Geological Engineering Commons^™

Detecting Electrical Submersible Pump (Esp) Failures And Estimating Run Life Using Artificial Neural Networks, Mostafa Ahmed Sobhy

Theses and Dissertations

Electric Submersible Pumps (ESPs) are one of the important artificial lift methods for sustaining production in mature and high-water-cut wells; but may suffer frequent failures due to mechanical, electrical, hydraulic, chemical, and operational failures. These failures can yield substantial deferred production and intervention costs. Plenty of ESP installations are fitted with downhole sensors. Yet, it is observed that the current industry practice underutilizes the wealth of available sensor and operational data and lacks standardized, explainable failure-type identification and classification.

In this thesis, a comprehensive Machine Learning (ML) and Deep Learning (DL) framework was introduced for ESPs that simultaneously estimates remaining …

Development Of Uas And Geophysical Techniques For Agriculture And Environmental Site Characterization, Yunyi Guan

Doctoral Dissertations

"Soil properties are critical for agricultural management. Geophysical techniques such as ground penetrating radar (GPR) and electromagnetic induction have been used to measure soil properties for agricultural applications. However, their measurements are only acquired along select traverses within a field, and the data processing can be time-consuming, the instruments can also be expensive. Unmanned aerial vehicles (UAVs) are a recent advancement used for precision agriculture. Compared with geophysical data, UAVs have higher resolution and wider coverage and can quickly collect data across the whole field. The operation is also relatively simpler with a more economical acquisition cost. This research investigates …

Remote Sensing Approach For Terramechanics Applications Utilizing Machine And Deep Learning, Jordan J. Ewing

Dissertations, Master's Theses and Master's Reports

Terrain traversability is critical for developing Go/No Go maps, significantly impacting a mission's success. To predict the mobility of a vehicle over a terrain, one must understand the soil characteristics. In situ measurements performed by soldiers in the field are the current method of collecting this information, which is time-consuming, are only point measurements, and can put soldiers in harm's way. Therefore, this study investigates using remote sensing as an alternative approach to characterize terrain properties.

This approach will explore the relationships between electromagnetic radiation and soil types with varying properties. Optical, thermal, and hyperspectral sensors will be used to …

Tropospheric Correction For Insar Using Machine Learning, Ngo Hi Kenny Yue

Masters Theses

"Interferometric Synthetic Aperture Radar (InSAR) is a popular technique for studying Earth's surface deformation caused by phenomena like earthquakes and subsidence. However, its accuracy is limited by tropospheric delays caused by water vapor in the atmosphere. This limitation can be overcome by using methods that correct for tropospheric noise, such as statistical, empirical, and predictive approaches. This study explores the potential of using machine learning algorithms to predict the zenith total delay caused by tropospheric effects in InSAR measurements. The study employs two different machine learning algorithms, random forest and neural networks, to learn the relationship between numerical weather prediction …

Bibliometric Survey On Flood Prediction Using Machine Learning, Seema Patil Prof., Daksh Khurana Mr., Kartik Rao Mr, Priyanshu Meena Mr, Shivendra Singh Mr

Library Philosophy and Practice (e-journal)

Floods are one of the most devastating natural hazards, and modelling them is extremely difficult. Flood prediction model advancement study led to factors such as loss of human and animal life, property damage, and risk mitigation. The focus of this bibliometric survey is to recognise the few studies which have upheld on the factors affecting the floods. The analysis is done based on 254 documents such as articles, conference papers, article reviews and some reviews and notes. India contributes to the maximum number of documents followed by China and the United States of America. This bibliometric survey is conducted using …

Combining Machine Learning And Empirical Engineering Methods Towards Improving Oil Production Forecasting, Andrew J. Allen

Master's Theses

Current methods of production forecasting such as decline curve analysis (DCA) or numerical simulation require years of historical production data, and their accuracy is limited by the choice of model parameters. Unconventional resources have proven challenging to apply traditional methods of production forecasting because they lack long production histories and have extremely variable model parameters. This research proposes a data-driven alternative to reservoir simulation and production forecasting techniques. We create a proxy-well model for predicting cumulative oil production by selecting statistically significant well completion parameters and reservoir information as independent predictor variables in regression-based models. Then, principal component analysis (PCA) …

Developing Innovative Spectral And Machine Learning Methods For Mineral And Lithological Classification Using Multi-Sensor Datasets, Chandan Kumar

Dissertations, Master's Theses and Master's Reports

The sustainable exploration of mineral resources plays a significant role in the economic development of any nation. The lithological maps and surface mineral distribution can be vital baseline data to narrow down the geochemical and geophysical analysis potential areas. This study developed innovative spectral and Machine Learning (ML) methods for mineral and lithological classification. Multi-sensor datasets such as Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Advanced Land Observing (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR), Sentinel-1, and Digital Elevation Model (DEM) were utilized. The study mapped the hydrothermal alteration minerals derived …