The Influence of Lamina Density and Occurrence on the Permeability of Lamellar Shale after Hydration

Zhao, Pengfei; Wang, Xingxing; Fan, Xiangyu; Zeng, Feitao; Zhang, Mingming; Meng, Fan; Nie, Wen

doi:10.3390/cryst11121524

Cited by 3 publications

(2 citation statements)

References 41 publications

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“…For low-permeability sandstone reservoir, laminar density, attitude of rocks, and degree of hydration have significant effects on permeability anisotropy [22], and there should be some logging curves that are influenced by the median grain size; that is, there is a certain relationship with the change of median grain size. It is already difficult for log interpretation analysts to find these relationships through principal component analysis with the help of mathematical tools.…”

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confidence: 99%

“…22 Geofluids involving state secrets, the group of companies and research institute of business and technical secrets, file information, such as the exploration and development research, project during dynamic and important research results, and other undisclosed technical data and technical information, ready to declare the patent and the research achievement of formation of the technical secret, confidentiality, and nondisclosure obligations.…”

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Quantitative Prediction of Low-Permeability Sandstone Grain Size Based on Conventional Logging Data by Deep Neural Network-Based BP Algorithm

et al. 2022

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The median grain size of rock is the main basis for the identification of sedimentary facies, and the variation of the median grain size of rock can be used to obtain the stratum sedimentary rhythm and thus to classify the flow unit. Therefore, the median grain size of rock is an extremely important parameter for reservoir evaluation. However, there is no petrophysical method that can directly evaluate the median grain size of rock in the logging data. The predecessors used natural gamma logging data to calculate the median rock grain size (Md) based on linear and statistical analysis for medium-high porosity and permeability sandstone reservoirs work. However, for low-permeability sandstone reservoirs, the error in the fitted median grain size of rock using linear multiple regression methods is too large for the calculated results to be applied. Therefore, the calculation of the median grain size of low-permeability sandstone reservoirs is a difficult problem to be solved. In this paper, the sensitivity logging parameters of median rock grain size are optimized for low permeability sandstone reservoirs using principal component analysis obtained the grain size direction correlation curves (DEN, CNL,GR, and RD) in the study area, and the corresponding loss and activation functions are selected based on the learning characteristics of the nonlinear mapping of the logging data and the BP neural network to ensure that overfitting occurs. The best model was obtained by using decision tree, support vector machine, shallow and deep neural networks to model the median rock grain size and predict neighboring wells, and a comparative analysis showed that for the problem of predicting the median rock grain size in low-permeability sandstone reservoirs, the deep neural network improved significantly over the shallow one and was much stronger than other machine learning methods. The best model obtained a coefficient of determination ( R 2 ) of 0.9831. Machine learning of median grain size from conventional logging data was systematically carried out through conventional logging sensitivity curve optimization, algorithm modeling, network parameter optimization, median grain size prediction, and validation, and the relative error in its quantitative prediction met application requirements. This method takes into account the nonlinear mapping relationship between the logging data and the fitting of small sample data and provides a systematic way of thinking for the logging curve to predict the grain size of low-permeability sandstone.

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Section: Introductionmentioning

confidence: 99%

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Quantitative Prediction of Low-Permeability Sandstone Grain Size Based on Conventional Logging Data by Deep Neural Network-Based BP Algorithm

et al. 2022

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Influence of Overburden Pressure on Imbibition Behavior in Tight Sandstones Using Nuclear Magnetic Resonance Technique

Meng

Yuan

et al. 2023

Journal of Energy Resources Technology

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Imbibition under overburden pressure can simulate the imbibition behavior in reservoir conditions during hydraulic fracturing, about which the mechanism is still unclear. This study investigated the imbibition with overburden pressure using a nuclear magnetic resonance displacement design. The main contribution of this study is that the initial imbibition rate under confining pressure can reflect the pore connectivity of reservoirs under overburden pressure and a method for appraising the pore connectivity under confining pressure was established. The tight sandstone samples were collected from the Upper Paleozoic Taiyuan and Shihezi Formations in Ordos Basin. The Taiyuan Formation presents apparent double-peak structure from nuclear magnetic resonance (NMR) spectra, and liquid fills into small pore preferentially as a whole. When the imbibition time is on a square root scale, the cumulative imbibition height at the initial imbibition period is not stable, which deviates from the linear principle, and the initial imbibition rate ranges from 0.077 to 0.1145. The Shihezi Formation shows a dominant peak structure from NMR spectra, and the liquid has no obvious filling order as a whole. When the imbibition time is on a square root scale, the cumulative imbibition height at the initial imbibition period also deviates from the linear principle, and the initial imbibition rate ranges from 0.0641 to 0.1619.

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Time-Sensitive Characteristics of Bedding Shale Deterioration under the Action of Drilling Fluid

Wang

Ling

et al. 2022

Lithosphere

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Fractures continuously extend and expand along bedding shale formations under the action of drilling fluid and eventually form a complex fracture network, which greatly reduces the strength of the rock. To determine the effect of the drilling fluid action time on the physical and mechanical properties of shale, nuclear magnetic resonance tests are carried out on shale soaked in oil-based drilling fluid for different soaking times. The fluid absorption rate of shale takes the form of a power function. The equations relating the shale mass, porosity, and permeability to soaking time are established. Then, in a sonic time-difference test, the change in the dynamic elastic parameters with the immersion time are observed. According to a triaxial strength test, the failure form and the deterioration degrees of the layered shale in different loading directions with immersion time are analyzed. Numerical simulation of the deterioration degree of shale under different water content conditions is carried out. When the water content increases, the collapse density of the surrounding rock of the well wall increases significantly. Finally, considering the anisotropy of bedding shale, the inclination angle, azimuth angle, and drilling fluid immersion time are substituted into the rock mechanics parameter deterioration model, the three pressure profiles of the formation are corrected, and the safe drilling fluid density window of the target interval is given.

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The Influence of Lamina Density and Occurrence on the Permeability of Lamellar Shale after Hydration

Cited by 3 publications

References 41 publications

Quantitative Prediction of Low-Permeability Sandstone Grain Size Based on Conventional Logging Data by Deep Neural Network-Based BP Algorithm

Quantitative Prediction of Low-Permeability Sandstone Grain Size Based on Conventional Logging Data by Deep Neural Network-Based BP Algorithm

Influence of Overburden Pressure on Imbibition Behavior in Tight Sandstones Using Nuclear Magnetic Resonance Technique

Time-Sensitive Characteristics of Bedding Shale Deterioration under the Action of Drilling Fluid

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