IMPROVING THE SIMULATION OF A WATERFLOODING RECOVERY PROCESS USING ARTIFICIAL NEURAL NETWORKS

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" IMPROVING THE SIMULATION OF A WATERFLOODING RECOVERY PROCESS USING ARTIFICIAL NEURAL NETWORKS "

Document Type	:	Latin Dissertation
Language of Document	:	English
Record Number	:	151119
Doc. No	:	ET22911
Main Entry	:	Edison Gil
Title Proper	:	IMPROVING THE SIMULATION OF A WATERFLOODING RECOVERY PROCESS USING ARTIFICIAL NEURAL NETWORKS
Note	:	This document is digital این مدرک بصورت الکترونیکی می باشد
Abstract	:	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 ischaracterized by severe heterogeneity due to the depositional environment. Usingavailable core and log data and geological analysis, a reservoir characterization study wasdone. A preliminary reservoir description based on log porosity - core permeabilitycorrelation was improved by developing Artificial Neural Networks (A.N.N.), whichincorporates geophysical well log information. These A.N.N.بs were utilized to predictporosity and permeability for five wells in the pilot area.A reservoir model for simulation purposes was constructed after identifying the principalflow units within the formation. Results from the simulation were compared with fiveyears of actual field data. A close history matching for the cumulative oil and waterproduction in the pilot project was..,....-...,..tested for theQ1 PC1 bus cardBoth these projects mere sofixare des elopment efforts tonards contributing to dlfferentaspects of Roboucs and lZ1echatronics projects m the Controls and Roboucs Group..
Subject	:	Electericl tess
	:	برق
electronic file name	:	TL46139.pdf
Title and statement of responsibility and	:	IMPROVING THE SIMULATION OF A WATERFLOODING RECOVERY PROCESS USING ARTIFICIAL NEURAL NETWORKS [Thesis]