Search Results (3)

Entrapped air in porous media can significantly affect water flow but simulations of air entrapment are still challenging. We developed a pore-network model using quasi-static algorithms to simulate air entrapment during spontaneous wetting and subsequent drainage processes. The model, implemented in OpenPNM, was tailored to replicate an experiment conducted on a medium-sized unconsolidated sand sample. We started building the model with three types of relatively small networks formed by 54,000 pore bodies which we used to calibrate basic network topological parameters by fitting the model to the water retention curve and the saturated hydraulic conductivity of the sand sample. Using these parameters, along with X-ray image data (µCT), a larger network formed by over 250,000 pore bodies was introduced in the form of stacked sub-networks where topological parameters were scaled along the z-axis. We investigated the impact of two different contact angles on air entrapment. For a contact angle of 0, the model showed good agreement with the experimental data, accurately predicting the amount of entrapped air and the saturated hydraulic conductivity. On the contrary, for a contact angle of π/4, the model provided reasonable accuracy for saturated hydraulic conductivity but overestimated the amount of entrapped air. Overall, this approach demonstrated that a reasonable match between simulated and experimental data can be achieved with minimal computational costs. Full article

Data-driven models with some evolutionary optimization algorithms, such as particle swarm optimization (PSO) and ant colony optimization (ACO) for hydraulic fracturing of shale reservoirs, have in recent times been validated as one of the best-performing machine learning algorithms. Log data from well-logging tools and physics-driven models is difficult to collate and model to enhance decision-making processes. The study sought to train, test, and validate synthetic data emanating from CMG’s numerically propped fracture morphology modeling to support and enhance productive hydrocarbon production and recovery. This data-driven numerical model was investigated for efficient hydraulic-induced fracturing by using machine learning, gradient descent, and adaptive optimizers. While satiating research curiosities, the online predictive analysis was conducted using the Google TensorFlow tool with the Tensor Processing Unit (TPU), focusing on linear and non-linear neural network regressions. A multi-structured dense layer with 1000, 100, and 1 neurons was compiled with mean absolute error (MAE) as loss functions and evaluation metrics concentrating on stochastic gradient descent (SGD), Adam, and RMSprop optimizers at a learning rate of 0.01. However, the emerging algorithm with the best overall optimization process was found to be Adam, whose error margin was 101.22 and whose accuracy was 80.24% for the entire set of 2000 synthetic data it trained and tested. Based on fracture conductivity, the data indicates that there was a higher chance of hydrocarbon production recovery using this method. Full article

The relationship between entrapped air content and the corresponding hydraulic conductivity was investigated experimentally for two coarse sands. Two packed samples of 5 cm height were prepared for each sand. Air entrapment was created by repeated infiltration and drainage cycles. The value of K was determined using repetitive falling-head infiltration experiments, which were evaluated using Darcy’s law. The entrapped air content was determined gravimetrically after each infiltration run. The amount and distribution of air bubbles were quantified by micro-computed X-ray tomography (CT) for selected runs. The obtained relationship between entrapped air content and satiated hydraulic conductivity agreed well with Faybishenko’s (1995) formula. CT imaging revealed that entrapped air contents and bubbles sizes were increasing with the height of the sample. It was found that the size of the air bubbles and clusters increased with each experimental cycle. The relationship between initial and residual gas saturation was successfully fitted with a linear model. The combination of X-ray computed tomography and infiltration experiments has a large potential to explore the effects of entrapped air on water flow. Full article