Date of Graduation
Statler College of Engineering and Mineral Resources
Petroleum and Natural Gas Engineering
The hydraulic fracturing technique is one of the major developments in petroleum engineering in the last two decades. Today, nearly all the wells completed in low permeability gas reservoirs require a hydraulic fracturing treatment in order to produce at an economical level. This study presents a new methodology, applicable to tight gas reservoirs, for designing hydraulic fractures.;This study is intended to develop an automatic hydraulic fracture design tool to help users design fracture jobs without being an expert in the art and science of hydraulic fracturing. This process is composed entirely of an integration of several artificial intelligence techniques.;The methodology consists of three modules: formation stress determination, optimum treatment design and net treatment pressure prediction. The first module combines the classic approach of stress calculations with a fuzzy lithology identification system to better characterize the reservoir and estimate the stress profile. The result of this module is essential for the fracture treatment design. The second module incorporates an optimization system composed of neural networks and a genetic algorithm to search for the optimum treatment design. The third, and final, module is designed to predict the net treating pressure expected during fracturing. A one-dimensional vector quantization technique samples and extracts the main characteristic of the pressure profile. The net treatment pressure neural network generates the main features of the pressure profile and then reconstructs the entire signal.;The methodology was integrated in a computer program aimed to help petroleum engineers design optimum treatment schedules and predict net treatment pressure for hydraulic fracturing. This tool is designed to reduce the engineering time for designing optimum treatment schedules.
Popa, Andrei Sergiu, "Automatic hydraulic fracturing design for low permeability reservoirs using artificial intelligence" (2004). Graduate Theses, Dissertations, and Problem Reports. 2638.