Digital Commons Network^™

Assessing The Potential Of Uav-Based Multispectral And Thermal Data To Estimate Soil Water Content Using Geophysical Methods, Yunyi Guan, Katherine R. Grote

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Knowledge of the soil water content (SWC) is important for many aspects of agriculture and must be monitored to maximize crop yield, efficiently use limited supplies of irrigation water, and ensure optimal nutrient management with minimal environmental impact. Single-location sensors are often used to monitor SWC, but a limited number of point measurements is insufficient to measure SWC across most fields since SWC is typically very heterogeneous. To overcome this difficulty, several researchers have used data acquired from unmanned aerial vehicles (UAVs) to predict the SWC by using machine learning on a limited number of point measurements acquired across a …

Descriptive Statistical Analysis Of Experimental Data For Wettability Alteration With Smart Water Flooding In Carbonate Reservoirs, Muhammad Ali Buriro, Mingzhen Wei, Baojun Bai, Ya Yao

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Smart water flooding is a promising eco-friendly method for enhancing oil recovery in carbonate reservoirs. The optimal salinity and ionic composition of the injected water play a critical role in the success of this method. This study advances the field by employing machine learning and data analytics to streamline the determination of these critical parameters, which are traditionally reliant on time-intensive laboratory work. The primary objectives are to utilize data analytics to examine how smart water flooding influences wettability modification, identify key parameter ranges that notably alter the contact angle, and formulate guidelines and screening criteria for successful lab design. …

Classification Of Teleseismic Shear Wave Splitting Measurements: A Convolutional Neural Network Approach, Yanwei Zhang, Stephen S. Gao

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Shear wave splitting (SWS) analysis is widely used to provide critical constraints on crustal and mantle structure and dynamic models. In order to obtain reliable splitting measurements, an essential step is to visually verify all the measurements to reject problematic measurements, a task that is increasingly time consuming due to the exponential increase in the amount of data. In this study, we utilized a convolutional neural network (CNN) based method to automatically select reliable SWS measurements. The CNN was trained by human-verified teleseismic SWS measurements and tested using synthetic SWS measurements. Application of the trained CNN to broadband seismic data …

Application Of Artificial Neural Networks In The Drilling Processes: Can Equivalent Circulation Density Be Estimated Prior To Drilling?, Husam Hasan Alkinani, Abo Taleb Al-Hameedi, Shari Dunn-Norman, David Lian

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

As the drilling environment became more challenging nowadays, managing equivalent circulating density (ECD) is a key factor to minimize non-productive time (NPT) due to many drilling obstacles such as stuck pipe, formation fracturing, and lost circulation. The goal of this work was to predict ECD prior to drilling by using artificial neural network (ANN). Once ECD is recognized, the crucial drilling variables impact ECD can be modified to control ECD within the acceptable ranges. Data from over 2000 wells collected worldwide were used in this study to create an ANN to predict ECD prior to drilling. Into training, validation, and …

Mud Loss Estimation Using Machine Learning Approach, Abo Taleb T. Al-Hameedi, Husam H. Alkinani, Shari Dunn-Norman, Ralph E. Flori, Steven Austin Hilgedick, Ahmed S. Amer, Mortadha Alsaba

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Lost circulation costs are a significant expense in drilling oil and gas wells. Drilling anywhere in the Rumaila field, one the world's largest oilfields, requires penetrating the Dammam formation, which is notorious for lost circulation issues and thus a great source of information on lost circulation events. This paper presents a new, more precise model to predict lost circulation volumes, equivalent circulation density (ECD), and rate of penetration (ROP) in the Dammam formation. A larger data set, more systematic statistical approach, and a machine-learning algorithm have produced statistical models that give a better prediction of the lost circulation volumes, ECD, …

Building Shared Knowledge For Eor Technologies: Screening Guideline Constructions, Dashboards, And Advanced Data Analysis, Na Zhang

Doctoral Dissertations

"Successful implementation of enhanced oil recovery (EOR) technology requires comprehensive knowledge and experiences based on existing EOR projects. EOR screening guidelines and EOR reservoir analog are served as such knowledge which are considered as the first step for a reservoir engineer to determine the next step techniques to improve the ultimate oil recovery from their assets. The objective of this research work is to provide better assistance for EOR selection by using fundamental statistics methods and machine learning techniques.

In this dissertation, a total of 977 worldwide EOR projects with the most uniformed, high-quality, and comprehensive information were collected from …