|dc.description.abstract||The 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.||en