Development of the Approach for Automatic Well Logging Interpretation for Big Number of Wells with the Use of Machine Learning

Validov, Marat F.; Ismagilov, A.R.; Voloskov, Dmitry; Magdeev, M.S.; Nazarov, A.A.

doi:10.3997/2214-4609.201702257

Cited by 4 publications

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Application of neural network technology to calculate well logging porosity on the example of UK2-7 formations in the Yelizarovsky deflection (Western Siberia)

Lisovskaya

Platov

2021

E3S Web Conf.

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The article describes the use of an artificial neural network to calculate porosity in the West Siberian oil and gas province for the UK2–7 strata. The estimated porosity was compared with core porosity data. Correlation coefficient between core samples porosity and well logging porosity (using the neural network) showed higher values in comparison with traditional methods of porosity estimation.

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Application of neural network technology to calculate well logging porosity on the example of UK2-7 formations in the Yelizarovsky deflection (Western Siberia)

Lisovskaya

Platov

2021

E3S Web Conf.

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Study of vertical electrical resistance changes in reservoir beds of Tournaisian oil deposits

Sidorov,

Rizvanova,

Leontyev

et al. 2023

Georesursy

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The article proposes a method for rapid assessment of the initial position of oil-water contacts, oxidized oil zones, transition zones, zones of high and decreasing resistance in the Lower Carboniferous Tournaisian oil deposits at one of the oilfields of the Republic of Tatarstan. As initial data, the values of electrical resistivity of effective interlayers from the catalog of geological and geophysical data, determined from induction logging curves, were used. Well log data for more than 200 wells drilled in elevation areas were used in analysis. The change in the electrical properties of deposits vertically is considered using the average resistivity values of effective interlayers averaged over all wells. Conclusions are based on the results of the done work: according to the resistivity values in massive carbonate deposits of the Tournaisian age, it is possible to distinguish various zones of oil saturation along the vertical; initial oil saturation does not grow exponentially, as in the Leverett function, but linearly in each zone.

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The Use of Neural Network Technologies in Prediction the Reservoir Properties of Unconsolidated Reservoir Rocks of Shallow Bitumen Deposits

Validov

Нургалиев

Sudakov

et al. 2021

Day 1 Tue, October 05, 2021

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In the conditions of the dynamically changing conjuncture of the oil and gas market, there is an urgent need to reduce the cost of oil production and increase the efficiency of development, this is especially important for the local ultra-viscous oil. In this regard, it is necessary to optimize costs at all stages, starting from the geological exploration and even at the stage of completion of the development process. For ultra-viscous oil deposits, this is especially relevant at the stage of assessing the resource potential of a separate uplift of any of the fields, when the only reliable way to perform a high-frequency section at shallow depths is to drill appraisal wells with full core sampling. An additional load is exerted by the period between core extraction and obtaining information about the flow properties of each of the samples. By themselves, standard core studies are complicated by the fact that sand rocks of weakly cemented bitumoids can often be destroyed during experiments. In this regard, the use of new approaches, including digital ones, which allow us to make quick decisions on a part of the geological section in the area of the appraisal well and on the uplift as a whole, are highly in demand. This article describes the methods that allow the determining of flow properties for uncemented (loose sands) rocks in Permian sediments. More than 25,000 core samples were studied from 805 wells at several fields of the Republic of Tatarstan. The technology used allows us to calculate a continuous curve of volumetric bitumen saturation in the conditions of complete or partial absence of core at the well. This paper presents the results of creating an algorithm for automatic prediction of weight bitumen saturation in a sand pack of the Sheshminsky horizon of the Permian system using neural network technologies, as well as using an alternative calculation method.

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Development of the Approach for Automatic Well Logging Interpretation for Big Number of Wells with the Use of Machine Learning

Cited by 4 publications

References 0 publications

Application of neural network technology to calculate well logging porosity on the example of UK2-7 formations in the Yelizarovsky deflection (Western Siberia)

Application of neural network technology to calculate well logging porosity on the example of UK2-7 formations in the Yelizarovsky deflection (Western Siberia)

Study of vertical electrical resistance changes in reservoir beds of Tournaisian oil deposits

The Use of Neural Network Technologies in Prediction the Reservoir Properties of Unconsolidated Reservoir Rocks of Shallow Bitumen Deposits

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