Browsing by Subject "neural network"
Now showing 1 - 4 of 4
Results Per Page
Sort Options
Item Applications of artificial neural networks in the identification of flow units, Happy Spraberry Field, Garza County, Texas(Texas A&M University, 2005-02-17) Gentry, Matthew DavidThe use of neural networks in the field of development geology is in its infancy. In this study, a neural network will be used to identify flow units in Happy Spraberry Field, Garza County, Texas. A flow unit is the mappable portion of the total reservoir within which geological and petrophysical properties that affect the flow of fluids are consistent and predictably different from the properties of other reservoir rock volumes (Ebanks, 1987). Ahr and Hammel (1999) further state a highly "ranked" flow unit (i.e. a good flow unit) would have the highest combined values of porosity and permeability with the least resistance to fluid flow. A flow unit may also include nonreservoir features such as shales and cemented layers where combined porosity-permeability values are lower and resistance to fluid flow much higher (i.e. a poor flow unit) (Ebanks, 1987). Production from Happy Spraberry Field primarily comes from a 100 foot interval of grainstones and packstones, Leonardian in age, at an average depth of 4,900 feet. Happy Spraberry Field is unlike most fields in that the majority of the wells have been cored in the zone of interest. This fact more easily lends the Happy Spraberry Field to a study involving neural networks. A neural network model was developed using a data set of 409 points where X and Y location, depth, gamma ray, deep resistivity, density porosity, neutron porosity, lab porosity, lab permeability and electrofacies were known throughout Happy Spraberry Field. The model contained a training data set of 205 cases, a verification data set of 102 cases and a testing data set of 102 cases. Ultimately two neural network models were created to identify electrofacies and reservoir quality (i.e. flow units). The neural networks were able to outperform linear methods and have a correct classification rate of 0.87 for electrofacies identification and 0.75 for reservoir quality identification.Item Data driven process monitoring based on neural networks and classification trees(Texas A&M University, 2005-11-01) Zhou, YifengProcess monitoring in the chemical and other process industries has been of great practical importance. Early detection of faults is critical in avoiding product quality deterioration, equipment damage, and personal injury. The goal of this dissertation is to develop process monitoring schemes that can be applied to complex process systems. Neural networks have been a popular tool for modeling and pattern classification for monitoring of process systems. However, due to the prohibitive computational cost caused by high dimensionality and frequently changing operating conditions in batch processes, their applications have been difficult. The first part of this work tackles this problem by employing a polynomial-based data preprocessing step that greatly reduces the dimensionality of the neural network process model. The process measurements and manipulated variables go through a polynomial regression step and the polynomial coefficients, which are usually of far lower dimensionality than the original data, are used to build a neural network model to produce residuals for fault classification. Case studies show a significant reduction in neural model construction time and sometimes better classification results as well. The second part of this research investigates classification trees as a promising approach to fault detection and classification. It is found that the underlying principles of classification trees often result in complicated trees even for rather simple problems, and construction time can excessive for high dimensional problems. Fisher Discriminant Analysis (FDA), which features an optimal linear discrimination between different faults and projects original data on to perpendicular scores, is used as a dimensionality reduction tool. Classification trees use the scores to separate observations into different fault classes. A procedure identifies the order of FDA scores that results in a minimum tree cost as the optimal order. Comparisons to other popular multivariate statistical analysis based methods indicate that the new scheme exhibits better performance on a benchmarking problem.Item Learning to segment texture in 2D vs. 3D : A comparative study(Texas A&M University, 2004-11-15) Oh, Se JongTexture boundary detection (or segmentation) is an important capability of the human visual system. Usually, texture segmentation is viewed as a 2D problem, as the definition of the problem itself assumes a 2D substrate. However, an interesting hypothesis emerges when we ask a question regarding the nature of textures: What are textures, and why did the ability to discriminate texture evolve or develop? A possible answer to this question is that textures naturally define physically distinct surfaces or objects, thus, we can hypothesize that 2D texture segmentation may be an outgrowth of the ability to discriminate surfaces in 3D. In this thesis, I investigated the relative difficulty of learning to segment textures in 2D vs. 3D configurations. It turns out that learning is faster and more accurate in 3D, very much in line with what was expected. Furthermore, I have shown that the learned ability to segment texture in 3D transfers well into 2D texture segmentation, but not the other way around, bolstering the initial hypothesis, and providing an alternative approach to the texture segmentation problem.Item Neural networks predict well inflow performance(Texas A&M University, 2004-09-30) Alrumah, Muhammad K.Predicting well inflow performance relationship accurately is very important for production engineers. From these predictions, future plans for handling and improving well performance can be established. One method of predicting well inflow performance is to use artificial neural networks. Vogel's reference curve, which is produced from a series of simulation runs for a reservoir model proposed by Weller, is typically used to predict inflow performance relationship for solution-gas-drive reservoirs. In this study, I reproduced Vogel's work, but instead of producing one curve by conventional regression, I built three neural network models. Two models predict the IPR efficiently with higher overall accuracy than Vogel's reference curve.