Simultaneous prediction of rock matrix modulus and critical porosity

2019 ◽  
Vol 16 (1) ◽  
pp. 14-24
Author(s):  
Nuo Li ◽  
Hao Chen ◽  
Xiu-Mei Zhang ◽  
Jian-Qiang Han ◽  
Jian Wang ◽  
...  
2011 ◽  
Vol 8 (3) ◽  
pp. 155-162 ◽  
Author(s):  
Xi-Lei He ◽  
Zhen-Hua He ◽  
Rui-Liang Wang ◽  
Xu-Ben Wang ◽  
Lian Jiang

2020 ◽  
Vol 17 (5) ◽  
pp. 1237-1258
Author(s):  
Kun Li ◽  
Xing-Yao Yin ◽  
Zhao-Yun Zong ◽  
Hai-Kun Lin

Abstract Seismic amplitude variation with offset (AVO) inversion is an important approach for quantitative prediction of rock elasticity, lithology and fluid properties. With Biot–Gassmann’s poroelasticity, an improved statistical AVO inversion approach is proposed. To distinguish the influence of rock porosity and pore fluid modulus on AVO reflection coefficients, the AVO equation of reflection coefficients parameterized by porosity, rock-matrix moduli, density and fluid modulus is initially derived from Gassmann equation and critical porosity model. From the analysis of the influences of model parameters on the proposed AVO equation, rock porosity has the greatest influences, followed by rock-matrix moduli and density, and fluid modulus has the least influences among these model parameters. Furthermore, a statistical AVO stepwise inversion method is implemented to the simultaneous estimation of rock porosity, rock-matrix modulus, density and fluid modulus. Besides, the Laplace probability model and differential evolution, Markov chain Monte Carlo algorithm is utilized for the stochastic simulation within Bayesian framework. Models and field data examples demonstrate that the simultaneous optimizations of multiple Markov chains can achieve the efficient simulation of the posterior probability density distribution of model parameters, which is helpful for the uncertainty analysis of the inversion and sets a theoretical fundament for reservoir characterization and fluid discrimination.


2019 ◽  
Vol 7 (1) ◽  
pp. T241-T253
Author(s):  
Siqi Wang ◽  
Jianguo Zhang ◽  
Shuai Yin ◽  
Chao Han

Accurate prediction of the S-wave velocity of highly heterogeneous coal measure strata using high-resolution logging can effectively identify high-quality reservoirs. We have used multipole array sonic logs to predict the S-wave velocity of coal measure strata based on the conventional empirical method (CEM), multiple regression method (MRM), and rock-matrix modulus extraction (MME) method. Moreover, we used a complex multiple parameter iterative computational method of forward calculation and inversion in the MME method. Our results indicate that the MME method can effectively extract several rock modulus parameters. There are good binomial relationships between the extracted rock modulus parameters ([Formula: see text]) and between the extracted modulus parameters and the P-wave impedance ([Formula: see text]). The average relative errors of the S-wave velocities predicted by the CEM, MRM, and MME methods are 7.58%, 5.64%, and 2.31%, respectively. The MME method can effectively extract and couple effective information from different types of conventional well logs and perform high-precision S-wave time difference prediction.


Author(s):  
Michel Fialin ◽  
Guy Rémond

Oxygen-bearing minerals are generally strong insulators (e.g. silicates), or if not (e.g. transition metal oxides), they are included within a rock matrix which electrically isolates them from the sample holder contacts. In this respect, a thin carbon layer (150 Å in our laboratory) is evaporated on the sections in order to restore the conductivity. For silicates, overestimated oxygen concentrations are usually noted when transition metal oxides are used as standards. These trends corroborate the results of Bastin and Heijligers on MgO, Al2O3 and SiO2. According to our experiments, these errors are independent of the accelerating voltage used (fig.l).Owing to the low density of preexisting defects within the Al2O3 single-crystal, no significant charge buildup occurs under irradiation at low accelerating voltage (< 10keV). As a consequence, neither beam instabilities, due to electrical discharges within the excited volume, nor losses of energy for beam electrons before striking the sample, due to the presence of the electrostatic charge-induced potential, are noted : measurements from both coated and uncoated samples give comparable results which demonstrates that the carbon coating is not the cause of the observed errors.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Da Un Jeong ◽  
Ki Moo Lim

AbstractThe pulse arrival time (PAT), the difference between the R-peak time of electrocardiogram (ECG) signal and the systolic peak of photoplethysmography (PPG) signal, is an indicator that enables noninvasive and continuous blood pressure estimation. However, it is difficult to accurately measure PAT from ECG and PPG signals because they have inconsistent shapes owing to patient-specific physical characteristics, pathological conditions, and movements. Accordingly, complex preprocessing is required to estimate blood pressure based on PAT. In this paper, as an alternative solution, we propose a noninvasive continuous algorithm using the difference between ECG and PPG as a new feature that can include PAT information. The proposed algorithm is a deep CNN–LSTM-based multitasking machine learning model that outputs simultaneous prediction results of systolic (SBP) and diastolic blood pressures (DBP). We used a total of 48 patients on the PhysioNet website by splitting them into 38 patients for training and 10 patients for testing. The prediction accuracies of SBP and DBP were 0.0 ± 1.6 mmHg and 0.2 ± 1.3 mmHg, respectively. Even though the proposed model was assessed with only 10 patients, this result was satisfied with three guidelines, which are the BHS, AAMI, and IEEE standards for blood pressure measurement devices.


Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1998
Author(s):  
Haishan Luo ◽  
Kishore K. Mohanty

Unlocking oil from tight reservoirs remains a challenging task, as the existence of fractures and oil-wet rock surfaces tends to make the recovery uneconomic. Injecting a gas in the form of a foam is considered a feasible technique in such reservoirs for providing conformance control and reducing gas-oil interfacial tension (IFT) that allows the injected fluids to enter the rock matrix. This paper presents a modeling strategy that aims to understand the behavior of near-miscible foam injection and to find the optimal strategy to oil recovery depending on the reservoir pressure and gas availability. Corefloods with foam injection following gas injection into a fractured rock were simulated and history matched using a compositional commercial simulator. The simulation results agreed with the experimental data with respect to both oil recovery and pressure gradient during both injection schedules. Additional simulations were carried out by increasing the foam strength and changing the injected gas composition. It was found that increasing foam strength or the proportion of ethane could boost oil production rate significantly. When injected gas gets miscible or near miscible, the foam model would face serious challenges, as gas and oil phases could not be distinguished by the simulator, while they have essentially different effects on the presence and strength of foam in terms of modeling. We provide in-depth thoughts and discussions on potential ways to improve current foam models to account for miscible and near-miscible conditions.


2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Daniel Bohnsack ◽  
Martin Potten ◽  
Simon Freitag ◽  
Florian Einsiedl ◽  
Kai Zosseder

AbstractIn geothermal reservoir systems, changes in pore pressure due to production (depletion), injection or temperature changes result in a displacement of the effective stresses acting on the rock matrix of the aquifer. To compensate for these intrinsic stress changes, the rock matrix is subjected to poroelastic deformation through changes in rock and pore volume. This in turn may induce changes in the effective pore network and thus in the hydraulic properties of the aquifer. Therefore, for the conception of precise reservoir models and for long-term simulations, stress sensitivity of porosity and permeability is required for parametrization. Stress sensitivity was measured in hydrostatic compression tests on 14 samples of rock cores stemming from two boreholes of the Upper Jurassic Malm aquifer of the Bavarian Molasse Basin. To account for the heterogeneity of this carbonate sequence, typical rock and facies types representing the productive zones within the thermal reservoir were used. Prior to hydrostatic investigations, the hydraulic (effective porosity, permeability) and geomechanical (rock strength, dynamic, and static moduli) parameters as well as the microstructure (pore and pore throat size) of each rock sample were studied for thorough sample characterization. Subsequently, the samples were tested in a triaxial test setup with effective stresses of up to 28 MPa (hydrostatic) to simulate in-situ stress conditions for depths up to 2000 m. It was shown that stress sensitivity of the porosity was comparably low, resulting in a relative reduction of 0.7–2.1% at maximum effective stress. In contrast, relative permeability losses were observed in the range of 17.3–56.7% compared to the initial permeability at low effective stresses. Stress sensitivity coefficients for porosity and permeability were derived for characterization of each sample and the different rock types. For the stress sensitivity of porosity, a negative correlation with rock strength and a positive correlation with initial porosity was observed. The stress sensitivity of permeability is probably controlled by more complex processes than that of porosity, where the latter is mainly controlled by the compressibility of the pore space. It may depend more on the compaction of precedented flow paths and the geometry of pores and pore throats controlling the connectivity within the rock matrix. In general, limestone samples showed a higher stress sensitivity than dolomitic limestone or dolostones, because dolomitization of the rock matrix may lead to an increasing stiffness of the rock. Furthermore, the stress sensitivity is related to the history of burial diagenesis, during which changes in the pore network (dissolution, precipitation, and replacement of minerals and cements) as well as compaction and microcrack formation may occur. This study, in addition to improving the quality of input parameters for hydraulic–mechanical modeling, shows that hydraulic properties in flow zones largely characterized by less stiff, porous limestones can deteriorate significantly with increasing effective stress.


Geofluids ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Guanghui Jiang ◽  
Jianping Zuo ◽  
Teng Ma ◽  
Xu Wei

Understanding the change of permeability of rocks before and after heating is of great significance for exploitation of hydrocarbon resources and disposal of nuclear waste. The rock permeability under high temperature cannot be measured with most of the existing methods. In this paper, quality, wave velocity, and permeability of granite specimen from Maluanshan tunnel are measured after high temperature processing. Quality and wave velocity of granite decrease and permeability of granite increases with increasing temperature. Using porosity as the medium, a new wave velocity-permeability model is established with modified wave velocity-porosity formula and Kozeny-Carman formula. Under some given wave velocities and corresponding permeabilities through experiment, the permeabilities at different temperatures and wave velocities can be obtained. By comparing the experimental and the theoretical results, the proposed formulas are verified. In addition, a sensitivity analysis is performed to examine the effect of particle size, wave velocities in rock matrix, and pore fluid on permeability: permeability increases with increasing particle size, wave velocities in rock matrix, and pore fluid; the higher the rock wave velocity, the lower the effect of wave velocities in rock matrix and pore fluid on permeability.


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