Evaluating essential features of proppant transport at engineering scales combining field measurements with machine learning algorithms

The characterization of the proppant transport at a field-engineering scale is still challenging due to the lack of direct subsurface measurements. Features that control the proppant transport may link the experimental and numerical observations to the practical operations at a field scale. To improve the numerical and laboratory simulations, we propose a machine-learning-based workflow to evaluate the essential features of proppant transport and their corresponding calculations. The proppant flow in fractures is estimated by applying the Gated recurrent unit (GRU) and Support-vector machine (SVM) algorithms to the measurements obtained from shale gas fracturing operations. Over 430,000 groups of fracturing data are collected and pre-processed by the proppant transport models to calculate key features, including settlement, stratified flow and inception of settled particles. The features are then fed into machine learning algorithms for pressure prediction. The root mean squared error (RMSE) is used as the criterion for ranking selected features via the control variate method. Our result shows that the stratified-flow feature (fracture-level) possesses better interpretations for the proppant transport, in which the Bi-power model helps to produce the best predictions. The settlement and inception features (particle-level) perform better in cases that the pressure fluctuates significantly, indicating that more complex fractures may have been generated. Moreover, our analyses on the remaining errors in the pressure-ascending cases suggest that (1) an introduction of the alternate-injection process, and (2) the improved calculation of proppant transport in complex fracture networks and highly-filled fractures will be beneficial to both experimental observations and field applications.

Numerical Analysis of Stratified and Slug Flows

The main purpose of this study is to compare two-dimensional (2D) and three-dimensional (3D) two-phase models for both stratified and slug flows. These two flow regimes interest mainly the petroleum and chemical industries. The volume of fluid (VOF) approach is used to predict the interface between the two-phase flows. The stratified turbulent flow corresponds to the oil-water phases through a cylindrical pipe. To simulate the turbulent stratified flow, the k − ω turbulence model is used. The slug laminar flow concerns the kerosene-water phases through a rectangular microchannel. The simulated results are validated using the previous experimental results available in the literature. For the stratified flow, the axial velocity and the water volume fraction profiles obtained by 2D and 3D models approximate the measurement profiles at the same test section. Also, the T-junction in a 2D model affects only the inlet vicinity. For downstream, the 2D and 3D models lead to the same axial velocity and water volume distribution. For the slug flow, the simulated results show that the 3D model predicts the thin film wall contrary to the 2D model. Moreover, the 2D model underestimates the slug length.