Petroleum Engineering Commons^™

Optimization Of Carbon Capture And Sequestration In The Tri-State Hub West Virginia, Ohio, And Pennsylvania, Saif Abdullah Saif Al-Nabhani

Graduate Theses, Dissertations, and Problem Reports (ETD)

Carbon Capture and sequestration (CCS) is a critical way for the decarbonization of power and industrial sectors. The Tri-State area between West Virgina, Ohio and Pennsylvania are a diverse region with various CO₂ sources, deep saline, depleted reservoirs, pipeline network, and the midstream infrastructure that makes it possible for Captured CO₂ to be safely and economically moved and stored over long periods of time. This project addresses the need for an integrated optimization framework that includes CO₂ sources to potential storing sites using a transportation network design and a subsurface model that secures storage and minimizes leakage risk.

The model …

Deep Learning-Based Co-Current Upward Gas-Liquid Two-Phase Flow Regime Identification In An Annular Conduit, Joshua Robert Macomber

Graduate Theses, Dissertations, and Problem Reports (ETD)

Flow regime identification in co-current upward gas-liquid flow through annular conduits remains a significant challenge in petroleum engineering, with major safety and operational implications. It is also important across industries involving the transport of multiphase fluids. Misidentifying flow regimes can introduce major operational risk, yet regime boundaries in annular gas-liquid flow are often visually complex and context dependent.

The objective of this study was to evaluate the utility of convolutional neural network (CNN) classifiers for flow regime identification. The CNN was trained using annular flow image dataset published by Texas A&M University. The dataset consists of approximately 947 RGB images …

Enhancing Pipeline Simulations Through Artificial Intelligence And Machine Learning: A Smart Proxy Modelling Approach, Afeez Shittu

Graduate Theses, Dissertations, and Problem Reports (ETD)

ABSTRACT

Enhancing pipeline simulations is essential for improving operational efficiencies and effectively managing risks in the oil and gas industry. Traditional pipeline simulators, relying heavily on mathematical modeling assumptions, often face limitations due to their high energy and computational demands. This thesis addresses these challenges by introducing an innovative approach that integrates artificial intelligence (AI) and machine learning (ML) through a smart proxy model, offering a more efficient, cost-effective, and flexible alternative to conventional full-physics models used in pipeline simulation software.

The primary aim of this research is to develop and implement a smart proxy model capable of accurately predicting …

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 (ETD)

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 (ETD)

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 …

Machine Learning Assisted Framework For Advanced Subsurface Fracture Mapping And Well Interference Quantification, Mohammad Faiq Adenan

Graduate Theses, Dissertations, and Problem Reports (ETD)

The oil and gas industry has historically spent significant amount of capital to acquire large volumes of analog and digital data often left unused due to lack of digital awareness. It has instead relied on individual expertise and numerical modelling for reservoir development, characterization, and simulation, which is extremely time consuming and expensive and inevitably invites significant human bias and error into the equation. One of the major questions that has significant impact in unconventional reservoir development (e.g., completion design, production, and well spacing optimization), CO2 sequestration in geological formations (e.g., well and reservoir integrity), and engineered geothermal systems (e.g., …

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

Graduate Theses, Dissertations, and Problem Reports (ETD)

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 …

The Effect Of Different Fracturing Fluids On The Productivity Of Multi-Staged Fractured Marcellus Shale Horizontal Wells, Vida Gyaubea Matey-Korley

Graduate Theses, Dissertations, and Problem Reports (ETD)

While hydraulic fracturing has undeniably improved the production from oil and gas reservoirs, this technology is not without limitations. The primary hurdles lie in the areas of proppant transport, fluid rheology, and stress management. Despite the extensive research conducted in this domain, there remains a considerable amount of work to be done for comprehensive solutions that account for the complex interactions among fracturing fluid, proppant distribution, and geomechanical conditions. Achieving this will then make room for a holistic and efficient hydraulic fracturing strategy.

This study addresses the above-mentioned problem by examining the impact of fluid type on proppant transport and …

An Artificial Neural Network Approach To Predicting Formation Stress In Multi-Stage Fractured Marcellus Shale Horizontal Wells Based On Drilling Operations Data, Moudhi Alawadh

Graduate Theses, Dissertations, and Problem Reports (ETD)

The distribution of the anisotropic minimum horizontal stress, both in horizontal and vertical directions, is necessary for effective hydraulic fracture treatment design in Marcellus Shale horizontal wells. Typically, the minimum horizontal stress can be estimated sonic logs. However, sonic log data is not commonly available for the horizontal Marcellus shale wells due to the complexity and cost. The objective of this research is to predict the anisotropic minimum horizontal stress by utilizing drilling parameters including depth, weight-on-bit (WOB), revolution per minute (RPM), standpipe pressure, torque, pump flow rate, and the rate of penetration (ROP). More specifically, artificial neural network (ANN) …

Leveraging Artificial Intelligence And Geomechanical Data For Accurate Shear Stress Prediction In Co2 Sequestration Within Saline Aquifers (Smart Proxy Modeling), Munirah Alawadh

Graduate Theses, Dissertations, and Problem Reports (ETD)

This research builds upon the success of a previous project that used a Smart Proxy Model (SPM) to predict pressure and saturation in Carbon Capture and Storage (CCS) operations into saline aquifers. The Smart Proxy Model is a data-driven machine learning model that can replicate the output of a sophisticated numerical simulation model for each time step in a short amount of time, using Artificial Intelligence (AI) and large volumes of subsurface data. This study aims to develop the Smart Proxy Model further by incorporating geomechanical datadriven techniques to predict shear stress by using a neural network, specifically through supervised …

Predictions Of Produced Water Quality And Recycled Water Optimization For Spatially-Distributed Wells In Point Pleasant Formation, Armel Quentin Mbakop

Graduate Theses, Dissertations, and Problem Reports (ETD)

The treatment of produced water as a fracturing fluid is becoming an increasingly important aspect of water management surrounding the booming of the unconventional oil and gas industry. Two main problems facing the oil and gas industry are the availability of water for well drilling and completion and disposal of the produced water. Unconventional well drilling and completion in the Utica shale requires large amounts of water. The wastewater that results after production—containing high levels of organic and inorganic matter— is usually disposed of through deep well injection. A new approach reuses this produced water as part of subsequent fracturing …

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

Graduate Theses, Dissertations, and Problem Reports (ETD)

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 …

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 (ETD)

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 …

Quantitative Analysis Of Rate Transient Analysis In Unconventional Shale Gas Reserviors, Gabriel Quintero

Graduate Theses, Dissertations, and Problem Reports (ETD)

Rate Transient Analysis is a quick reservoir modeling solution that has been used throughout the oil and gas industry over its continuous development and has provided breakthroughs for modeling conventional plays for decades. As the Marcellus Shale play continues to be a massive producer of Natural Gas in the world, operators look to find economical yet fairly accurate solutions to develop accurate reservoir models of their wells given the complex nature of unconventional reservoirs. Due to extremely low permeability and heterogeneity along with its complex fracture networks, it becomes an extremely difficult problem to model and predict the fluid flow …

A Workflow For Unconventional Reservoirs Optimization Using Supervised Machine Learning In Conjunction With Orthorhombic Elasticity Modeling, Aymen Ab Ali Alhemdi

Graduate Theses, Dissertations, and Problem Reports (ETD)

Due to the anisotropy and heterogeneous nature of unconventional reservoirs like shale, a comprehensive parametric study to optimize hydraulic fracture treatment for such reservoirs is a tough challenge, especially when natural fractures are present. Most of the current frac simulators do not consider the anisotropy of rock elasticity in the shales. Besides, using the fracture simulation linked with reservoir simulation for the parametric study to understand the impact of multiple different design parameters on fracture propagation and production is time expensive and low efficient. The study proposes a workflow including a new orthorhombic (OB) rock algorithm to interpret geomechanical properties …

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

Graduate Theses, Dissertations, and Problem Reports (ETD)

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 …

Well Performance Analysis Of Mseel1 And Mseel2 Wellpads, Mohammad Faiq Adenan

Graduate Theses, Dissertations, and Problem Reports (ETD)

MSEEL1 and MSEEL2 are wellpads that were developed as a means of conducting research into how a gas well being developed in the Marcellus shale can be optimized from the reservoir identification stage to the completions and operational stage. This thesis provides a report of the workflow performed to use the generated data from different sources and analyze them for optimization.

MIP-3H and Boggess 17H were designated as science wells, for which the majority of the well logging and coring experiments were conducted on. The data obtained from the experiments were used as a basis for petrophysical analysis, the results …

Analyzing And Measuring Petrophysical Properties Of Marcellus Shale, Yahia Mustafa Yassin Mustafa

Graduate Theses, Dissertations, and Problem Reports (ETD)

Unconventional oil and natural gas play a key role in our nation's future. The U.S. has vast reserves of such resources that are commercially viable as a result of advances in horizontal drilling and hydraulic fracturing technology. These technologies enable greater access to oil and natural gas in shale formations. Responsible development of America's shale gas resources offers important economic, energy security, and environmental benefits. Hydraulic fracturing and horizontal drilling apply the latest technologies and make it commercially viable to recover shale gas and oil.

In order to estimate the original gas-in-place, predict the production rates, and optimize the hydraulic …

Application Of Fiber Optics Das On Completion Design Optimization, Aliou Sylla

Graduate Theses, Dissertations, and Problem Reports (ETD)

Interstage communication during well stimulation reducing the effectivity of the well completion is known to be a concern in the oil and gas industry. The leading cause of this is fracture communication due to the presence of natural fractures where the formation is being hydraulically fractured. In this study, a technique was developed to map natural fractures, on a larger scale, underground to be able to avoid the high fracture intensity zones when hydraulically fracturing. This study developed a technique to optimize well completion designs by introducing the ability of locating natural fractures zones in the formation (Marcellus Shale) which …

The Impacts Of The Net Stress And Stress Shadow On The Productivity Of Marcellus Shale Horizontal Well, Mohamed El Sgher

Graduate Theses, Dissertations, and Problem Reports (ETD)

Shale gas development has become a crucial part of the global oil and gas industry in recent years, especially in North America. Even though the application of horizontal drilling and hydraulic fracturing techniques have successfully unlocked considerable reserves of natural gas in shale-gas reservoirs, production rates from unconventional reservoirs decline more rapidly than conventional reservoirs. This is because production from a reservoir results in an increase in net stress due to a reduction in the pore pressure, while overburden pressure remains constant. This leads to the reduction in permeability of both the matrix and the fissures, as well as the …

Application Of Decline Curve Analysis To Unconventional Reservoir, Ahmed Wasel Alqattan

Graduate Theses, Dissertations, and Problem Reports (ETD)

The oil and gas industry has been dealing with the unconventional reservoirs for about only two decades. Consequently, many challenges remain in evaluating the unconventional reservoirs. A few of these obstacles are the absence of the adequate production history, understanding the fluid flow regime after hydraulic fracturing, and estimating the drainage volume of the reservoir. One of the essential elements for evaluation of the oil and reservoir is to estimate the production rates over time in order to investigate the economic potential of the project.

Decline Curve Analysis (DCA) is most common the methodology for predicting the production rates over …

Application Of Flowing Material Balance In Unconventional Reservoirs, Salem Alharbi

Graduate Theses, Dissertations, and Problem Reports (ETD)

The application of the horizontal drilling and hydraulic fracturing to unconventional reservoirs, such as Marcellus Shale, has resulted in a significant increase in gas production in the United States. The estimation of the original hydrocarbons in place is an essential element for evaluating the economic success of any reservoir. There are a number of techniques available for estimating the original hydrocarbon in place such as decline curve analysis, volumetric method, and the material balance equation. Flowing Material Balance (FMB) is a more advanced techniques which has become popular in recent years because it does not require static (shut-in) pressure data …

Drilling Performance Monitoring And Optimization: A Data-Driven Approach, Shan E Zehra Lashari, Ali Takbiri-Borujeni, Ebrahim Fathi, Ting Sun, Reza Rahmani, Mehdi Khazaeli

Faculty & Staff Scholarship

Abstract Drilling performance monitoring and optimization are crucial in increasing the overall NPV of an oil and gas project. Even after rigorous planning, drilling phase of any project can be hindered by unanticipated problems, such as bit balling. The objective of this paper is to implement artifcial intelligence technique to develop a smart model for more accurate and robust real-time drilling performance monitoring and optimization. For this purpose, the back propagation, feed forward neural network model was developed to predict rate of penetration (ROP) using diferent input parameters such as weight on bit, rotations per minute, mud fow (GPM) and …

Impact Of Natural Fracture Induced Elastic Anisotropy On Completion And Hydraulic Fracturing Designs, Emre Can Dundar

Graduate Theses, Dissertations, and Problem Reports (ETD)

Shale has been usually recognized as a transverse isotropic (TI) medium in conventional geomechanical log interpretation due to its laminated nature. However, when natural fractures (NFs) exist in the rock body, additional elastic anisotropy can be introduced, converting Shale to an orthorhombic (OB) medium. Previous study illustrates that treating the naturally fractured shale rock as a TI medium by ignoring the NF-induced anisotropy can cause the erroneous estimation of the geomechanical properties (i.e. Young’s modulus, Poisson’s ratio, brittleness index, and etc.) and in-situ stress. In this paper, the study is extended to quantify the impact of NF-induced elastic anisotropy on …

Confirmation Of Data-Driven Reservoir Modeling Using Numerical Reservoir Simulation, Al Hasan Mohamed Al Haifi

Graduate Theses, Dissertations, and Problem Reports (ETD)

Data driven reservoir modeling, also known as Top-Down Model (TDM), is an alternative to the traditional numerical reservoir simulation technique. Data driven reservoir modeling is a new technology that uses artificial intelligence and machine learning to build full-field reservoir models using field measurements (data - facts) instead of mathematical formulations that represent our current understanding of the physics of the fluid flow through porous media. TDM combines all field measurements into a comprehensive reservoir model to predict the production from each well in a field with multiple wells.

There are many opinions, speculations and criticism about not using the physics-based …

Using Artificial Intelligence And Machine Learning To Develop Synthetic Well Logs, Marwan Mohammed Alnuaimi

Graduate Theses, Dissertations, and Problem Reports (ETD)

There has been an increase in the need for energy in the recent past. Oil and gas stand as the source of energy that are widely used. The oil and gas reservoirs are targeted for the purposes of field development. The conventional methods of reservoir characteristics require computing techniques that are unique and complex, some of which are labor and time intensive. Mohaghegh argues that all efforts must be tried and made possible to apply Petroleum Data analytics in production and management of reservoir so as to earn a maximum return (Mohaghegh, Shale Analytics, 2017). Different methodologies have been applied …

Analysis Of The Production Data From Marcellus Shale Wells, Amanda Parrish

Graduate Theses, Dissertations, and Problem Reports (ETD)

The increasingly prevalent practice of unconventional technologies has called for the need for the critical analysis of production decline rates to meet economic targets. The high early time production returns and transient flow make matching a production curve to known methods of decline curve analysis problematic.

This research focuses on finding a technique that best fits a known production curve and then applying that technique to another well to check its accuracy. The goal is to find the earliest time of production data necessary to predict an accurate production curve over the lifespan of the well. This is essential to …