Quantitative characterization of fluid occurrence in shale reservoirs

Shale oil and gas, as important unconventional resources, have been widely discussed in the last decade. The occurrence characteristics of fluids (oil, gas, and water) in shale reservoirs are closely related to the exploitation of shale oil and gas, therefore the quantitative characterization of fluid occurrence in shale reservoirs has received extensive attention. In this paper, the latest advances and potential challenges on this subject are summarized. With respect to shale oil, the amounts, ratios and micro-distributions of shale oil in different states can be determined using the state equation of liquid and adsorption ratio equation, which contributes to identifying high-quality shale oil reservoirs. However, it is still necessary to strengthen the research on the multi-attribute coupling relationship and oil-rock interaction of shale oil reservoirs, and the determination of occurrence characteristics of adsorbed and free oil under in situ reservoir conditions. In terms of shale gas evaluation, the process analysis method and isotope fractionation method effectively solve the problem of evaluating in situ gas-bearing characteristics of shale, and can accurately estimate the amounts of total, adsorbed and free gas. The quantum physisorption behavior of gas could be a new research direction to reveal the microscopic occurrence mechanism of shale gas. As for shale pore water, a complete evaluation procedure for determining the amounts and micro-distributions of adsorbed and free water in shale matrix pores has been established, which provides insight into the storage and flow of oil and gas. In future work, a study on the quantitative evaluation of water-rock interaction is significant for obtaining the adsorbed and free water under in situ reservoir conditions.Document Type: PerspectiveCited as: Li, J., Cai, J. Quantitative characterization of fluid occurrence in shale reservoirs. Advances in Geo-Energy Research, 2023, 9(3): 146-151. https://doi.org/10.46690/ager.2023.09.0

EIGEN: Ecologically-Inspired GENetic Approach for Neural Network Structure Searching from Scratch

Designing the structure of neural networks is considered one of the most challenging tasks in deep learning, especially when there is few prior knowledge about the task domain. In this paper, we propose an Ecologically-Inspired GENetic (EIGEN) approach that uses the concept of succession, extinction, mimicry, and gene duplication to search neural network structure from scratch with poorly initialized simple network and few constraints forced during the evolution, as we assume no prior knowledge about the task domain. Specifically, we first use primary succession to rapidly evolve a population of poorly initialized neural network structures into a more diverse population, followed by a secondary succession stage for fine-grained searching based on the networks from the primary succession. Extinction is applied in both stages to reduce computational cost. Mimicry is employed during the entire evolution process to help the inferior networks imitate the behavior of a superior network and gene duplication is utilized to duplicate the learned blocks of novel structures, both of which help to find better network structures. Experimental results show that our proposed approach can achieve similar or better performance compared to the existing genetic approaches with dramatically reduced computation cost. For example, the network discovered by our approach on CIFAR-100 dataset achieves 78.1% test accuracy under 120 GPU hours, compared to 77.0% test accuracy in more than 65, 536 GPU hours in [35].Comment: CVPR 201

From Kepler to Newton: Explainable AI for Science Discovery

The Observation--Hypothesis--Prediction--Experimentation loop paradigm for scientific research has been practiced by researchers for years towards scientific discoveries. However, with data explosion in both mega-scale and milli-scale scientific research, it has been sometimes very difficult to manually analyze the data and propose new hypotheses to drive the cycle for scientific discovery. In this paper, we discuss the role of Explainable AI in scientific discovery process by demonstrating an Explainable AI-based paradigm for science discovery. The key is to use Explainable AI to help derive data or model interpretations, hypotheses, as well as scientific discoveries or insights. We show how computational and data-intensive methodology -- together with experimental and theoretical methodology -- can be seamlessly integrated for scientific research. To demonstrate the AI-based science discovery process, and to pay our respect to some of the greatest minds in human history, we show how Kepler's laws of planetary motion and Newton's law of universal gravitation can be rediscovered by (Explainable) AI based on Tycho Brahe's astronomical observation data, whose works were leading the scientific revolution in the 16-17th century. This work also highlights the important role of Explainable AI (as compared to Blackbox AI) in science discovery to help humans prevent or better prepare for the possible technological singularity that may happen in the future, since science is not only about the know how, but also the know why.Comment: Presented at ICML-AI4Science 202