Automated intelligent assistant in the selection of well placement when developing hard-to-recover reserves

Sergeychev, A. V.; Toropov, Konstantin; Antonov, M.S.; Fedorov, A. Yu.; Povalyaev, Andrey Aleksandrovich; Dilmuhametov, Ilshat Rashitovich; Nadezdin, O.; Llc, Rf RN-BashNIPIneft

doi:10.24887/0028-2448-2020-10-76-81

Cited by 2 publications

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“…Reasonable production allocation is critical for the policy and project design of gas reservoir development (Li et al, 2006) and plays an important role in maintaining stable production of gas reservoirs and extending the product life cycle of gas wells. It is important to increase gas reservoir recovery (Selemenev et al, 2008;Li et al, 2021) and achieve efficient gas reservoir development (Khan et al, 2014;Sergeychev et al, 2020), which has been a focus of recent research (Li et al, 2018;Li et al, 2020). For conventional gas reservoirs, the frequently used methods for reasonable production allocation of gas wells include empirical method, nodal systems analysis, gas recovery curve method, profit analysis of production, reasonable drawdown pressure, and critical liquid-carrying flow rate (Liu et al, 1999;Li et al, 2008;Liu et al, 2013;Chen et al, 2016;Liu et al, 2022).…”

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confidence: 99%

Reasonable production allocation model of gas wells for deep tight gas reservoirs with the edge water

et al. 2023

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Deep tight gas reservoirs are one of the important unconventional gas reservoirs. Deep burial, tight reservoirs have many characteristics, including diverse accumulation patterns, multiple accumulation regulations, low natural energy generation, complex gas–water relationship, and intricate seepage mechanism. These features of gas reservoirs put forward the requirement for new methods for a reasonable production allocation of horizontal wells and optimization of such allocations from the perspective of stress sensitivity. While CO2 huff-and-puff-based models, numerical simulation models, and thermos-hydrodynamic models have been built to solve these issues, there is still a lack of theoretical guidance for reasonable production allocation, especially with the edge-water problem. Here, we present a new one-dimensional mathematical and physical model to capture the stable movement of the gas–water interface in deep tight edge-water gas reservoirs. Our results show that there is a starting pressure in deep tight gas reservoirs. The starting pressure gradient increases with the growth of water saturation, which is far greater than the starting pressure gradient of medium, shallow gas reservoirs under the same water saturation. In addition, by considering the stable movement of the gas–water interface under the starting pressure, we found that the gas well has a larger upper limit of production differential pressure, a smaller seepage velocity, and a lower upper limit of production allocation. Finally, we make a comparison between our model results and production characteristics of real gas wells and find a consistency between the model results with real data. Our model provides a theoretical framework for reasonable production allocation of gas wells in deep tight gas reservoirs with the edge water.

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Section: Introductionmentioning

confidence: 99%

Reasonable production allocation model of gas wells for deep tight gas reservoirs with the edge water

et al. 2023

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“…Currently, the methods of artificial intelligence and machine learning are widely used in all areas, including oil and gas engineering, and their application makes it possible to modernize existing technologies and processes [36][37][38]. Machine learning methods can be used to indirectly assess and predict the energy state of deposits [39][40].…”

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Reproduction of reservoir pressure by machine learning methods and study of its influence on the cracks formation process in hydraulic fracturing

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Zakharov

Martyushev

et al. 2022

PMI

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Hydraulic fracturing is an effective way to stimulate oil production, which is currently widely used in various conditions, including complex carbonate reservoirs. In the conditions of the considered field, hydraulic fracturing leads to a significant differentiation of technological efficiency indicators, which makes it expedient to study in detail the crack formation patterns. For all affected wells, the assessment of the resulting fractures spatial orientation was performed using the developed indirect technique, the reliability of which was confirmed by geophysical methods. In the course of the analysis, it was found that in all cases the fracture is oriented in the direction of the development system element area, which is characterized by the maximum reservoir pressure. At the same time, reservoir pressure values for all wells were determined at one point in time (at the beginning of hydraulic fracturing) using machine learning methods. The reliability of the used machine learning methods is confirmed by high convergence with the actual (historical) reservoir pressures obtained during hydrodynamic studies of wells. The obtained conclusion about the influence of the formation pressure on the patterns of fracturing should be taken into account when planning hydraulic fracturing in the considered conditions.

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Automated intelligent assistant in the selection of well placement when developing hard-to-recover reserves

Cited by 2 publications

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Reasonable production allocation model of gas wells for deep tight gas reservoirs with the edge water

Reasonable production allocation model of gas wells for deep tight gas reservoirs with the edge water

Reproduction of reservoir pressure by machine learning methods and study of its influence on the cracks formation process in hydraulic fracturing

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