Artificial Intelligence and Robotics Commons^™

Development Of Machine Learning Based Approach To Predict Fuel Consumption And Maintenance Cost Of Heavy-Duty Vehicles Using Diesel And Alternative Fuels, Sasanka Katreddi

Graduate Theses, Dissertations, and Problem Reports

One of the major contributors of human-made greenhouse gases (GHG) namely carbon dioxide (CO2), methane (CH4), and nitrous oxide (NOX) in the transportation sector and heavy-duty vehicles (HDV) contributing to about 27% of the overall fraction. In addition to the rapid increase in global temperature, airborne pollutants from diesel vehicles also present a risk to human health. Even a small improvement that could potentially drive energy savings to the century-old mature diesel technology could yield a significant impact on minimizing greenhouse gas emissions. With the increasing focus on reducing emissions and operating costs, there is a need for efficient and …

Application Of Artificial Intelligence For Co2 Storage In Saline Aquifer (Smart Proxy For Snap-Shot In Time), Marwan Mohammed Alnuaimi

Graduate Theses, Dissertations, and Problem Reports

In recent years, artificial intelligence (AI) and machine learning (ML) technology have grown in popularity. Smart Proxy Models (SPM) are AI/ML based data-driven models which have proven to be quite crucial in petroleum engineering domain with abundant data, or operations in which large surface/ subsurface volume of data is generated. Climate change mitigation is one application of such technology to simulate and monitor CO2 injection into underground formations.

The goal of the SPM developed in this study is to replicate the results (in terms of pressure and saturation outputs) of the numerical reservoir simulation model (CMG) for CO2 injection into …

Coupling Numerical Simulation And Pattern Recognition To Model Production And Evaluate Carbon Dioxide Injection In Shale Gas Reservoir, Amirmasoud Kalantari-Dahaghi

Graduate Theses, Dissertations, and Problem Reports

Massive multi-cluster, multi-stage hydraulic fractures have significantly increased the complexity of the flow behavior in shale. This has translated into multiple challenges in the modeling of production from shale wells.

Most commonly used numerical techniques for modeling production from shale wells are Explicit Hydraulic Fracture (EHF) and Stimulated Reservoir Volume (SRV). Model setup for the EHF technique is long and laborious and its implementation is computationally expensive, such that it becomes impractical to model beyond a single pad. On the other hand, identifying the extent and conductivity of SRV is a challenging proposition. SRV technique is commonly used to simplify …