Engineering Commons^™

Enhancing Reservoir Modeling And Simulation Through Artificial Intelligence And Machine Learning: A Smart Proxy Modeling Approach, Andrew Timothy Jenkins

Graduate Theses, Dissertations, and Problem Reports

The application of numerical reservoir simulation (NRS) has been a common approach within the oil and gas industry for decades, providing a means to model and forecast dynamic subsurface interactions, as a basis for reservoir management and development decisions. These techniques have expanded to application within carbon capture utilization and storage (CCUS) projects as domestic and global policy shift towards reducing carbon emissions while maintaining the energy needs of our modern society. NRS techniques have become a core process for permitting approval in Class VI (large-scale geological sequestration) wells due to the fundamental similarity of these types of subsurface processes. …

Engineering Applications Of Artificial Intelligence To Forecast Production Of Shale Wells, Yasir Jassim Alkalby

Graduate Theses, Dissertations, and Problem Reports

This study examines the application of artificial intelligence (AI) and supervised machine learning techniques to forecast production from unconventional shale wells, utilizing actual field measurement data over a period of two years. Traditional methods, such as decline curve analysis, offer valuable insights but often fail to fully capture the complex nuances affecting productivity and tend to rely excessively on empirical equations.

In this research, the AI-based Shale Analytics approach, introduced by Mohaghegh in 2017, is employed. This method leverages Big Data Analytics to identify unique patterns from actual field observations, enhancing the evaluation and quantification of various productivity factors, facilitating …

Evaluation Of Liquid Loading In Gas Wells Using Machine Learning, Abderraouf Chemmakh, Olusegun Stanley Tomomewo, Kegang Ling, Ahmed Shammari

Petroleum Engineering Student Publications

The inevitable result that gas wells witness during their life production is the liquid loading problem. The liquids that come with gas block the production tubing if the gas velocity supplied by the reservoir pressure is not enough to carry them to surface. Researchers used different theories to solve the problem naming, droplet fallback theory, liquid film reversal theory, characteristic velocity, transient simulations, and others. While there is no definitive answer on what theory is the most valid or the one that performs the best in all cases. This paper comes to involve a different approach, a combination between physics-based …

Comparative Analysis Of Artificial Intelligence And Numerical Reservoir Simulation In Marcellus Shale Wells, Arya Maher Sattari

Graduate Theses, Dissertations, and Problem Reports

This dissertation addresses the limitations of conventional numerical reservoir simulation techniques in the context of unconventional shale plays and proposes the use of data-driven artificial intelligence (AI) models as a promising alternative. Traditional methods, while providing valuable insights, often rely on simplifying assumptions and are constrained by time, resources, and data quality. The research leverages AI models to handle the complexities of shale behavior more effectively, facilitating accurate predictions and optimizations with less resource expenditure.

Two specific methodologies are investigated for this purpose: traditional numerical reservoir simulations using Computer Modelling Group's GEM reservoir simulation software, and an AI-based Shale Analytics …

Application Of Distributed Fiber-Optic Sensing For Pressure Predictions And Multiphase Flow Characterization, Gerald Kelechi Ekechukwu

LSU Doctoral Dissertations

In the oil and gas industry, distributed fiber optics sensing (DFOS) has the potential to revolutionize well and reservoir surveillance applications. Using fiber optic sensors is becoming increasingly common because of its chemically passive and non-magnetic interference properties, the possibility of flexible installations that could be behind the casing, on the tubing, or run on wireline, as well as the potential for densely distributed measurements along the entire length of the fiber. The main objectives of my research are to develop and demonstrate novel signal processing and machine learning computational techniques and workflows on DFOS data for a variety of …

Machine Learning Based Real-Time Quantification Of Production From Individual Clusters In Shale Wells, Ayodeji Luke Aboaba

Graduate Theses, Dissertations, and Problem Reports

Over the last two decades, there has been advances in downhole monitoring in oil and gas wells with the use of Fiber-Optic sensing technology such as the Distributed Temperature Sensing (DTS). Unlike a conventional production log that provides only snapshots of the well performance, DTS provides continuous temperature measurements along the entire wellbore.

Whether by fluid extraction or injection, oil and gas production changes reservoir conditions, and continuous monitoring of downhole conditions is highly desirable. This research study presents a tool for real-time quantification of production from individual perforation clusters in a multi-stage shale well using Artificial Intelligence and Machine …

Hydrocarbon Pay Zone Prediction Using Ai Neural Network Modeling., Darren D. Guedon

Graduate Theses, Dissertations, and Problem Reports

This paper captures the ability of AI neural network technology to analyze petrophysical datasets for pattern recognition and accurate prediction of the pay zone of a vertical well from the Santa Fe field in Kansas.

During this project, data from 10 completed wells in the Santa Fe field were gathered, resulting in a dataset with 25,580 records, ten predictors (logs data), and a single binary output (Yes or No) to identify the availability of Hydrocarbon over a half feet depth segment in the well. Several models composed of different predictors combinations were also tested to determine how impactful some logs …

Thermodynamic Vapor-Liquid Equilibrium In Naphtha-Water Mixtures, Sandra Milena Lopez-Zamora

Electronic Thesis and Dissertation Repository

Naphtha is used to dilute the froth from bitumen treatment. Naphtha is recovered using a Naphtha Recovery Unit (NRU) and sent back to the froth dilution step. To minimize the environmental and economic impact of the NRU, it is imperative to maximize the naphtha recovery. It is, in this respect, that enhanced NRU Vapour-Liquid-Liquid equilibrium data is a significant value. The prediction of phase equilibria for hydrocarbon/water blends in separators, is a subject of considerable importance for chemical processes. Despite its relevance, there are still pending questions. Among them, is the prediction of the correct number of phases. While a …

Well Oiled Machine: Classifying Machinery Performance Reductions Using Work Order Data, Jacob Brionez, Amber Burnett, Cho Kim, Scott M. Whitney, Thomas N. Anderson, Sumeet Treehan

SMU Data Science Review

Work Order (WO) data from System Applications and Products in Data Processing (SAP) software contains valuable information about what WOs intend to accomplish. Using SAP work order data, with time-series machinery sensor data combined into the same dataset, provides an opportunity to optimize prediction models to increase performance. Ideally, WO data can be utilized to help predict machinery's anticipated performance and can help prioritize a WO among others based on the anticipated machinery performance. It is possible to identify anomalies in pump sensor data using the Isolation Forest algorithm as the method for anomaly detection. The relationship between the sensor …