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Improving The Simulation Of A Waterflooding Recovery Process Using Artificial Neural Networks, Edison Gil
Improving The Simulation Of A Waterflooding Recovery Process Using Artificial Neural Networks, Edison Gil
Graduate Theses, Dissertations, and Problem Reports
The waterflood performance of the dual five-spot pilot project in the Stringtown oil field, situated in West Virginia, has been studied. A numerical simulator, called BOAST98, was used for the simulation purposes, after developing a reservoir description.;The producing horizon in the field is the Upper Devonian Gordon sandstone, which is characterized by severe heterogeneity due to the depositional environment. Using available core and log data and geological analysis, a reservoir characterization study was done. A preliminary reservoir description based on log porosity-core permeability correlation was improved by developing Artificial Neural Networks (A.N.N.), which incorporates geophysical well log information. These A.N.N.'s …
Restimulation Candidate Selection Using Virtual Intelligence, Khalid Y. Mohamad
Restimulation Candidate Selection Using Virtual Intelligence, Khalid Y. Mohamad
Graduate Theses, Dissertations, and Problem Reports
Due to the importance of well deliverability maintenance, a committee of specialists from Dominion East Ohio and other service companies meets every year to select the wells to be included in the deliverability maintenance plan. The application tool not only help in selecting the wells for deliverability maintenance plan but goes beyond that by designing the most optimum frac recipe.;The purpose of this study is to develop an engineering tool that will help petroleum engineers making a better decision for selecting well candidate and design well restimulation. The project focuses on a gas storage field and use data such as …
Predicting A Detailed Permeability Profile From Minipermeameter Measurements And Well Log Data, Shawn D. Nines
Predicting A Detailed Permeability Profile From Minipermeameter Measurements And Well Log Data, Shawn D. Nines
Graduate Theses, Dissertations, and Problem Reports
Permeability, along with the porosity, comprises one of the two most important properties in petroleum engineering with respect producing hydrocarbon fluids.;It is standard practice in the petroleum industry to determine permeability in one of two ways. These are pressure transient testing and core analysis. Both methods are expensive in their own ways. This research focuses on a way to minimize or limit the need for both of these testing procedures.;The purpose of this research was to utilize well log data, mainly gamma ray and density, Minipermeameter values, and basic information such as depth and spatial coordinates to predict permeability in …
Porosity Distribution Prediction Using Artificial Neural Networks, Fahad Abdullah Al-Qahtani
Porosity Distribution Prediction Using Artificial Neural Networks, Fahad Abdullah Al-Qahtani
Graduate Theses, Dissertations, and Problem Reports
Reservoir characterization plays a very important role in the petroleum industry, especially to the economic success of the reservoir development. Heterogeneity can complicate the evaluation of reservoir properties. Porosity is the primary key to a reliable reservoir model.;Several studies in the literature indicated that accurate evaluation of reservoir properties can be made by the analysis of electric logs. Stringtown oil field in Tyler and Wetzel counties in the northwestern part of West Virginia was selected to conduct this study.;Artificial Neural Networks (ANN) is one of the latest technologies available to the petroleum industry. The objective of this study was to …
Predicting Permeability And Flow Capacity Distribution With Back-Propagation Artificial Neural Networks, Alexis Jose Riera
Predicting Permeability And Flow Capacity Distribution With Back-Propagation Artificial Neural Networks, Alexis Jose Riera
Graduate Theses, Dissertations, and Problem Reports
The prediction of permeability is a critical, key step for reservoir modeling and management of oil recovery operations. Previous studies have successfully demonstrated that the new technology called Artificial Neural Network (ANN), a biologically inspired, massive parallel, distributed information processing system, is an excellent tool for permeability predictions using well log data. This technology overcomes the drawbacks caused by the inherent heterogeneity of the reservoir and lack of sufficient cores or pressure transient tests, allowing to define reservoir characterization within an acceptable accuracy while maintaining costs low. The methodology used in this study takes advantage of this technology to accomplish …
Gas Production Forecasting Using Automatic Type Curve Matching, Walter Jonathan Diazgranados
Gas Production Forecasting Using Automatic Type Curve Matching, Walter Jonathan Diazgranados
Graduate Theses, Dissertations, and Problem Reports
As the demand for natural gas has increased in the last years, also the need for forecast reliable gas recoveries. Gas type curves are one of the methods utilized to estimate future well performance. The purpose of this study is the utilization of the Aminian et al type curves to model gas well performance. Unlike other studies developed in the past, Aminian et al type curves account for important factors ignored in the derivation of the proposed theoretical solutions. Thus, the pressure dependency of gas viscosity and compressibility, as well as the pressure loss owing to non-Darcy flow, make of …
Designing Neural Networks For The Prediction Of The Drilling Parameters For Kuwait Oil And Gas Fields, Abdulrahman F. Al-Rashidi
Designing Neural Networks For The Prediction Of The Drilling Parameters For Kuwait Oil And Gas Fields, Abdulrahman F. Al-Rashidi
Graduate Theses, Dissertations, and Problem Reports
In this study a new methodology was developed to predict the drilling parameters using the Artificial Neural Network. Three models were developed to predict bit type, rate of penetration (ROP), and cost-per-foot (cost/ft), respectively.;The prediction of bit type and other drilling parameters from the current available data is an important criterion in selecting the most cost efficient bit. History of bit runs plays an important factor in bit selection and bit design. Based on field data, the selection of bit type can be accomplished by the use of a neural network as an alternative bit selection method.;Three drilling parameters were …