Modelling of liquid loading in gas wells using a software-based approach

Liquid loading is the most common operational problem influencing gas well productivity for the petroleum operator. Liquid loading is defined as an operational constraint that is associated with gas wells where the major driving mechanism for hydrocarbon production is by the associated gas-driven mechanisms. Liquid loading occurs when liquid accumulated in the tubing or casing results in the gas velocity lower than the critical value (the minimum velocity required for gas to push the liquid out of the gas well), which overtime leads to a hydrostatic back pressure greater than the formation pressure of the well, thereby limiting the flow of gas into the well. The continuous build-up of pressure from liquid loading eventually minimizes well productivity and expensive work over operations. However, current mathematical models to predict liquid loading are flawed with varying inaccuracies and depending on the models deployed will ultimately lead to loss of production time and well productivity. In our work we present prediction of liquid loading using a software-based model incorporating the particle swarm optimization algorithm, genetic algorithm, and artificial neural network and Bayesian neural network algorithms applications. The results of our research findings show that artificial neural network software-based model with a simulated accuracy of 93% and 92% for test and trained data, respectively, outperformed the particle swarm optimization data-driven model with a simulated sensitivity accuracy of 92% and 83%, and genetic algorithm data-driven models with a simulated accuracy of 89% and 83%. The Bayesian neural network was postulated as a robust model because of its simplicity shown to have simulated accuracy of 77% and 73% for train and test data, respectively. Thus software-based code environment and data-driven model developed and presented in this paper may resolve many of current deficiencies and gaps in the current technical literature to predict liquid loading with high precision offering saving in millions of dollars to the operators.