An Overview of Reserves and Resources Evaluation in Unconventionals (Russian)

Canbaz, Celal Hakan; Temizel, Cenk; Ozesen, Ahsen; Gayde, Simel; Aksahan, Firat

doi:10.2118/202577-ru

Cited by 8 publications

(1 citation statement)

References 1 publication

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“…*Resultado aproximado obtido por meio da relação: (2.560 * 30) / 1.1962.2.2. Avaliação de Recursos Contínuos pelo U.S. Geological SurveyEm conformidade com os tipos de avaliações descritas porZou et al (2017);Schmoker (2002),Hongjun et al (2016), Satter e Iqbal (2016) eCanbaz et al (2020) apontam a relevância do uso de métodos de analogia, volumétricos e de performance de reservatório para a estimativa de recursos contínuos. Dentre estes, o método desenvolvido pelo USGS, denominado como Assessment for Continuous Petroleum Accumulations, destaca-se pela possibilidade de estimativa de produção futura de gás em folhelho por meio de analogias e da performance atual de seus reservatórios -via dados de poços -, desconsiderando estimativas volumétricas pela nos EUA e mundo aforaHONGJUN et al, 2016).…”

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Avaliação de recursos de gás em folhelho (Shale Gas) na Formação Barreirinha, Bacia do Amazonas, e suas perspectivas para a Amazônia

Cardoso

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A Amazônia merece não só o atencioso desvelo e a solicitude dos governos, mas a aplicação de toda a energia de que dispomos e de toda a técnica que conseguirmos obter, para [...] encontrar uma interpretação adequada, uma compreensão justa, um modo de agir, a fim de que se possa mover, num longo ritmo criador, o mundo que é esta grande região de nosso país". JuscelinoKubitschek (OLIVEIRA, 1956) "Jovens equipes de geólogos e geofísicos brasileiros, trabalhando nas piores condições que se possam imaginar [...], acabaram por reverter os prognósticos negativos de seus antecessores. [...] Pouca gente no país sabe o que foi a campanha e epopeia que conduziram à descoberta do petróleo na região ocidental da Amazônia".Aziz Ab'Saber (AB 'SABER, 1996) "Depois, Deus disse a Noé: [...] Faça uma arca de madeira resinosa. Você fará compartimentos na arca e a revestirá de betume por dentro e por fora".

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Avaliação de recursos de gás em folhelho (Shale Gas) na Formação Barreirinha, Bacia do Amazonas, e suas perspectivas para a Amazônia

Cardoso

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Environmentally-Friendly Production and Recovery Processes for Heavy Oils

Canbaz

Temizel

Palabiyik³

et al. 2021

Day 364 Mon, June 29, 2020

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Oil Industry is going green and there is no solid and comprehensive publication that outlines the use of green energies and methods in oil recovery. Thus, this paper is going to close that gap. As there are more environmental restrictions especially in developed countries, inclusion of green energy methods in petroleum recovery processes is very important for the future of these reserves. We will focus on extra/heavy oil as conventional oil is simpler to produce and doesn't need EOR processes that may come with environmental footprints. The objective of this study is to investigate and outline the ‘green’ production and recovery processes of heavy oil recovery in environmentally-sensitive locations where greenhouse gas emissions, type of energy used to extract oil and gas (e.g., generation of steam using natural gas vs solar), environmental impact of surface facilities, transportation of produced oil and gas and other associated materials/chemica ls required for recovery (e.g. solvents for steam injection process) are critical for the operations as well as economics.

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Geology-Driven EUR Forecasting in Unconventional Fields

Temizel

Canbaz

Alsaheib

et al. 2021

SPE Middle East Oil &Amp; Gas Show and Conference

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EUR (Estimated Ultimate Recovery) forecasting in unconventional fields has been a tough process sourced by its physics involved in the production mechanism of such systems which makes it hard to model or forecast. Machine learning (ML) based EUR prediction becomes very challenging because of the operational issues and the quality of the data in historical production. Geology-driven EUR forecasting, once established, offers EUR forecasting solutions that is not affected by operational issues such as shut-ins. This study illustrates the overall methodology in intelligent fields with real-time data flow and model update that enables optimization of well placement in addition to EUR forecasting for individual wells. A synthetic but realistic model which demonstrates the physics is utilized to generate input data for training the ML model where the spatially-distributed geological parameters including but not limited to porosity, permeability, saturation have been used to describe the production values and ultimately the EUR. The completion is given where the formation characteristics vary in the field that lead to location-dependent production performance leading to well placement optimization based on EUR forecasting from the geological parameters. The algorithm not only predicts the EUR of an individual well and makes decision for the optimum well locations. As the training model includes data of interfering wells, the model is capable of capturing the pattern in the well interference. Even though a synthetic but realistic reservoir model is constructed to generate the data for the aim of assisting the ML model, in practice, it is not an easy task to (1) obtain the input parameters to build a robust reservoir simulation model and (2) understanding and modeling of physics of fluid flow and production in unconventionals is a complex and time-consuming task to build real models. Thus, data-driven approaches like this help to speed up reservoir management and development decisions with reasonable approximations compared to numerical models and solutions. Application of machine learning in intelligent fields is also explained where the models are dynamically-updated and trained with the new data. Geology-driven EUR forecasting has been applied and relatively-new in the industry. In. this study, we are extending it to optimize well placement in intelligent fields in unconventionals beyond other existing studies in the literature.

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An Overview of Reserves and Resources Evaluation in Unconventionals (Russian)

Cited by 8 publications

References 1 publication

Avaliação de recursos de gás em folhelho (Shale Gas) na Formação Barreirinha, Bacia do Amazonas, e suas perspectivas para a Amazônia

Avaliação de recursos de gás em folhelho (Shale Gas) na Formação Barreirinha, Bacia do Amazonas, e suas perspectivas para a Amazônia

Environmentally-Friendly Production and Recovery Processes for Heavy Oils

Geology-Driven EUR Forecasting in Unconventional Fields

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