253 research outputs found
Manufactured caverns in carbonate rock
Disclosed is a process for manufacturing underground caverns suitable in one embodiment for storage of large volumes of gaseous or liquid materials. The method is an acid dissolution process that can be utilized to form caverns in carbonate rock formations. The caverns can be used to store large quantities of materials near transportation facilities or destination markets. The caverns can be used for storage of materials including fossil fuels, such as natural gas, refined products formed from fossil fuels, or waste materials, such as hazardous waste materials. The caverns can also be utilized for applications involving human access such as recreation or research. The method can also be utilized to form calcium chloride as a by-product of the cavern formation process
Small drill-hole, gas mini-permeameter probe
The distal end of a basic tube element including a stopper device with an expandable plug is positioned in a pre-drilled hole in a rock face. Rotating a force control wheel threaded on the tube element exerts force on a sleeve that in turn causes the plug component of the stopper means to expand and seal the distal end of the tube in the hole. Gas under known pressure is introduced through the tube element. A thin capillary tube positioned in the tube element connects the distal end of the tube element to means to detect and display pressure changes and data that allow the permeability of the rock to be determined
Interpretable and Steerable Sequence Learning via Prototypes
One of the major challenges in machine learning nowadays is to provide
predictions with not only high accuracy but also user-friendly explanations.
Although in recent years we have witnessed increasingly popular use of deep
neural networks for sequence modeling, it is still challenging to explain the
rationales behind the model outputs, which is essential for building trust and
supporting the domain experts to validate, critique and refine the model. We
propose ProSeNet, an interpretable and steerable deep sequence model with
natural explanations derived from case-based reasoning. The prediction is
obtained by comparing the inputs to a few prototypes, which are exemplar cases
in the problem domain. For better interpretability, we define several criteria
for constructing the prototypes, including simplicity, diversity, and sparsity
and propose the learning objective and the optimization procedure. ProSeNet
also provides a user-friendly approach to model steering: domain experts
without any knowledge on the underlying model or parameters can easily
incorporate their intuition and experience by manually refining the prototypes.
We conduct experiments on a wide range of real-world applications, including
predictive diagnostics for automobiles, ECG, and protein sequence
classification and sentiment analysis on texts. The result shows that ProSeNet
can achieve accuracy on par with state-of-the-art deep learning models. We also
evaluate the interpretability of the results with concrete case studies.
Finally, through user study on Amazon Mechanical Turk (MTurk), we demonstrate
that the model selects high-quality prototypes which align well with human
knowledge and can be interactively refined for better interpretability without
loss of performance.Comment: Accepted as a full paper at KDD 2019 on May 8, 201
Recommended from our members
Fracture Dissolution of Carbonate Rock: An Innovative Process for Gas Storage
The goal of the project is to develop and assess the feasibility and economic viability of an innovative concept that may lead to commercialization of new gas-storage capacity near major markets. The investigation involves a new approach to developing underground gas storage in carbonate rock, which is present near major markets in many areas of the United States. Because of the lack of conventional gas storage and the projected growth in demand for storage capacity, many of these areas are likely to experience shortfalls in gas deliverability. Since depleted gas reservoirs and salt formations are nearly non-existent in many areas, alternatives to conventional methods of gas storage are required. The need for improved methods of gas storage, particularly for ways to meet peak demand, is increasing. Gas-market conditions are driving the need for higher deliverability and more flexibility in injection/withdrawal cycling. In order to meet these needs, the project involves an innovative approach to developing underground storage capacity by creating caverns in carbonate rock formations by acid dissolution. The basic concept of the acid-dissolution method is to drill to depth, fracture the carbonate rock layer as needed, and then create a cavern using an aqueous acid to dissolve the carbonate rock. Assessing feasibility of the acid-dissolution method included a regional geologic investigation. Data were compiled and analyzed from carbonate formations in six states: Indiana, Ohio, Kentucky, West Virginia, Pennsylvania, and New York. To analyze the requirements for creating storage volume, the following aspects of the dissolution process were examined: weight and volume of rock to be dissolved; gas storage pressure, temperature, and volume at depth; rock solubility; and acid costs. Hydrochloric acid was determined to be the best acid to use because of low cost, high acid solubility, fast reaction rates with carbonate rock, and highly soluble products (calcium chloride) that allow for the easy removal of calcium waste from the well. Physical and chemical analysis of core samples taken from prospective geologic formations for the acid dissolution process confirmed that many of the limestone samples readily dissolved in concentrated hydrochloric acid. Further, some samples contained oily residues that may help to seal the walls of the final cavern structure. These results suggest that there exist carbonate rock formations well suited for the dissolution technology and that the presence of inert impurities had no noticeable effect on the dissolution rate for the carbonate rock. A sensitivity analysis was performed for characteristics of hydraulic fractures induced in carbonate formations to enhance the dissolution process. Multiple fracture simulations were conducted using modeling software that has a fully 3-D fracture geometry package. The simulations, which predict the distribution of fracture geometry and fracture conductivity, show that the stress difference between adjacent beds is the physical property of the formations that has the greatest influence on fracture characteristics by restricting vertical growth. The results indicate that by modifying the fracturing fluid, proppant type, or pumping rate, a fracture can be created with characteristics within a predictable range, which contributes to predicting the geometry of storage caverns created by acid dissolution of carbonate formations. A series of three-dimensional simulations of cavern formation were used to investigate three different configurations of the acid-dissolution process: (a) injection into an open borehole with production from that same borehole and no fracture; (b) injection into an open borehole with production from that same borehole, with an open fracture; and (c) injection into an open borehole connected by a fracture to an adjacent borehole from which the fluids are produced. The two-well configuration maximizes the overall mass transfer from the rock to the fluid, but it results in a complex cavern shape. Numerical simulations were performed to evaluate the ability of storage caverns produced by the acid-dissolution method to store natural gas. In addition, analyses were conducted to evaluate cavern stability during gas injection and withdrawal from storage caverns created in carbonate formations by the acid-dissolution method. The stability analyses were conducted using FLAC2D, a commercially available geotechnical analysis and design software. The analyses indicate that a tall cylindrical cavern with a domed roof and floor will be stable under the expected range of in situ and operational conditions. This result suggests that it should be feasible to avoid mechanical instabilities that could potentially diminish the effectiveness of the storage facility. The feasibility of using pressure transients measured at the ground surface was investigated as a means to evaluate (Abstract truncated
Definitions, methods, and applications in interpretable machine learning.
Machine-learning models have demonstrated great success in learning complex patterns that enable them to make predictions about unobserved data. In addition to using models for prediction, the ability to interpret what a model has learned is receiving an increasing amount of attention. However, this increased focus has led to considerable confusion about the notion of interpretability. In particular, it is unclear how the wide array of proposed interpretation methods are related and what common concepts can be used to evaluate them. We aim to address these concerns by defining interpretability in the context of machine learning and introducing the predictive, descriptive, relevant (PDR) framework for discussing interpretations. The PDR framework provides 3 overarching desiderata for evaluation: predictive accuracy, descriptive accuracy, and relevancy, with relevancy judged relative to a human audience. Moreover, to help manage the deluge of interpretation methods, we introduce a categorization of existing techniques into model-based and post hoc categories, with subgroups including sparsity, modularity, and simulatability. To demonstrate how practitioners can use the PDR framework to evaluate and understand interpretations, we provide numerous real-world examples. These examples highlight the often underappreciated role played by human audiences in discussions of interpretability. Finally, based on our framework, we discuss limitations of existing methods and directions for future work. We hope that this work will provide a common vocabulary that will make it easier for both practitioners and researchers to discuss and choose from the full range of interpretation methods
- …