Prediction of Wells Productive Characteristics with the Use of Unsupervised Machine Learning Algorithms

Balashov, Dmitry; Egorov, Dmitry; Belozerov, Boris; Slivkin, Stanislav

doi:10.2118/196857-ms

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…In the application of artificial intelligence technology, − Balashov et al can also provide corresponding emergency response and rescue measures for artificial intelligence through analysis and learning of accident cases. For example, Statoil in Norway uses artificial intelligence technology for safety monitoring and accident prevention of its offshore oil and gas platforms, effectively reducing the likelihood of accidents occurring.…”

Section: Application Status Of Artificial Intelligence In the Oil And...mentioning

confidence: 99%

Current Status and Prospects of Artificial Intelligence Technology Application in Oil and Gas Field Development

Wang,

Wei,

Xiong

et al. 2024

ACS Omega

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Artificial intelligence technology will be increasingly applied in the oil and gas industry. The rapid development of artificial intelligence technology can solve problems such as high environmental sensitivity and complex production processes in the oil and gas industry. In recent years, emerging technologies represented by artificial intelligence have developed rapidly, assisting petroleum enterprises in digital transformation and intelligent upgrading. This article elaborates on the development trend of artificial intelligence technology. Based on the business scenarios and characteristics of the oil and gas industry, the application status of artificial intelligence technology in domestic and foreign petroleum technology service enterprises was summarized and analyzed. The application scenarios of artificial intelligence technology in the fields of dynamic analysis of oil and gas reservoirs, intelligent historical fitting, numerical simulation proxy models, and production plan optimization were analyzed with emphasis. Based on the problems and challenges faced in the development process of oil and gas reservoirs, it is proposed that petroleum enterprises should attach importance to the "three modernizations" innovation of data standardization, oil and gas field intelligence, and platform collaboration, in order to achieve more refined intelligent analysis and management of oil and gas reservoirs and quickly develop more targeted oil and gas reservoir development plans to assist in the intelligent transformation of oil and gas reservoir development. On this basis, prospects for future artificial intelligence technology are proposed, pointing out that the development of artificial intelligence technology will be faster and faster, and there will be higher demand for artificial intelligence technology in the construction of digital oil and gas fields in China in the future. The research results have important reference value for the development of the oil and gas industry.

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Section: Application Status Of Artificial Intelligence In the Oil And...mentioning

confidence: 99%

Current Status and Prospects of Artificial Intelligence Technology Application in Oil and Gas Field Development

Wang,

Wei,

Xiong

et al. 2024

ACS Omega

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“…Unsupervised machine learning algorithms, such as the K-means and Gaussian mixture model, SOM and many others, have been used for lithofacies identification (Balashov et al 2019;Saputelli et al 2019;Celma et al 2020). As a neural network tool, SOM is used to classify multivariate data samples into petrophysical groups or classes using unsupervised learning.…”

Section: Clustering With a Self-organising Map (Som)mentioning

confidence: 99%

Petrophysical characterisation of the Neoproterozoic and Cambrian successions in the Officer Basin

et al. 2022

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The Neoproterozoic–Paleozoic Officer Basin, located in South Australia and Western Australia, remains a frontier basin for energy exploration, with significant uncertainty due to a paucity of data. As part of Geoscience Australia’s Exploring for the Future (EFTF) program, the objective of this study is to derive the petrophysical properties and to characterise potential reservoirs in the Neoproterozoic–Cambrian sedimentary succession in the Officer Basin through laboratory testing and well log interpretation using both conventional and neural network methods. Laboratory measurements of 41 legacy core samples provide the relationships between gas permeability, Klinkenberg corrected permeability, and nano-scale permeability, as well as grain density, effective and total porosity for various rock types. Conventional log interpretation generates the volume fraction of shale, effective and total porosity from gamma ray and lithology logs. A self-organising map (SOM) was used to cluster the well log data to generate petrophysical group/class index and probability profiles for different classes. Neural network technology was employed to approximate porosity and permeability from logs, conventional interpretation results and class index from SOM modelling. The Neoproterozoic−Cambrian successions have the potential to host both conventional and tight hydrocarbon reservoirs. Neoproterozoic successions are demonstrated to host mainly tight reservoirs with the range in average porosity and geometric mean permeability of 4.77–6.39% and 0.00087–0.01307 mD, respectively, in the different sequences. The range in average porosity and geometric mean permeability of the potential Cambrian conventional reservoirs is 14.54−26.38% and 0.341−103.68 mD, respectively. The Neoproterozoic shales have favourable sealing capacities. This work updates the knowledge of rock properties to further the evaluation of the resource potential of the Officer Basin.

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Optimization Model of Fracture Parameters of Shale Gas Fracturing Horizontal Well Based on Genetic Algorithm

Zhao

Lan

Tang

et al. 2023

Day 2 Mon, February 20, 2023

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Based on a genetic algorithm and field production data, this paper reasonably optimizes the fracture parameters of multistage fractured horizontal wells (MFHWs). First, a mathematical model of MFHWs in shale gas reservoir is proposed under the conditions of variable production and variable pressure. Then, the main factors affecting well production are determined by sensitive analysis. Finally, the production prediction model based on a genetic algorithm (GA) is used to optimize the fracture parameters of the Fuling shale gas reservoir. The results show that in the study area, the number of fractures in typical wells is 10, and the fracture half-length is 90m, and the fracture conductivity is 15.24mD.m. The effective fracture half-length is the main parameter affecting production, so the effective fracture half-length and effective fracture number should be increased reasonably, and the support concentration should be increased properly. What's more, the optimal solution of fracture half-length is 160m, and the optimal solution of fracture number is 8, and the optimal solution of fracture conductivity is 20 mD·m. This paper provides a new idea based a genetic algorithm for optimizing fracture parameters of shale gas wells in the Fuling area.

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Prediction of Wells Productive Characteristics with the Use of Unsupervised Machine Learning Algorithms

Cited by 3 publications

References 5 publications

Current Status and Prospects of Artificial Intelligence Technology Application in Oil and Gas Field Development

Current Status and Prospects of Artificial Intelligence Technology Application in Oil and Gas Field Development

Petrophysical characterisation of the Neoproterozoic and Cambrian successions in the Officer Basin

Optimization Model of Fracture Parameters of Shale Gas Fracturing Horizontal Well Based on Genetic Algorithm

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