid dynamics within the well.

• The second aspect examines the influence of wellhead pressures on gas fraction, mix- ture velocity, and boiling pressures. Findings indicate that higher wellhead pressures correlate with lower gas fractions and reduced mixture velocities, aligning with fluid behavior in multiphase flow.

• In the third part, the study explores the interaction between boiling pressures, well- head pressures, and reservoir production. The results reveal complex trends, where increasing boiling pressure initially enhances production due to reduced hydrostatic pressure, but at higher pressures, frictional pressure becomes dominant, diminishing production.

Overall, this thesis provides a robust framework for understanding gas-liquid flow dynamics in petroleum wells, demonstrating the effectiveness of Python for digital modeling in the energy sector. The insights gained contribute to optimizing well operations and enhancing production efficiency. Due to the availability of Python, further development of the models is encouraged.

The primary objective of this thesis is to develop a Python script for predicting reservoir production in petroleum wells, based on an existing MATLAB script. This transition is motivated by Python’s cost-effectiveness and open-source nature, offering a free alternative to the commercially licensed MATLAB. There are three codes: one main code that has been translated from the MATLAB script, and two supplementary scripts that are built on the main code. The study investigates the interplay between key parameters, such as well- head pressure, gas fraction, mixture velocity, and boiling pressures, utilizing three distinct Python scripts. The main script iteratively evaluates varying wellhead pressures, while two supplementary scripts explore the effects of boiling pressure on reservoir production and the relationship between gas velocity and pressure gradient. This thesis comprises three key aspects:

• The first aspect of the thesis focuses on validating the Python model against theo- retical expectations for single-phase flow conditions. Results demonstrate the model’s accuracy, highlighting its capacity to simulate realistic fluid dynamics within the well.

• The second aspect examines the influence of wellhead pressures on gas fraction, mix- ture velocity, and boiling pressures. Findings indicate that higher wellhead pressures correlate with lower gas fractions and reduced mixture velocities, aligning with fluid behavior in multiphase flow.

• In the third part, the study explores the interaction between boiling pressures, well- head pressures, and reservoir production. The results reveal complex trends, where increasing boiling pressure initially enhances production due to reduced hydrostatic pressure, but at higher pressures, frictional pressure becomes dominant, diminishing production.

Overall, this thesis provides a robust framework for understanding gas-liquid flow dynamics in petroleum wells, demonstrating the effectiveness of Python for digital modeling in the energy sector. The insights gained contribute to optimizing well operations and enhancing production efficiency. Due to the availability of Python, further development of the models is encouraged.