Browsing by Author "Rahmanifard, Hamid"

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    Production Forecasting in Unconventional Reservoirs: A Workflow for Data-Driven Analysis
    (2024-03-13) Rahmanifard, Hamid; Gates, Ian Donald; Aguilera, Roberto; Gates, Ian Donald; Kantzas, Apostolos; Razavi, Saman; Chen, Nancy
    The oil and gas industry has experienced a significant transformation in recent years, with the use of advanced data analytics and machine learning (ML) techniques. These innovations have opened new opportunities in reservoir engineering, allowing engineers to make data-driven decisions and optimize well completion parameters. The accuracy of forecasts in predicting hydrocarbon production from unconventional reservoirs has become very important, as it directly affects energy security, economic growth, and sustainable resource management. This thesis presents a novel and comprehensive automated multistep workflow that includes data collection, data preparation, feature selection, hyperparameter tuning, ML algorithm selection, and well completion parameter optimization. In the workflow, we also propose novel methods for outlier detection, feature selection, and a modified optimization algorithm. To verify the accuracy of the workflow, we applied it to various synthetic and field databases and considered different objective functions, such as oil and gas cumulative production and monthly/daily production rates. The proposed integrated workflow enhanced the accuracy and reliability of production forecasting, as well as the assessment and improvement of well performance in unconventional formations. It also helped us determine how geology, reservoir characteristics, and completion designs influence production, and how to adjust them to achieve better outcomes. Moreover, we used the workflow to find the best completion design and estimate the production loss due to sub-optimal completion practices for the wells in the unconventional plays. These results of the research offer valuable insights for the stakeholders in the energy sector who operate in unconventional resources to make informed decisions about well completions, field development, resource extraction, and operational costs.
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    Techno-Economic Assessment of CAES Technologies in Enhanced Geothermal Systems (EGS) in Alberta
    (2018-07-25) Rahmanifard, Hamid; Plaksina, Tatyana; Gates, Ian Donald; Shor, Roman J.
    Global warming, depletion of cheap fossil resources, and adherence to more stringent carbon emission policies dramatically increase the demand for low carbon and sustainable renewable energy sources. Wind and geothermal energy are two energy sources alternative to hydrocarbons that have gained wide interest for electricity generation in Alberta. However, the relatively high cost of geothermal energy and intermittent nature of wind power hinder their widespread use in Alberta. Research shows that the use of existing wells (inactive wells) and the conversion of them into Compressed Air Energy Systems (CAES) is a solution to the abovementioned problems. Therefore, in this work, we perform the techno-economic modelling of CAES and geothermal systems coupled with wind energy using new wells and existing wells scenarios for stable generation of electricity in Alberta. To evaluate the potential of CAES in Alberta, we simulate the non-isothermal flow of the injected fluid (water or air) in a typical hot dry rock (HDR) reservoir (with different thermal conductivities) using the simulator CMG STARS. Then, to compare the performance (electricity generation) of the power plants with different working fluids (air and water), we develop and employ an integrated VBA software to model three different types of CAES power plants (Huntorf power plant, Germany, McIntosh power plant, USA, and adiabatic-CAES power plant) with and without wind energy and simulate the conventional geothermal power plants (enhanced geothermal systems and hydrothermal reservoirs with binary and flash power plants). Furthermore, we incorporate an economic assessment by adding another module to the software for estimating the levelized cost of energy (LCOE) with three different approaches: fixed cost rate, EREE method, and discounted cash flow analysis. The results show that for both scenarios (with drilling new wells and with existing wells) wind/CAES-geothermal scenario with McIntosh technology has the lowest LCOE (10.5 ¢/kWh and 7.5 ¢/kWh, respectively) with the CO2 intensity of 0.09 ton-CO2/MWh. Additionally, running the discounted cash flow analysis for all scenarios with different technologies shows that only wind/CAES-geothermal scenario with McIntosh technology achieves the internal rate of return (IRR) 10% value (industrial benchmark) with the 2017 end-user electricity prices for residential and industrial sectors. Finally, building a power plant with new wells using the best scenario results in total GDP impacts of $1,239 and $1,386 million and creation of 5,540 and 6,842 jobs over a 10-year period in Alberta and Canada, respectively.