Technology Focus: Flow Assurance (November 2021)

I sincerely hope that all JPT readers and your families, peers, and employers remain safe and healthy and have work as they read this year’s Flow Assurance feature. Flow-assurance effects from slug-flow engineering, design, maintenance, and operations technical concerns still create and sustain challenging technical issues requiring safe, economical solutions for both onshore unconventional and offshore conventional production facilities. The recurring long-term mitigation of slugging and various flow-assurance phenomena—along with the prevention of wax, erosion, asphaltenes, corrosion, and salt deposition—and gas hydrate prediction and handling still demand attention and considerable project technical effort. Slug-flow assessments present opportunities for significant optimization in work flows to target governing operating scenarios. Paper OTC 30172 describes an integrated iterative approach between the flow-assurance and pipeline-engineering disciplines to streamline the work flow based on the value or cost associated with changes in input parameters that affect pipeline fatigue-assessment outcomes. Case studies on two multiphase pipelines are presented to illustrate this design approach. The results show that early identification of the key pipeline profile features and dominating spans for pipeline slugging fatigue assessments facilitated the optimization of slug-flow modeling and reduced computational time. The second paper, SPE 203448, presents decision trees that are considered as nonparametric machine-learning models. The data sets used in training and testing the predictive model are experimental and were collected from literature. Air/kerosene and air/water mixtures were used in obtaining the experimental data points. Results show that the proposed boosted decision tree regression (BDTR) model outperforms the best empirical correlations and the fuzzy-logic model used in estimating liquid holdup in gas/liquid multiphase flows. For the built model, the most important input feature in estimating liquid holdup is the superficial gas velocity. The empirical correlations developed in the past for identifying liquid holdup in multiphase flow can be applied only under the flow conditions by which they were originally developed. However, this machine-learning model does not suffer from this limitation. The third paper, OTC 31298, describes a slugging-control solution that was rejected because of the use of a pseudovariable as the principal control point. A novel control scheme, therefore, was developed and tested on simulations for both hydrodynamic slugging and severe riser-induced slugging in an Angolan field. The project implemented the novel active slugging control using a topsides control valve and topsides instrumentation. While a pseudovariable, a pseudoflow controller, was used, it was part of a cascade scheme such that the principal control variable was a real top-side pressure measurement. Upon com-missioning, slugging at the facility was found to be more severe than anticipated during design, but the novel active slug-control scheme was effective in controlling incoming slugs. The desire to understand better how to describe and improve flow assurance and multiphase flow for both offshore and onshore facilities drives new production technology research, applications, and approaches. The three papers listed for additional reading focus on developing further new analytical tools while providing safe, cost-effective, and reliable operations for flow assurance. I hope you find them as interesting as I did. In addition, I invite you to join the Flow Assurance Technical Section to augment your learning. Recommended additional reading at OnePetro: www.onepetro.org. OTC 30177 - Real-Time Online Hydrate Monitoring and Prevention in Offshore Fields by Syahida Husna Azman, Petronas, et al. SPE 201316 - Modeling Dynamic Loads Induced by Slug Flows Considering Gas Expansion Caused by the Pressure Gradient in a Free Span Horizontal Hanging Pipeline by Gabriel Meneses Santos, Universidade Estadual de Campinas, et al. OTC 31238 - Taylor Bubbles of Viscous Slug Flow in Inclined Pipes by Longtong Abednego Dafyak, University of Nottingham, et al.

Decision Tree Regressions Estimate Liquid Holdup in Two-Phase Gas/Liquid Flows

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 203448, “Decision-Tree Regressions for Estimating Liquid Holdup in Two-Phase Gas/Liquid Flows,” by Meshal Almashan, SPE, Yoshiaki Narusue, and Hiroyuki Morikawa, University of Tokyo, prepared for the 2020 Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, held virtually 9–12 November. The paper has not been peer reviewed. In the authors’ study, a machine-learning predictive model—boosted decision tree regression (BDTR)—is trained, tested, and evaluated in predicting liquid holdup (HL) in multiphase flows in oil and gas wells. Results show that the proposed BDTR model outperforms the best empirical correlations and the fuzzy-logic model used in estimating HL in gas/liquid multiphase flows. Using the BDTR model with its interpretable representation, the heuristic feature importance of the input features used in building the model can be determined clearly. Introduction Machine-learning approaches in predicting HL in multiphase flows have been recently studied to improve prediction accuracy compared with existing empirical correlations. However, these approaches ignore the heuristic feature importance of the input parameters to the predicted HL values. The heuristic feature importance can help provide better insight into the issues associated with HL studies, such as the liquid-loading phenomenon. To the best of the authors’ knowledge, the present study is the first work that shows how decision-forest regression predictive models can predict HL accurately. Data Acquisition The performance and the predictive power of a machine-learning model relies greatly on the quality and completeness of the data set used in building the model. The data sets used in training and testing the predictive model are experimental and were collected from the literature (111 data points). Air/kerosene and air/water mixtures were used in obtaining the 111 experimental data points. In this study, this data set is divided into three different subsets: training, validation, and testing. The data sets consist of the properties of HL, the superficial gas velocity (Vsg), the superficial liquid velocity (Vsl), pressure, and temperature (T). The statistical measures of the data sets are shown in Table 1 of the complete paper.

Understanding the Effect of Stepwise Irrigation on Liquid Holdup and Hysteresis Behavior of Unsaturated Ore Heap

Liquid is a crucial medium to contain soluble oxygen, valuable metal ions, and bacteria in unsaturated heap leaching. Liquid retention behavior is the first critical issue to be considered to efficiently extract low-grade minerals or wastes. In this study, the residual liquid holdup of an unsaturated packed bed was quantitatively discussed by liquid holdup (θ), residual liquid holdup (θresidual), relative liquid holdup (θ’), and relative porosity (n*) using the designed measuring device. The detailed liquid holdup and the hysteresis behavior under stepwise irrigation are indicated and discussed herein. The results show that relative porosity of the packed bed was negatively related to particle size, and intra-particle porosity was more developed in the −4.0 + 2.0 mm packed bed. The higher liquid retention of the unsaturated packed bed could be obtained by using stepwise irrigation (incrementally improved from 0.001 to 0.1 mm/s) instead of uniform irrigation (0.1 mm/s). It could be explained in that some of the immobile liquid could not flow out of the unsaturated packed bed, and this historical irrigation could have accelerated formation of flow paths. The θ was sensitive to superficial flow rate (or irrigation rate) in that it obviously increased if a higher superficial flow rate (u) was introduced, however, the θresidual was commonly affected by n* and θ’. Moreover, the liquid hysteresis easily performed under stepwise irrigation condition, where θ and θresidual were larger at u of the decreasing flow rate stage (DFRS) instead of u of the increasing flow rate stage (IFRS). These findings effectively quantify the liquid retention and the hysteresis behavior of ore heap, and the stepwise irrigation provides potential possibility to adjust liquid retention conditions.

Experimental Studies of Liquid Holdup in the Impeller of Electric Submersible Pump

Artificial lift of oil by electric submersible pumps (ESP) is often complicated by free gas in production. Free gas content in production leads to ESP performance degradation in rate and head. Gas slip in the ESP impeller is one of the reasons of ESP performance degradation. Thus, the goal of the work is to determine the gas slip coefficient i.e. liquid holdup in the ESP impeller. It is known that a gas-liquid mixture (GLM) flow characterized by a slippage effect. Gas slippage relative to the liquid determines the GLM structure (bubble, dispersed-bubble, slug, stratified or annular), as well as the difference between the GLM densities calculated by liquid holdup or liquid volume content. Special stand was designed and created to determine the liquid holdup at the Department of Oil Fields Development and Operation of Gubkin University. Liquid holdup in the impeller of the ESP was measured by the method of cutting off the flow. This paper shows the results of experimental studies of liquid holdup and gas slip velocity in the ESP impeller (ESP5-50) at a rotational speed n = 2997 rpm, at an absolute intake pressure Pin = 0.4 MPa. The dependence of the liquid holdup on liquid volume content (i.e. the dependence of the gas void fraction on gas volume fraction) was determined for the model GLM "water-air", "water-surfactant-air" with different foaming capacity. The degradation of the ESP characteristics, boundaries of surging and gas locking limits are determined taking into account liquid holdup. The dependence of gas holdup was experimentally obtained over the entire range of ESP operation (from 0.5∙Qopt to Qmax). A comparison of the obtained correlation with existing models is presented too. A new correlation for predicting liquid holdup in the ESP impeller for the low-rate wells operation is obtained. A new approach to determining the liquid holdup and consequently gas slip velocity in the ESP impeller is proposed.

Experimental and Modeling Studies on Pressure Gradient Prediction for Horizontal Gas Wells Based on Dimensionless Number Analysis

Accurate prediction of pressure gradient in gas wells is the theoretical basis of gas well performance analysis, production optimization and deliquification technologies design. Experiment is the best access to characterize the flow behavior of gas wells. For low-pressure experimental investigation and gas wells, the most difference is the pressure (gas density), which could lead to totally different flow behavior. Dimensionless numbers are often used in the flow pattern maps to account for the flow similarities at different conditions, which means liquid holdup in the high pressure can be also predicted at low pressure conditions if we choose proper dimensionless numbers for pressure scaling up. However, no studies have focused on this point before. Besides, gas wells have high GLR, most empirical models were intended to developed for oil wells, which have greater weight in low GLR, decreasing the accuracy in gas wells. In order to predict the pressure gradient in horizontal gas wells, an experimental investigation of gas-water flow has been conducted. The experimental test matrix was designed to cover all the flow patterns. The experiment was conducted in a 5-m long pipe. The liquid holdup and pressure gradient were measured. Subsequently, the effect of gas velocity, liquid velocity, pipe diameter, and inclined angle on liquid holdup was analyzed. Then the dimensionless numbers proposed in the literature have been investigated and analyzed for pressure scaling up. Finally, a comprehensive model was established, which is developed for prediction pressure drop in gas wells. Some field and experimental data were provided to evaluate the new model. The results show that the Duns-Ros dimensionless number was not proper for pressure scaling up while the Hewitt-Robert Number performs best. Compared to widely used pressure gradient models with field data, the new model with Hewitt-Robert Number performed best, which shows that it is capable of dealing with prediction of pressure gradient in gas wells.