An experimental investigation of single Taylor bubbles rising in stagnant and downward flowing non-Newtonian fluids was carried out in an 80 ft long inclined pipe (4°, 15°, 30°, 45° from vertical) of 6 in. inner diameter. Water and four concentrations of bentonite–water mixtures were applied as the liquid phase, with Reynolds numbers in the range 118 < Re < 105,227 in countercurrent flow conditions. The velocity and length of Taylor bubbles were determined by differential pressure measurements. The experimental results indicate that for all fluids tested, the bubble velocity increases as the inclination angle increases, and decreases as liquid viscosity increases. The length of Taylor bubbles decreases as the downward flow liquid velocity and viscosity increase. The bubble velocity was found to be independent of the bubble length. A new drift velocity correlation that incorporates inclination angle and apparent viscosity was developed, which is applicable for non-Newtonian fluids with the Eötvös numbers ($E_0$) ranging from 3212 to 3405 and apparent viscosity (${\mu }_{app}$) ranging from 0.001 Pa∙s to 129 Pa∙s. The proposed correlation exhibits good performance for predicting drift velocity from both the present study (mean absolute relative difference is 0.0702) and a database of previous investigator’s results (mean absolute relative difference is 0.09614).

Summary: This study aims to investigate the hole-cleaning process during the flow of a drilling fluid consisting of a gas and a liquid phase through a horizontal annulus. Experiments have been conducted using the Middle East Technical University (METU) multiphase flow loop under a wide range of air- and water-flow rates while introducing cuttings into the annulus for different amounts. Data have been collected for steady-state conditions (i.e., liquid, gas, and cuttings injection rates are stabilized). Collected data include flow rates of liquid and gas phases, frictional pressure drop inside the test section, local pressures at different locations in the flow loop, and high-speed digital images for identification of solid, liquid, and gas distribution inside the wellbore. Digital imageprocessing techniques are applied on the recorded images for volumetric phase distribution inside the test section, which are in dynamic condition. The effects of liquid and gas phases are investigated on cuttings-transport behavior under different flow conditions. Observations showed that the major contribution for carrying the cuttings along the wellbore is the liquid phase. However, as the gas-flow rate is increased, the flow area left for the liquid phase dramatically decreases, which leads to an increase in the local velocity of the liquid phase causing the cuttings to be dragged and moved, or a significant erosion on the cuttings bed. Therefore, increase in the flow rate of gas phase causes an improvement in the cuttings transport although the liquid-phase flow rate is kept constant. On the basis of the experimental observations, a mechanistic model that estimates the total cuttings concentration and frictional pressure loss inside the wellbore is introduced for gasified fluids flowing through a horizontal annulus. The model estimations are in good agreement with the measurements obtained from the experiments. By using the model, minimum liquid- and gas-flow rates can be identified for having an acceptable cuttings concentration inside the wellbore as well as a preferably low frictional pressure drop. Thus, the information obtained from this study is applicable to any underbalanced drilling operation conducted with gas/liquid mixtures, for optimization of flow rates for liquid and gas phases to transport the cuttings in the horizontal sections in an effective way with a reasonably low frictional pressure loss.

Effectively transporting drilled cuttings to the surface is a vital part of the well construction process. Usually, mechanistic models are used to estimate the cuttings concentration during drilling. Based on the results from these model, operational parameters are adjusted to mitigate any nonproductive time events such as pack-off or lost circulation. However, these models do not capture the underlying complex physics completely and frequently require updating the input parameters, which is usually performed manually. To address this, in this study, a data-driven modeling approach is taken and evaluated together with widely used mechanistic models. Artificial neural networks are selected after several trials. The experimental data collected at The University of Tulsa–Drilling Research Projects (in the last 40 years) are used to train and validate the model, which includes a wide range of wellbore and pipe sizes, inclinations, rate-of-penetration values, pipe rotation speeds, flow rates, and fluid and cuttings properties. It is observed that, in many cases, the data-driven model significantly outperforms the mechanistic models, which provides a very promising direction for real-time drilling optimization and automation. After the neural network is proven to work effectively, an optimization attempt to estimate flow rate and pipe rotation speed is introduced using a genetic algorithm. The decision is made considering minimizing the required total energy for this process. This approach may be used as a design tool to identify the required flow rate and pipe rotation speed to acquire effective hole cleaning while consuming minimal energy.

As explorations advance and drilling techniques become more innovative, complex and challenging trajectories arise. In consequence, cuttings transport has continued to be a subject of interest because, if the drilled cuttings cannot be removed from the wellbore, drilling cannot proceed for long. Therefore, efficient cleaning of highly inclined and horizontal wellbores is still