Pore-Scale Investigation of the Electrical Property and Saturation Exponent of Archie’s Law in Hydrate-Bearing Sediments

Characterizing the electrical property of hydrate-bearing sediments is essential for hydrate reservoir identification and saturation evaluation. As the major contributor to electrical conductivity, pore water is a key factor in characterizing the electrical properties of hydrate-bearing sediments. The objective of this study is to clarify the effect of hydrates on pore water and the relationship between pore water characteristics and the saturation exponent of Archie’s law in hydrate-bearing sediments. A combination of X-ray computed tomography and resistivity measurement technology is used to derive the three-dimensional spatial structure and resistivity of hydrate-bearing sediments simultaneously, which is helpful to characterize pore water and investigate the saturation exponent of Archie’s law at the micro-scale. The results show that the resistivity of hydrate-bearing sediments is controlled by changes in pore water distribution and connectivity caused by hydrate formation. With the increase of hydrate saturation, pore water connectivity decreases, but the average coordination number and tortuosity increase due to much smaller and more tortuous throats of pore water divided by hydrate particles. It is also found that the saturation exponent of Archie’s law is controlled by the distribution and connectivity of pore water. As the parameters of connected pore water (e.g., porosity, water saturation) decrease, the saturation exponent decreases. At a low hydrate-saturation stage, the saturation exponent of Archie’s law changes obviously due to the complicated pore structure of hydrate-bearing sediments. A new logarithmic relationship between the saturation exponent of Archie’s law and the tortuosity of pore water is proposed which helps to calculate field hydrate saturation using resistivity logging data.

Improved water saturation estimation in shaly sandstone through variable cementation factor

Estimation of water saturation, Sw, in shaly sandstone is an intricate process. The surface conduction of clay minerals adds up to the electrolyte conduction in the pore spaces, thus generating high formation conductivity that overshadows the hydrocarbon effect. In each resistivity-based water saturation model, the key parameter is formation factor, F, which is typically derived from Archie’s Law. Referring to a log–log plot between formation factor and porosity, cementation factor reflects the slope of the straight line abiding Archie’s Law. In the case of shaly sandstone, derivation based on Archie’s Law in combination with Waxman–Smits equation leads to higher cementation factor, m*. In the shaly parts of the reservoir, high m* is counterbalanced by clay conductivity. Nonetheless, high m* used in clean parts increases Sw estimation. In this study, the variable cementation factor equation is introduced into the standard correlation of Sw versus Resistivity Index, RI, to develop a water saturation model with shaly sandstone parameters. Data retrieved from two fields that yielded mean arctangent absolute percentage error (MAAPE) were analysed to determine the difference between calculated and measured data within the 0.01–0.15 range for variable cementation factor method. The conventional method yielded maximum MAAPE at 0.46.

Electrical Resistivity Tomography (ERT) Monitoring for Landslides: Case Study in the Lantai Area, Yilan Taiping Mountain, Northeast Taiwan

Water saturation in the bedrock or colluvium is highly related to most landslide hazards, and rainfall is likely a crucial factor. The dynamic processes of onsite rock/soil mechanics could be revealed via monitoring using the electrical resistivity tomography (ERT) technique and Archie’s law. This study aims to investigate water saturation changes over time using time-lapse ERT images, providing a powerful method for monitoring landslide events. A fully automatic remote resistivity monitoring system was deployed to acquire hourly electrical resistivity data using a nontraditional hybrid array in the Lantai area of Yilan Taiping Mountain in Northeast Taiwan from 2019 to 2021. Six subzones in borehole ERT images were examined for the temporal and spatial resistivity variations, as well as possible pathways of the groundwater. Two representative cases of inverted electrical resistivity images varying with precipitation may be correlated with water saturation changes in the studied hillslope, implying the process of rainfall infiltration. Layers with decreased and increased electrical resistivity are also observed before sliding events. Accordingly, we suggest that high-frequency time-lapse ERT monitoring could play a crucial role in landslide early warning.

Oil Content Prediction Method Based on the TOC and Porosity of Organic-Rich Shales from Wireline Logs: A Case Study of Lacustrine Intersalt Shale Plays in Qianjiang Sag, Jianghan Basin, China

Organic-rich shales in between salt rock layers distribute widely in Qianjiang Sag, Jianghan Basin, central China. Due to the complexity of matrix mineral components and their distribution and tight pore structure, Archie’s law cannot be used directly to calculate oil saturation in those shale oil reservoirs. A new oil content model for shale oil reservoirs was introduced. By analyzing the logging and core experimental data from Qianjiang Sag, Jianghan Oilfield, we built the relationship between kerogen and the different well logging porosities including nuclear magnetic resonance (NMR) porosity, neutron porosity, and density porosity. And we used the dual- V sh method to calculate the total organic carbon (TOC). After calculating the volume fraction of the solid organic matters and separating it from the TOC, we acquired the hydrocarbon fluid content in the formations. The calculated oil content results are coherent with the core experimental data, which indicates the efficiency of this model. This model is simple and can be quickly applied. However, this method also shows its weakness in calculation precision when the TOC is not calculated precisely or the quality of the porosity logs is low.

Simulation of Time-Lapse Resistivity Method on Sandbox Model to Determine Fluid Changes and Desaturation

Time-lapse resistivity method is an implementation of the resistivity method that is executed exactly at the same spot but with various in time. In this study, the technique uses to identify the dynamics of groundwater fluids. The application of the time-lapse resistivity method was carried out by performing a sandbox model simulation that contains layers of rocks with a fault structure. The rock layers consist of tuff, fine sandstone, shale, coarse sandstone, gravel that represents confined and unconfined aquifers. The simulation was achieved by applying the Electrical Resistivity Tomography (ERT) dipole-dipole configuration at the same place, and measurements with 3 different conditions, namely dry, wet conditions filled with 2.5% water and wet conditions filled with 5% water. Data acquisition uses Naniura resistivity meters with a track length of 96 cm. The first measurement results (dry conditions) obtained a range of resistivity values ​​from 3.7 to 168.1 Ω.m, the second measurement (wet conditions filled 2.5% water) obtained the range of resistivity values ​​from 3.3 to 110.8 Ω.m and the third measurement (wet conditions) filled with 5% water the resistivity values ​​range from 1.7 to 91.2 Ω.m. Following the results of time-lapse inversion processing, a larger percentage change in the amount of 5.6% due to water absorption by the surface which then migrates into the inner layer. Whereas the percentage of desaturation ranges is from -3.11 to 0.217 %, refer to Archie’s Law assumes conduction is caused by water content.

Modeling of electro-conductive properties of woven structure based on mixing model

The main purpose of the research is to model the electro-conductive properties of the woven structure based on the mixing model. The generalized Archie's law for analysis of the woven structure and its conductivity was chosen. Electro-conductive components of woven structure i.e. strips and contacts of strips were treated as conducting phases in the structure. Based on generalized Archie’s law, the connectivity as being a measure of how the components of the whole structure are arranged, and the connectedness of a given phase as being a measure of the availability of pathways for conduction through that phase was determined for all structures. It was found that the connectivity of the strips phase is higher than the connectivity of the contacts of strips phase. It means that the strips phase (in terms of their quantity) has a greater effect on the conductivity of the woven structure than the contacts of strips phase. A decrease of the connectedness of strips and contacts of strips phases (in terms of their quality) can be obtained by adding another component to the woven structure which will reduce the conductivity of the structure.

Oil content prediction of lacustrine organic-rich shale from wireline logs: A case study of intersalt reservoirs in the Qianjiang Sag, Jianghan Basin, China

Shale oil is an unconventional oil resource with great potential. Oil saturation ([Formula: see text]) is an essential parameter for formation evaluation. However, due to the complexity of matrix mineral components and pore structure, Archie’s law cannot be used directly to calculate [Formula: see text] in shale oil reservoirs. We have developed a new saturation model for shale oil reservoirs. This model allows us to separate the organic from the inorganic pores, eliminate the background conductivity mainly caused by the borehole fluid or conductive minerals and determine the effective conductive porosity, which rules out nonconductive porosity, including isolated pores and the pore space affected by the fluid distribution. By analyzing the logging and core experimental data from the Qianjiang Sag, Jianghan Oilfield, we found that the T2 cutoff porosities of nuclear magnetic resonance logging are strongly related to the nonconductive porosities. After we determine the T2 cutoff value using the core experimental data, we can use it to obtain nonconductive porosity fraction in each depth point, which allows us to efficiently calculate [Formula: see text]. We calculate oil saturation values and use them to estimate the oil content. The results are coherent with the core experimental data, which indicates the efficiency of this model.