Semester
Summer
Date of Graduation
2010
Document Type
Thesis
Degree Type
MS
College
Statler College of Engineering and Mineral Resources
Department
Petroleum and Natural Gas Engineering
Committee Chair
Shahab Mohaghegh.
Abstract
The technique, that is named Top-Down Intelligent Reservoir Modeling, (not to be confused with BP's TDRM history matching technique), integrates traditional reservoir engineering analysis with Artificial Intelligence & Data Mining (AI&DM) technology to generate a full field model. The distinguishing feature of this novel technique is its incredibly low data requirement in order to perform analysis which leads to savings of time and research resources to obtain accurate predictions. It only requires field production rate and some well log data as porosity, thickness, and initial water saturation to start the analysis and provide complete development strategies of the field. Although it can incorporate almost any type and amount of data that is available in the modeling process to increase the accuracy and validity of the developed model.;In this work three different reservoir models with different characteristics and operational conditions have been generated using a commercial simulator and also using the proposed Top Down Modeling Method. The models were built with different PVT-Initial reservoir conditions (saturated or under-saturated), a different number of wells, and different distributions of reservoir characteristics (introducing heterogeneity).;Production rates and well log data, which had been used in the commercial simulator to produce particular models. The same values of data were imported into Top Down Modeling Software (IPDA & IDEA) to develop a new empirical reservoir model in order to validate the capabilities of Top Down Modeling in predicting production issues of an oil reservoir against the commercial simulator.;Investigation and validation of Top Down Modeling's capabilities included identification of the gas cap development within the formation, identification of infill locations by mapping the remaining reserves and prediction of the production performance for the newly drilled wells. Then the results of Top Down Modeling analysis were closer to the commercial simulation models/results.
Recommended Citation
Gomez, Yorgi, "Validation of top-down, intelligent reservoir modeling using numerical reservoir simulation" (2010). Graduate Theses, Dissertations, and Problem Reports. 2148.
https://researchrepository.wvu.edu/etd/2148