Predicting Dynamic Contact Angle in Immiscible Fluid Displacement: A Machine Learning Approach for Subsurface Flow Applications

Abstract

Immiscible fluid−fluid displacement dynamics is a crucial element to understanding and engineering many subsurface flow applications, including enhanced oil recovery and carbon dioxide geological sequestration. Although there are several interfacial properties that govern such a displacement dynamic, the wettability has been considered a dominant factor. Owing to its complex coaffinity among the three phases (i.e., solid−fluid−fluid) and difficulty to be characterized accurately and efficiently, the wettability (defined as the contact angle: θ) determination is of interest in the current study with aim toward machine learning (ML) approach.In the current research, four experimental packages of fluid displacement at 1D capillary scale served as data sets for ML examination on the θ predictability. Via digital image processing, fluid traveling length at a given time was extracted, and the theoretical θ was calculated as ground truth for the modeling, with input features being fluid traveling length, displacing velocity, and the interfacial tension. Random forest (RF) and multilayer perception (MLP) were selected for the modeling due to their appropriate characteristics to the investigated data (being nonlinear relation). The prediction results showed that RF apparently outperformed MLP on the θ prediction, reflecting its best ability to manage missing values and outliers. Although more input features analyzed (from two to three features) did yield a better prediction, the best model remains RF. Sensitivity on the key parameters of displacing velocity and the interfacial tension was also analyzed, where the results confirm the model prediction agreement with theories. The study demonstrated how ML model can be an alternative tool to elucidate the fluid displacement in subsurface, with additional potential for autonomously improving the deep underground flows, converging a new concept of "augmented" artificial intelligence.

Relative Permeability-Dictated Huff-n-Puff Technique in Tight Reservoirs: Suitability and Limitation toward Field Implementation

“…To manipulate such a capillary-controlled fluid flow in tight reservoirs, alterations in the rock–oil–water wettability and/or the oil–water interfacial tension are normally conducted by adding some chemical additives into the injection fluid . The consequent relative flows of oil and water phases are thus changed accordingly, which is characterized by their respective relative permeability ( k r )an ability of each fluid to flow compared to another fluid in the same reservoir environment.…”