Sand production in the life of oil and gas reservoirs is inevitable, as it is co-produced with oil and gas from the reservoirs. Its deposition in petroleum pipelines poses considerable risk to production and can lead to pipe corrosion and flow assurance challenges. Therefore, it is important that pipe flow conditions are maintained to ensure sand particles are not deposited but in continuous motion with the flow. The combination of minimum gas and liquid velocities that ensure continuous sand motion is known as the minimum transport condition (MTC). This study investigates the effect both of sand particle diameter and concentration on MTC in gas/liquid stratified flow in a horizontal pipeline. We used non-intrusive conductivity sensors for sand detection. These sensors, used for film thickness measurement in gas/liquid flows, was used here for sand detection. We found that MTC increases with increase in particle diameter for the same concentration and also increases as the concentration increases for the same particle diameter. A correlation is proposed for the prediction of sand transport at MTC in air-water flows in horizontal pipes, by including the effect of sand concentration in Thomas's lower model. The correlation accounts for low sand concentrations and gave excellent predictions when compared with the experimental results at MTC.

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The correct prediction of minimum transport condition (MTC) is of great importance to the oil and gas industry. The sand deposition is an associated problem of multiphase transportation of oil, gas and or solid. The purpose of this work is to investigate the predictive capability of three different data-driven approaches: Artificial neural networks (ANN), Adaptive neuro-fuzzy inference system or adaptive network-based fuzzy inference system (ANFIS) and response surface methodologies (RSM). The models were developed using182 experimental data points with input parameters such as liquid superficial velocity, pipe diameter, particle size, pipe inclination and the output parameter predicted is the minimum transport condition (velocity) for sand particles. The developed models were compared with existing models. The results showed that the three methods performed creditably well in the prediction of MTC with ANFIS having the highest predictive capability with an R2 value of 0.99997 and an average error value of 0.00035836 compared with ANN and RSM having R2 value of 0.9998 and 0.9973 respectively. The three data-driven techniques investigated in this study also outperformed published correlations for the prediction of MTC. The findings from this research can be invaluable for the effective and robust management of sand transport in multiphase flow systems.Keywords— Artificial Intelligence, Fuzzy Inference System, Model, Minimum Transport Condition, Optimization methods, Response Surface Methodology

Producing sand during oil and gas production is unavoidable. Sand is produced along with oil and gas and its deposition in pipelines is a significant risk as it can cause pipe corrosion and flow assurance difficulties. It is therefore key that flow conditions are sustained to guarantee lack of deposition of sand particles. The minimum combination of mixture velocities that guarantee continuous sand motion is known as the minimum transport condition (MTC). Here, we investigate the effect both of sand concentration and particle diameter on MTC in a horizontal pipeline in the stratified flow regime. Non-intrusive conductivity probes were utilised for the detection of sand. These sensors are commonly used for the measurement of film thickness in gas and liquid flows, but we demonstrate their use here for sand detection after suitable calibration. It was observed that at the ultra-low sand concentrations of our experiments, MTC increases with both sand particle diameter and concentration. We developed a new correlation based on Thomas's lower model but included a sand concentration correction term that also applies at low particle concentrations. The correlation's predictions compared favourably with our measurements at MTC as well as data obtained from the open literature at medium concentrations.

Slug translational velocity, described as the velocity of slug units, is the summation of the maximum mixture velocity in the slug body and the drift velocity. Existing prediction models in literature were developed based on observation from low viscosity liquids, neglecting the effects of fluid properties (i.e., viscosity). However, slug translational velocity is expected to be affected by the fluid viscosity. Here, we investigate the influence of high liquid viscosity on slug translational velocity in a horizontal pipeline of 76.2-mm internal diameter. Air and mineral oil with viscosities within the range of 1.0–5.5 Pa·s were used in this investigation. Measurement was by means of a pair of gamma densitometer with fast sampling frequencies (up to 250 Hz). The results obtained show that slug translational velocity increases with increase in liquid viscosity. Existing slug translational velocity prediction models in literature were assessed based on the present high viscosity data for which statistical analysis revealed discrepancies. In view of this, a new empirical correlation for the calculation of slug translational velocity in highly viscous two-phase flow is proposed. A comparison study and validation of the new correlation showed an improved prediction performance.