Robotized petrophysics: Machine learning and thermal profiling for automated mapping of lithotypes in unconventionals

Meshalkin, Yury; Koroteev, Dmitry; Popov, Evgeny; Чехонин, E.; Popov, Y.

doi:10.1016/j.petrol.2018.03.110

Cited by 15 publications

(7 citation statements)

References 9 publications

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“…The application of machine learning and artificial intelligence involves several stages: data collection, data preparation, choosing the machine learning model, training, and testing of the model and validation [138]. Different researchers have applied artificial intelligence methods to predict geological [139], petrophysical [140], and geomechanical properties [141] and production and enhanced oil recovery methods' efficiency for shale and tight reservoirs [142,143]. A systematic review of the application of artificial intelligence by Syed et al [144] has shown an exponential increase in the publication of artificial intelligence applications to shale and tight reservoirs.…”

Section: Data-driven Modeling Approachesmentioning

confidence: 99%

Tight and Shale Oil Exploration: A Review of the Global Experience and a Case of West Siberia

Dorhjie,

Mukhina,

Kasyanenko

et al. 2023

Energies

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Shale and tight oil reservoirs, with horizontal wells and hydraulic fractures, typically have a recovery ratio of around 10%. The exploration of tight oil and shale in North America has proven economically viable, thanks to advancements, such as horizontal wells, hydraulic fracturing, and other enhanced oil recovery techniques. Taking inspiration from the global experience (the North American shale experience), the exploration and development of the West Siberian tight and shale reserves was more focused on the reported best practices of the exploration of North American shale. In this study, the advance in the specific areas of shale and tight oil exploration was considered, with more emphasis placed on the progress in the exploration of West Siberian shales. According to the review literature, thermal enhanced recovery methods capable of converting organic matter into hydrocarbons were studied more than other methods of enhanced oil recovery. Aligned with global trends, there has been a growing focus on research aiming to integrate data-driven approaches and pore-scale simulations to enhance recovery from tight and shale formations. Multiple pilot studies have showcased promising prospects for implementing multistage hydraulic fracturing. Nevertheless, there are limited pilot studies dedicated to enhanced oil recovery methods for West Siberian shale.

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Section: Data-driven Modeling Approachesmentioning

confidence: 99%

Tight and Shale Oil Exploration: A Review of the Global Experience and a Case of West Siberia

Dorhjie,

Mukhina,

Kasyanenko

et al. 2023

Energies

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“…Artificial intelligence is used in reservoir engineering, production, exploration, and well drilling, and there is a rich literature on this topic. [ 72–101 ] The use of artificial intelligence to calculate permeability and porosity from various data is increasing; for example, calculating permeability and porosity based on the well log or well test data and many other data can be done using artificial intelligence. [ 79,102–131 ]…”

Section: Introductionmentioning

confidence: 99%

Calculating porosity and permeability from synthetic micro‐CT scan images based on a hybrid artificial intelligence

Mohyeddini

Rasaei

2023

Can J Chem Eng

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Nowadays, lattice Boltzmann is one of the standard and exact methods of simulation in micro‐CT images of rock. However, it has a high weakness in run time. Therefore, the effort in this article is to reach a comprehensive substitute method for permeability calculation with less run time than the lattice Boltzmann method. The other purposes are the automation of processing operations, preparation of images, and in the end, the calculation of porosity. The best way to achieve these outcomes is to use hybrid artificial intelligence. In this research work, comprehensive model architecture has been used to design a hybrid artificial intelligence to be able to calculate permeability and porosity in complex images. A thousand images were randomly generated with high complexity, which makes the model comprehensive and extensible, and image processing was applied. After that, the lattice Boltzmann method as the direct simulation was selected. Finally, the convolutional neural network and multilayer perceptron based on a new and comprehensive model were evaluated for the first time; the mean squared error resulting from the evaluation of training data is 0.01, and the test data is 0.03. Expert systems have been used as a subset of artificial intelligence for automated image processing and porosity calculation. In this way, problems related to the direct implementation of classical algorithms for image processing, models, and patterns related to machine learning and needing an expert were solved to an acceptable extent, and an error of less than 5% was achieved.

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“…Numerous algorithms have already been employed in fruit bruise classification. such as K nearest neighbor, artificial neural networks, backpropagation neural networks (BPNN), Partial Least Squares Regression (PLSR), principal component analysis (PCA), Convolution neural network (CNN), support vector machine (SVM) [4], [6], [21][22][23][24][25][26][27][28], Such algorithms attained various advantages with specific cases of classification. However, in most situations, the most successful classification algoritcenteredeep learning is centered on artificial neural networks.…”

Section: Introductionmentioning

confidence: 99%

Damage Detection and Classification of Mangoes Based on Thermal Imaging

2022

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Thermal imaging is an effective tool to measure the surface temperatures of any object. Nowadays, it is widely used to evaluate the quality of different agricultural products. This work uses thermal images to assess mechanical damage in mangoes. During the image acquisition, hot air was supplied to ensure the accuracy of the image. However, the quality of the images was not sufficient to distinguish the subtle and noticeable variations in damage for the next few days. Therefore, each mango image's grey level co-occurrence matrix for consecutive days was examined, and the deviation was plotted. A total of 4505 samples comprised 3226 training samples, and 1279 testing samples obtained from 400 mangoes over ten days were processed with a modified deep learning method. As a result, a 98.3% accuracy rate was achieved.

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Robotized petrophysics: Machine learning and thermal profiling for automated mapping of lithotypes in unconventionals

Cited by 15 publications

References 9 publications

Tight and Shale Oil Exploration: A Review of the Global Experience and a Case of West Siberia

Tight and Shale Oil Exploration: A Review of the Global Experience and a Case of West Siberia

Calculating porosity and permeability from synthetic micro‐CT scan images based on a hybrid artificial intelligence

Damage Detection and Classification of Mangoes Based on Thermal Imaging

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